http://2013.igem.org/wiki/index.php?title=Special:Contributions/Veerledewever&feed=atom&limit=50&target=Veerledewever&year=&month=2013.igem.org - User contributions [en]2024-03-29T12:33:42ZFrom 2013.igem.orgMediaWiki 1.16.5http://2013.igem.org/Team:KU_Leuven/SponsorsTeam:KU Leuven/Sponsors2013-11-13T17:51:19Z<p>Veerledewever: </p>
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<h3>Students & Advisors</h3><br />
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<p>They made the BanAphids.</p><br />
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<h3>Attributions</h3> </a><br />
<p>A lot of thank yous!</p><br />
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<p>Working with other teams.</p><br />
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<h3>Sponsors</h3> </a><br />
<p>You are here!</p><br />
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<h3 class="bg-grey">Our Generous Sponsors</h3><br />
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<p align = "justify">The KU Leuven 2013 iGEM team would like to thank all their generous sponsors. They gave us the chance to have an amazing summer!</p><br />
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<h3 class="bg-grey">KU Leuven iGEM 2013 Organiser</h3><br />
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<h3>BioSCENTer</h3> </a><br />
<p align="justify">The Leuven Centre for Bio-Science, Bio-Engineering and Bio-Technology (BioSCENTer) is one of the largest research centres of the Group Science, Engineering and Technology of KU Leuven. It emerged in 2008 when four different research clusters, bioinformatics and systems biology, virtual life, Biomolecular interaction (Biomint), and evolutionary biology (iCEB) decided to join their efforts. <b> Its aim is to integrate the available expertise on fundamental and applied Life Sciences; to stimulate cross-disciplinary fertilisation; to boost excellence in research, teaching and training; to create an inspiring environment for innovative ideas on scientific and societal challenges; and to advise decision makers in policy and strategic initiatives.</b></p><br />
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<h3 class="bg-grey">Platinum Sponsors</h3><br />
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<h3>KU Leuven</h3> </a><br />
<p align="justify">The KU Leuven is an independent university founded in 1425. It is a <b> research-intensive university </b> where both fundamental and applied sciences have their home. Over 300 study programmes are provided to more than <b> 40.000 students </b>. For iGEM both the group of <a = href=http://set.kuleuven.be/English target="blank"> Science, Engineering and Technology</a> as the <a = href="http://gbiomed.kuleuven.be/english target="blank"> biomedical sciences group</a> were willing to sponsor.</p><br />
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<h3>KU Leuven R&D</h3> </a><br />
<p align="justify">KU Leuven Research & Development (LRD) was established in 1972 as one of the <b> first technology transfer offices in Europe</b>. Over the last 40 years, LRD has developed a tradition of collaborating with industry, securing and licensing intellectual property rights, and creating spin-off companies. LRD is dedicated to <b> building bridges between science and industry</b>, and to transferring knowledge and technologies to the marketplace.</p><br />
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<h3>Eurogentec</h3> </a><br />
<p align="justify">Eurogentec was founded in 1985 as a spin-off from the University of Liège. Eurogentec is a leading <b> worldwide provider of reliable and innovative products and services</b> to scientists in Life Science Research, Molecular Diagnostic and Therapeutic development and commercialisation.</p><br />
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<h3>iMinds</h3> </a><br />
<p align="justify">iMinds is an independent research institute, founded by the Flemish government in 2004, to stimulate innovation in the field of ICT. <br />
The institute conducts both strategic and applied research, bringing together new technologies, societal challenges and questions from the business world in interdisciplinary research projects.<br />
iMinds Future Health Department is located at the KU Leuven and focuses on innovative ICT solutions in healthcare. Its expertise comprises among others <b> data analytics, bioinformatics, biomedical signal processing, advanced image computing, e-learing, serious gaming and user research</b>.</p><br />
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<h3>Ko-Lo Instruments</h3><br />
<p align="justify">Since 1994, Ko-Lo Instruments is a provider of reliable and qualitative <b> laboratory materials</b>. They offer pipette tips (including automatic), micro tubes, deepwell plates, aluminium cover foils, glass vials, tailor-made glassware (in corporation with the glassworks), ice buckets and so on.</p><br />
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<h3>Sopachem</h3> </a><br />
<p align="justify">Sopachem Life Sciences brings <b> innovation to the lab with high tech reagent kits and instruments</b>. Our line of Molecular Biology products is selected to improve the scientists workflow by eliminating costly steps and saving time. Discover where innovation can advance your research and improve the quality of your results. Take the first step towards Better Science and go to <a href="http://www.sopachem.com/">sopachem.com</a></p><br />
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<h3>Globachem</h3> </a><br />
<p align="justify">Globachem nv is a young, dynamic company, engaged in <b> international agrochemical business</b>.<br/><br />
We specialise in both generic agrochemicals (fungicides, herbicides, insecticides, etc.) and new biological products, with useful applications in agriculture and horticulture.</p><br />
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<h3>Biobest</h3> </a><br />
<p align="justify">Biobest NV is a leading authority in <b> biological pollination and pest control</b>. With more than 25 years of experience, Biobest is known to be a pioneer in this sector. Biobest produces and commercialises more than 30 kinds of beneficial insects and mites, which are used for biological crop protection. Biobest is especially known for its core business i.e. the production of bumblebees that are used worldwide for biological pollination.</p><br />
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<h3>Genzyme</h3> </a><br />
<p align="justify">Genzyme Belgium, a Sanofi company, is a loyal iGEM KU Leuven sponsor for which we are very grateful. They are a leading biotech company in the Benelux, engaged in the <b> manufacturing of biotherapeutics </b> such as monoclonal antibodies or enzymes for use in human enzyme replacement therapies.<br/><p><br />
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<h3>Macherey-Nagel</h3> </a><br />
<p align="justify">Macherey-Nagel, originated in Germany, offer since 1911 <b> superior DNA, RNA and protein extraction kits</b>. Kits are based on state-of-the art silica and anion-exchange technology, as well as magnetic beads and filtration approaches. <a href="http://www.filterservice.be/"> Filter Service</a> distributes these extraction and clean-up kits exclusively in Belgium as well as a great number of other consumables.</p><br />
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<h3>Fablab</h3> </a><br />
<p align="justify">Fablab Leuven is an <b> open source hardware for everyone</b>: students, the social economy and the broad public. Fablab Leuven compromises different user-friendly machines that can make almost everything out of wood and plastic. Fablab provides their services for free, as long as you share your knowledge with others through their website.</p><br />
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<h3>Gimv</h3> </a><br />
<p align="justify">Gimv is a European investment company with over <b> three decades of experience in private equity and venture capital</b>. Gimv is listed on NYSE Euronext Brussels. Gimv currently manages around 1.8 billion EUR (including third party funds) of investments in 75 portfolio companies, which jointly realise a turnover of more than EUR 6 billion and employ over 26,000 professionals. As a recognised market leader in selected investment platforms, Gimv <b> identifies entrepreneurial and innovative companies with high-growth potential and supports them in their transformation into market leaders</b>. Gimv’s four investment platforms are: Consumer 2020, Health & Care, Smart Industries and Sustainable Cities. Each of these platforms works with a skilled and dedicated team across Gimv’s home markets of the Benelux, France and Germany and can count on an extended international network of experts.</p><br />
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<p align="justify">Eppendorf is a sparkling company that is specialised in the <b> development and manufacturing of high quality laboratory equipment and their distribution throughout the world</b>. The Eppendorf products mainly focus on use at academic and commercial research institutes as well as industrial companies within Life Science and Biotechnology.<br/><br />
The laboratory equipment of New Brunswick, which stands for expertise and innovation, fits excellently within the product portfolio of Eppendorf.</p><br />
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<p align="justify"> Elscolab NV is an important partner in the Benelux for providing laboratory and industrial tools. In a rapidly evolving and demanding market, Elscolab NV aims for a <b> continuous optimisation of their technically oriented customer service </b>. They focus on four areas: Industrial Process Monitoring; Environmental Water Monitoring; Laboratory Equipment & Consumables; Colour, Appearance & Design Tools. From these different groups, Elscolab provides their customers with advice for the optimal solution for their company. </p><br />
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<p align="justify">Founded in 2004, BIOKÉ is a young and dynamic company that specialises in <b> life sciences applications</b>. They provide innovative, high-performance applications for genomics, proteomics and molecular diagnostics. With their technologies and equipment, they help accelerate and improve your diagnostics and research activities by enabling reproducible results.</p><br />
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<p align="justify">Since 1961, the SARSTEDT Group <b> develops, manufactures and sells equipment and consumables </b> in the field of medicine and science worldwide. Their modern and integrated quality management systems allows their products to conform to a very high quality standard.</p><br />
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<p align="justify">ThromboGenics is dedicated to developing and commercialising <b> new pharmacologic treatments</b> that address important unmet clinical needs in ophthalmology and oncology. By achieving this goal ThromboGenics intends to assist clinicians around the world to continually improve treatment for patients with sight threatening ophthalmic disorders and cancer.</p><br />
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<p align="justify">AgroSavfe is a new start-up from VIB, established in January 2013 to develop more sustainable crop protection products based on its proprietary <b> Agrobody™ platform</b>. Agrobodies™ are derived from camelid antibodies and can be generated against virtually any target, to which they bind with high affinity and specificity. Agrobodies™ directed against crop produce, leaves, seeds, pests or particular structures thereof enable targeted delivery and retention of the active ingredient at or near its site of action. Thereby <b> Agrobodies™ allow to reduce dosage or application frequencies of crop protection products.</b></p><br />
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<p align="justify">Since 1997, Valesta has been <b> building bridges between medical organisations and passionate clinical research professionals</b>. Valesta is a dedicated HR partner for pharmaceutical firms as well as CROs (Contract Research Organisations) and all other companies that organise, coordinate and carry out clinical research projects. Their dedicated Account Executives thoroughly understand the industries they serve and offer resourcing solutions in specialised areas.</p><br />
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<p align="justify">Ablynx is a biopharmaceutical company engaged in the discovery and development of <b> Nanobodies®</b>, a novel class of therapeutic proteins based on single-domain antibody fragments, for a range of serious human diseases, including inflammation, haematology, oncology and pulmonary disease. Today, the Company has approximately 25 programmes in the pipeline and six Nanobodies at the clinical development stage. Ablynx has on-going research collaborations and significant partnerships with major pharmaceutical companies including Boehringer Ingelheim, Merck Serono, Novartis and Merck & Co. The Company is headquartered in Ghent, Belgium.</p><br />
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<p align="justify">Technopolis®, the Flemish Science Centre’s total exhibition area measures 6 791 m². It consists of a <b> permanent exhibition</b> inspired by broad themes such as air & wind, building blocks, action & reaction and space travel. Next to the permanent exhibition area, Technopolis has an area of 750 m² for annual themed exhibitions. From September 2013 onwards <i>“High Tech Romans”</i> is available to the public.<br />
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• Children’s Science Centre (900 m²) for children for kids aged 4 till 8.</br><br />
• Xplora (900 m²), an area where kids aged 8 till 14 are encouraged to start experimenting by means of interactive exhibitions based on original professions that appeal to their imagination such as tiger dentist, candy maker or private investigator (recently opened)</br><br />
• Inspirience (470 m²), an area where teenagers aged 14 till 18 are challenged to find creative solutions to open ended obstacles (recently opened).<br />
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<p align="justify">ERASynBio is an ERA-NET from the seventh framework program for the development and coordination of Synthetic Biology in the European Research Area. The central idea of ERASynBio is to promote the robust development of Synthetic Biology by structuring and coordinating national efforts and investment. ERASynBio will develop a strategic research agenda / white paper, which will support the emergence of national Synthetic Biology programmes and which will lay the ground for transnational funding activities via joint calls in the project. ERASynBio will stimulate and tackle the interdisciplinary nature and immaturity by offering training and educational possibilities, establishing an interdisciplinary advisory board and inviting observers of other funding organisations. Tight collaboration between academia and industry aiming to fertilize the innovation process will occur on several levels. To adhere to ethical, legal and societal aspects as well as to technical issues like standardisation and infrastructure development ERASynBio will trace and integrate the ongoing work and research on these framework conditions and integrate them in the strategic research.</p><br />
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<p align="justify">Since 1997, pcfruit npo is the <b> co-ordinating organisation of the research and advisory services of fruit growing in Belgium</b>. It is a co-operation between the Royal Research Station of Gorsem and the two experimental gardens, Experimental Garden for Small Fruits Tongeren, and Experimental Garden for Pome and Stone Fruits Velm. They are now all centralised at one location in Sint-Truiden. pcfruit wants to give additional value to fruit growing by carrying out applied scientific research and experimental research, by collecting information, transferring information, and services, taking into account the social developments.</p><br />
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<p align="justify">The Thermo Scientific™ Molecular Biology product portfolio represents <b> the most complete, state-of-the-art offering for molecular biology research</b>, spanning RNA interference and gene expression products, PCR/qPCR reagents, consumables and instruments, modifying and restriction enzymes, DNA ladders, nucleic acid purification kits, and other molecular biology tools. Today, the people behind the expanding Thermo Scientific research portfolio remain committed to supporting your research and making it even easier for you to do great science.</p><br />
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<p align="justify">Sigma-Aldrich is a leading Life Science and High Technology company whose <b> biochemical, organic chemical products, kits and services</b> are used in scientific research, including genomic and proteomic research, biotechnology, pharmaceutical development, the diagnosis of disease and as key components in pharmaceutical, diagnostics and high technology manufacturing. Sigma-Aldrich customers include more than 1.3 million scientists and technologists in life science companies, university and government institutions, hospitals and industry.</p><br />
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<p align="justify">Eos is a <b> Dutch magazine about science</b>, published in Belgium and the Netherlands. It exists since 1983. Eos has five editions: Eos-magazine, Eos: Psyche&Brein, Eos: Scientific American, Eos: Memo and Eos Weekblad. </p><br />
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<p align="justify">De Raaf is a <b> copy centre </b> situated in Leuven. It’s known for it's quality, speed and extremely friendly service. </p><br />
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<p align="justify">InBev Belgium is a part of Anheuser-Busch InBev, which is the <b> leading global brewer</b>. AB InBev manages a portfolio of well over 200 beer brands and holds the No. 1 or No. 2 market position in many of the world’s top beer markets. </p><br />
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</html></div>Veerledeweverhttp://2013.igem.org/File:Genzyme.jpgFile:Genzyme.jpg2013-11-13T17:41:31Z<p>Veerledewever: </p>
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<div></div>Veerledeweverhttp://2013.igem.org/File:Genzyme_A_SANOFI_CO_green.pdfFile:Genzyme A SANOFI CO green.pdf2013-11-13T17:40:22Z<p>Veerledewever: </p>
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<div></div>Veerledeweverhttp://2013.igem.org/File:Genzyme.tifFile:Genzyme.tif2013-11-13T17:38:24Z<p>Veerledewever: uploaded a new version of &quot;File:Genzyme.tif&quot;</p>
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<div></div>Veerledeweverhttp://2013.igem.org/File:Genzyme_green.pdfFile:Genzyme green.pdf2013-11-13T17:35:50Z<p>Veerledewever: </p>
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<div></div>Veerledeweverhttp://2013.igem.org/File:Genzyme.tifFile:Genzyme.tif2013-11-13T17:33:50Z<p>Veerledewever: </p>
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<div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/SponsorsTeam:KU Leuven/Sponsors2013-11-13T17:28:48Z<p>Veerledewever: </p>
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<p>They made the BanAphids.</p><br />
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<p>A lot of thank yous!</p><br />
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<p>Working with other teams.</p><br />
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<p>You are here!</p><br />
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<p align = "justify">The KU Leuven 2013 iGEM team would like to thank all their generous sponsors. They gave us the chance to have an amazing summer!</p><br />
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<p align="justify">The Leuven Centre for Bio-Science, Bio-Engineering and Bio-Technology (BioSCENTer) is one of the largest research centres of the Group Science, Engineering and Technology of KU Leuven. It emerged in 2008 when four different research clusters, bioinformatics and systems biology, virtual life, Biomolecular interaction (Biomint), and evolutionary biology (iCEB) decided to join their efforts. <b> Its aim is to integrate the available expertise on fundamental and applied Life Sciences; to stimulate cross-disciplinary fertilisation; to boost excellence in research, teaching and training; to create an inspiring environment for innovative ideas on scientific and societal challenges; and to advise decision makers in policy and strategic initiatives.</b></p><br />
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<p align="justify">The KU Leuven is an independent university founded in 1425. It is a <b> research-intensive university </b> where both fundamental and applied sciences have their home. Over 300 study programmes are provided to more than <b> 40.000 students </b>. For iGEM both the group of <a = href=http://set.kuleuven.be/English target="blank"> Science, Engineering and Technology</a> as the <a = href="http://gbiomed.kuleuven.be/english target="blank"> biomedical sciences group</a> were willing to sponsor.</p><br />
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<p align="justify">KU Leuven Research & Development (LRD) was established in 1972 as one of the <b> first technology transfer offices in Europe</b>. Over the last 40 years, LRD has developed a tradition of collaborating with industry, securing and licensing intellectual property rights, and creating spin-off companies. LRD is dedicated to <b> building bridges between science and industry</b>, and to transferring knowledge and technologies to the marketplace.</p><br />
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<p align="justify">Eurogentec was founded in 1985 as a spin-off from the University of Liège. Eurogentec is a leading <b> worldwide provider of reliable and innovative products and services</b> to scientists in Life Science Research, Molecular Diagnostic and Therapeutic development and commercialisation.</p><br />
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<p align="justify">iMinds is an independent research institute, founded by the Flemish government in 2004, to stimulate innovation in the field of ICT. <br />
The institute conducts both strategic and applied research, bringing together new technologies, societal challenges and questions from the business world in interdisciplinary research projects.<br />
iMinds Future Health Department is located at the KU Leuven and focuses on innovative ICT solutions in healthcare. Its expertise comprises among others <b> data analytics, bioinformatics, biomedical signal processing, advanced image computing, e-learing, serious gaming and user research</b>.</p><br />
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<p align="justify">Since 1994, Ko-Lo Instruments is a provider of reliable and qualitative <b> laboratory materials</b>. They offer pipette tips (including automatic), micro tubes, deepwell plates, aluminium cover foils, glass vials, tailor-made glassware (in corporation with the glassworks), ice buckets and so on.</p><br />
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<h3>Sopachem</h3> </a><br />
<p align="justify">Sopachem Life Sciences brings <b> innovation to the lab with high tech reagent kits and instruments</b>. Our line of Molecular Biology products is selected to improve the scientists workflow by eliminating costly steps and saving time. Discover where innovation can advance your research and improve the quality of your results. Take the first step towards Better Science and go to <a href="http://www.sopachem.com/">sopachem.com</a></p><br />
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<h3>Globachem</h3> </a><br />
<p align="justify">Globachem nv is a young, dynamic company, engaged in <b> international agrochemical business</b>.<br/><br />
We specialise in both generic agrochemicals (fungicides, herbicides, insecticides, etc.) and new biological products, with useful applications in agriculture and horticulture.</p><br />
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<h3>Biobest</h3> </a><br />
<p align="justify">Biobest NV is a leading authority in <b> biological pollination and pest control</b>. With more than 25 years of experience, Biobest is known to be a pioneer in this sector. Biobest produces and commercialises more than 30 kinds of beneficial insects and mites, which are used for biological crop protection. Biobest is especially known for its core business i.e. the production of bumblebees that are used worldwide for biological pollination.</p><br />
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<h3>Genzyme</h3> </a><br />
<p align="justify">Globachem nv is a young, dynamic company, engaged in <b> international agrochemical business</b>.<br/><br />
We specialise in both generic agrochemicals (fungicides, herbicides, insecticides, etc.) and new biological products, with useful applications in agriculture and horticulture.</p><br />
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<h3>Macherey-Nagel</h3> </a><br />
<p align="justify">Macherey-Nagel, originated in Germany, offer since 1911 <b> superior DNA, RNA and protein extraction kits</b>. Kits are based on state-of-the art silica and anion-exchange technology, as well as magnetic beads and filtration approaches. <a href="http://www.filterservice.be/"> Filter Service</a> distributes these extraction and clean-up kits exclusively in Belgium as well as a great number of other consumables.</p><br />
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<h3>Fablab</h3> </a><br />
<p align="justify">Fablab Leuven is an <b> open source hardware for everyone</b>: students, the social economy and the broad public. Fablab Leuven compromises different user-friendly machines that can make almost everything out of wood and plastic. Fablab provides their services for free, as long as you share your knowledge with others through their website.</p><br />
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<h3>Gimv</h3> </a><br />
<p align="justify">Gimv is a European investment company with over <b> three decades of experience in private equity and venture capital</b>. Gimv is listed on NYSE Euronext Brussels. Gimv currently manages around 1.8 billion EUR (including third party funds) of investments in 75 portfolio companies, which jointly realise a turnover of more than EUR 6 billion and employ over 26,000 professionals. As a recognised market leader in selected investment platforms, Gimv <b> identifies entrepreneurial and innovative companies with high-growth potential and supports them in their transformation into market leaders</b>. Gimv’s four investment platforms are: Consumer 2020, Health & Care, Smart Industries and Sustainable Cities. Each of these platforms works with a skilled and dedicated team across Gimv’s home markets of the Benelux, France and Germany and can count on an extended international network of experts.</p><br />
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<h3>eppendorf</h3> </a><br />
<p align="justify">Eppendorf is a sparkling company that is specialised in the <b> development and manufacturing of high quality laboratory equipment and their distribution throughout the world</b>. The Eppendorf products mainly focus on use at academic and commercial research institutes as well as industrial companies within Life Science and Biotechnology.<br/><br />
The laboratory equipment of New Brunswick, which stands for expertise and innovation, fits excellently within the product portfolio of Eppendorf.</p><br />
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<p align="justify"> Elscolab NV is an important partner in the Benelux for providing laboratory and industrial tools. In a rapidly evolving and demanding market, Elscolab NV aims for a <b> continuous optimisation of their technically oriented customer service </b>. They focus on four areas: Industrial Process Monitoring; Environmental Water Monitoring; Laboratory Equipment & Consumables; Colour, Appearance & Design Tools. From these different groups, Elscolab provides their customers with advice for the optimal solution for their company. </p><br />
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<h3>Bioké</h3> </a><br />
<p align="justify">Founded in 2004, BIOKÉ is a young and dynamic company that specialises in <b> life sciences applications</b>. They provide innovative, high-performance applications for genomics, proteomics and molecular diagnostics. With their technologies and equipment, they help accelerate and improve your diagnostics and research activities by enabling reproducible results.</p><br />
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<h3>Sarstedt</h3> </a><br />
<p align="justify">Since 1961, the SARSTEDT Group <b> develops, manufactures and sells equipment and consumables </b> in the field of medicine and science worldwide. Their modern and integrated quality management systems allows their products to conform to a very high quality standard.</p><br />
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<h3>ThromboGenics</h3> </a><br />
<p align="justify">ThromboGenics is dedicated to developing and commercialising <b> new pharmacologic treatments</b> that address important unmet clinical needs in ophthalmology and oncology. By achieving this goal ThromboGenics intends to assist clinicians around the world to continually improve treatment for patients with sight threatening ophthalmic disorders and cancer.</p><br />
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<p align="justify">AgroSavfe is a new start-up from VIB, established in January 2013 to develop more sustainable crop protection products based on its proprietary <b> Agrobody™ platform</b>. Agrobodies™ are derived from camelid antibodies and can be generated against virtually any target, to which they bind with high affinity and specificity. Agrobodies™ directed against crop produce, leaves, seeds, pests or particular structures thereof enable targeted delivery and retention of the active ingredient at or near its site of action. Thereby <b> Agrobodies™ allow to reduce dosage or application frequencies of crop protection products.</b></p><br />
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<h3>Valesta</h3> </a><br />
<p align="justify">Since 1997, Valesta has been <b> building bridges between medical organisations and passionate clinical research professionals</b>. Valesta is a dedicated HR partner for pharmaceutical firms as well as CROs (Contract Research Organisations) and all other companies that organise, coordinate and carry out clinical research projects. Their dedicated Account Executives thoroughly understand the industries they serve and offer resourcing solutions in specialised areas.</p><br />
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<h3>Ablynx</h3> </a><br />
<p align="justify">Ablynx is a biopharmaceutical company engaged in the discovery and development of <b> Nanobodies®</b>, a novel class of therapeutic proteins based on single-domain antibody fragments, for a range of serious human diseases, including inflammation, haematology, oncology and pulmonary disease. Today, the Company has approximately 25 programmes in the pipeline and six Nanobodies at the clinical development stage. Ablynx has on-going research collaborations and significant partnerships with major pharmaceutical companies including Boehringer Ingelheim, Merck Serono, Novartis and Merck & Co. The Company is headquartered in Ghent, Belgium.</p><br />
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<p align="justify">Technopolis®, the Flemish Science Centre’s total exhibition area measures 6 791 m². It consists of a <b> permanent exhibition</b> inspired by broad themes such as air & wind, building blocks, action & reaction and space travel. Next to the permanent exhibition area, Technopolis has an area of 750 m² for annual themed exhibitions. From September 2013 onwards <i>“High Tech Romans”</i> is available to the public.<br />
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In addition to the permanent and temporary exhibitions, we also have a general exhibition areas for specific target groups:</br><br />
• Children’s Science Centre (900 m²) for children for kids aged 4 till 8.</br><br />
• Xplora (900 m²), an area where kids aged 8 till 14 are encouraged to start experimenting by means of interactive exhibitions based on original professions that appeal to their imagination such as tiger dentist, candy maker or private investigator (recently opened)</br><br />
• Inspirience (470 m²), an area where teenagers aged 14 till 18 are challenged to find creative solutions to open ended obstacles (recently opened).<br />
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<p align="justify">ERASynBio is an ERA-NET from the seventh framework program for the development and coordination of Synthetic Biology in the European Research Area. The central idea of ERASynBio is to promote the robust development of Synthetic Biology by structuring and coordinating national efforts and investment. ERASynBio will develop a strategic research agenda / white paper, which will support the emergence of national Synthetic Biology programmes and which will lay the ground for transnational funding activities via joint calls in the project. ERASynBio will stimulate and tackle the interdisciplinary nature and immaturity by offering training and educational possibilities, establishing an interdisciplinary advisory board and inviting observers of other funding organisations. Tight collaboration between academia and industry aiming to fertilize the innovation process will occur on several levels. To adhere to ethical, legal and societal aspects as well as to technical issues like standardisation and infrastructure development ERASynBio will trace and integrate the ongoing work and research on these framework conditions and integrate them in the strategic research.</p><br />
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<p align="justify">Since 1997, pcfruit npo is the <b> co-ordinating organisation of the research and advisory services of fruit growing in Belgium</b>. It is a co-operation between the Royal Research Station of Gorsem and the two experimental gardens, Experimental Garden for Small Fruits Tongeren, and Experimental Garden for Pome and Stone Fruits Velm. They are now all centralised at one location in Sint-Truiden. pcfruit wants to give additional value to fruit growing by carrying out applied scientific research and experimental research, by collecting information, transferring information, and services, taking into account the social developments.</p><br />
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<p align="justify">The Thermo Scientific™ Molecular Biology product portfolio represents <b> the most complete, state-of-the-art offering for molecular biology research</b>, spanning RNA interference and gene expression products, PCR/qPCR reagents, consumables and instruments, modifying and restriction enzymes, DNA ladders, nucleic acid purification kits, and other molecular biology tools. Today, the people behind the expanding Thermo Scientific research portfolio remain committed to supporting your research and making it even easier for you to do great science.</p><br />
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<h3>Sigma-Aldrich</h3> </a><br />
<p align="justify">Sigma-Aldrich is a leading Life Science and High Technology company whose <b> biochemical, organic chemical products, kits and services</b> are used in scientific research, including genomic and proteomic research, biotechnology, pharmaceutical development, the diagnosis of disease and as key components in pharmaceutical, diagnostics and high technology manufacturing. Sigma-Aldrich customers include more than 1.3 million scientists and technologists in life science companies, university and government institutions, hospitals and industry.</p><br />
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<p align="justify">Eos is a <b> Dutch magazine about science</b>, published in Belgium and the Netherlands. It exists since 1983. Eos has five editions: Eos-magazine, Eos: Psyche&Brein, Eos: Scientific American, Eos: Memo and Eos Weekblad. </p><br />
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<p align="justify">De Raaf is a <b> copy centre </b> situated in Leuven. It’s known for it's quality, speed and extremely friendly service. </p><br />
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<p align="justify">InBev Belgium is a part of Anheuser-Busch InBev, which is the <b> leading global brewer</b>. AB InBev manages a portfolio of well over 200 beer brands and holds the No. 1 or No. 2 market position in many of the world’s top beer markets. </p><br />
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</html></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Team/StudentsTeam:KU Leuven/Team/Students2013-11-10T08:26:00Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>Meet our most generous sponsors!</p><br />
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<p align="justify">16 enthusiastic <a href="http://www.kuleuven.be/english"> KU Leuven </a> students who don’t mind working the whole summer join hands to kick some ass, iGEM style. It’s a very diverse team consisting of students with different backgrounds. There are 10 guys and 6 girls present with studies in biochemistry, (bio-)engineering, medicine, philosophy and others. The team is supported by former iGEMers and employees from all levels of the KU Leuven.</p><br />
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<h3>Aurelie Lenaerts</h3><br />
<h5>Biomedical sciences - Sponsoring & Wiki</h5><br />
<p align="justify">Hell’s to the no if it isn’t Aurelie hopping up and down the stairs to get things done. This energetic member of our team will help with anything as long as it’s not taking care of insects. Between seducing sponsors and sparking the interest of young students for synthetic biology she almost made us walk 100 kilometers just to sponsor our iGEM adventure.</p><br />
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<h5>Chemical Engineering - Modelling & Human Practices</h5><br />
<p align="justify">Bert is one tough critic! Everything a KU Leuven iGEMer does/makes/says will be inspected, scrutinised and commented on by Bert. Luckily then, that he is on the Ethics team so that synthetic biology cannot slip by without being inspected in every way possible. Bert is a chemical engineer who is fascinated by biochemistry and microbial life. You can find him working hard at our iGEM office, on his beloved oscillator model.</p><br />
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<h5>Biochemistry & Biotechnology - Sponsoring & Outreach</h5><br />
<p align="justify">With a strong female presence in the KU Leuven iGEM team, Flore is power woman number 1. Flore is studying a Master of Biochemistry and Biotechnology. When she is not busy finding sponsors for the team, she will be busy in the lab and when she has tackled that, you could find her helping the education team out or doing a literature study. On top of that, Flore finds the time to enjoy running, travelling and baking! Showing you the power of Women in Science.</p><br />
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<h5>Biochemistry & Biotechnology - Wiki & Outreach</h5><br />
<p align="justify">Besides focusing on his Biochemistry and Biotechnology studies, Frederik likes to get lost in the woods and find his way back using a map and a compass. Other hobbies are bragging about the fact that he lives in the same city (read: village) as cyclist Tom Boonen, taking his Gene Technology exam for yet another time and working on our wiki since he loves informatics as well. Actually, in retrospect, we don’t really know why we hang out with him. Oh yeah, probably because he has the most contagious laugh ever known to mankind and he’s a really nice guy to be around.</p><br />
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<h5>Biochemistry & Biotechnology - Wetlab</h5><br />
<p align="justify">This guitar playing rock star is, yes you guessed it, also a Master student in Biochemistry and Biotechnology! Laurens likes to keep us entertained by singing and dancing iGEM medley versions of classics (if you would like to see him in action, join us on facebook or youtube for video uploads!) and in his spare time he likes to come to the lab and help out by singing to some pipets instead. Besides the common Belgian hobby of beer drinking, Laurens is also very interested in signal transduction and cell regulation. <br />
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<h3>Lukas Marcelis</h3><br />
<h5>Medicine - Wetlab & Human Practices</h5><br />
<p align="justify">Lukas is for sure the most energetic one of our team! Coordinating the wetlab and discussing (especially ethics) is one of his top favourite occupations this summer. Next to iGEM, this future doctor is also vice-president of the Leuven student-researchers association (LVSO). Next to his 24h science job, he also likes to have fun with his friends and enjoys sausages at the BBQ, combined with some booze. In September, he is planning to go scouting in the Amazon forest.</p><br />
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<h5>Philosophy - Human Practices</h5><br />
<p align="justify"> Michael Poorthuis is graduate student in philosophy at the KU Leuven and graduate student in medicine at Utrecht University. He provided an ethical review of our iGEM project of which the leading question was: Are ethics related to technological progress, viz synthetic biology, or is it like a sermon on Sunday, totally separated from everyday reality?</p><br />
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<h5>Bio-engineering - Wetlab & Communication</h5><br />
<p align="justify">Pieter is one of the wetlab boys whose main occupation is to run from one freezer to another. If this student Bio-engineering (Cell and gene technology) is not busy in the wetlab cloning 20 different genes at once, he’s probably sitting quiet in a corner in the iGEM room, making some new memes. When Pieter’s around, you always feel at ease. He is fan of rock music, a good movie and a cooled beer. Combine the last with any of the first two and he’s good to go.</p><br />
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<h5>Biochemistry & Biotechnology - Outreach & Wetlab</h5><br />
<p align="justify">Meet Robbert, he will be your guide to the internet. Also, to cats. And funny videos. Next, this Biochemistry & Biotechnology lover also likes to play volleyball, is active in the student movement and often writes texts for the Leuven student newspaper. Oh, and sorry Ke$ha, but the party don't start 'till Robbert walks in. </p><br />
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<h5>Biomedical Sciences - Logistics</h5><br />
<p align="justify">Saar is a true jack-of-all-trades. She is perfectly capable of jumping in to help out in the lab when needed, organising great barbecues, she is making sure that we have the most awesome gadgets, the best looking T-shirts and most importantly getting us to Lyon! With her high energy level, there’s no river too deep, no mountain too high and no problem she can’t handle. Thumbs up for the best logistics team member ever!</p><br />
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<h5>Medicine - Outreach & Communication</h5><br />
<p align="justify"> Sabine is our “education girl” of this summer, which matches perfectly with her dream of becoming a fantastic doctor. Being busy during summers as student-researcher, she still enjoys playing the bassoon in her spare time. Now, she’s keeping herself busy with giving presentations in secondary schools. With her drawing skills, she was even able to create a 3D model of our bacterium, which is now being used during the workshops in schools.</p><br />
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<p align="justify">This blonde, confident guy is Sander, also known as Sandieboy. Currently, he is pursuing his Masters degree in Bioscience Engineering: Cell and Gene technology, and he has a big interest in bio-informatics. Whenever he is not reading one article for three days, he is busy with modelling, and trying to create more efficient, dependent feedback mechanisms and interactions between the wetlab and the modelling teams. Sander has a really big temper, but in the weekends he dances all his troubles away, on events like Tomorrowland.<br />
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<h5>Molecular & Cellular Biophysics - Wetlab & Wiki</h5><br />
<p align="justify">Su Wang, also known as Dinox, is in his first year of the Master in Molecular and Cellular Biophysics. He is currently proving himself to be quite the animal in the lab! From dawn till dusk you can find Su in the lab, dancing with some pipets. He is one of the most sweetest Chinese we ever met, and likes to please everyone with his golden smile.</p><br />
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<h5>Bio-engineering - Sponsoring, Wetlab & Logistics</h5><br />
<p align="justify">Sylvie is the one you really need to pay attention to, since she is the best in persuading you to sponsor the KULeuven iGEM team 2013. Everybody is undoubtedly charmed by her writing skills. In normal daily life, Sylvie studies Bioscience Engineering: Cell and Gene Biotechnology with a minor in Plant Production. For her Master Thesis she will be studying stress in plants. In the little spare time that’s left for her, she likes to go climbing with friends, mountainbiking and snowboarding.</p><br />
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<h3>Tina Smets</h3><br />
<h5>Biomedical Sciences - Modelling & Communication</h5><br />
<p align="justify">Tina is, except for being a master student in Biomedical Sciences, also the only woman in our modelling group this year. Thanks to her female influence, all male modellers are doomed to follow a strict time schedule. When she’s not roaming the world of synthetic biology, she often goes swimming or is busy reading books. Interestingly, she has an incomprehensible affection for insects –even loves to eat them as well- and loves to go to metal festivals –completely dressed up.</p><br />
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<h5>Electrical Engineering - Modelling</h5><br />
<p align="justify">Tomas was raised in Geetbets, but came to the big city of Leuven to study Electrical Engineering, minoring in Living Systems. He is a busy bird as he is part of the European students organisation BEST as well as iGEM. Tomas is also exploring the world of entomophagy, although aphids are still not in his every day diet. He is part of the modelling team, where he is busy trying to make a model of the ecology around the bacteria. He has developed a rather special bond with teammate Bert, of which the extent is still a mystery to us. </p><br />
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<h5>Post-Doc</h5><br />
<p align="justify">Ingmar is currently a postdoctoral researcher at the <a href="http://www.biw.kuleuven.be/m2s/cmpg/">Centre of Microbial and Plant Genetics</a>. After his studies of bio-engineering, he did a PhD on the probiotic bacterium <I>Lactobacillus rhamnosus GG</I>. In this PhD, he studied the factors determining the probiotic effect of the <I>L. rhamnosus GG</I> in experimental colitis. Interdisciplinary studies were performed combining bacterial genetics and immunology. He always had and still has a profound interest in scientific research, especially linking applied microbiology, mucosal immunology and health issues. The opportunity to guide the students on this iGEM project in a rather new field of synthetic biology looked very appealing and fascinating but also challenging.</p><br />
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<p align="justify">Meet Veerle, she loves science but also values non-scientific aspects such as sponsoring, administration, logistics, ...<br />
Leuven is her hometown but she went abroad to obtain her PhD in genetics and biochemistry from the University of Vienna (Austria). While doing a postdoc in Canada, she came in contact with iGEM via the Calgary team of 2011, 2012. She was sold immediately and, upon her return to Leuven, more than happy to support the KU Leuven team.<br />
Her research interests lie with signal transduction; particularly in response to environmental changes. Currently she is working as a postdoctoral researcher in the lab of Prof. Dr. Mathieu Bollen (<a href="http://gbiomed.kuleuven.be/english/research/50000618">Dept of Cellular and Molecular Medicine</a>) on protein phosphatases and their roles as master metabolic regulators in health and disease.</p><br />
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<h3>Johan Robben</h3><br />
<h5>Professor</h5><br />
<p align="justify">Professor Dr. Johan Robben is a member of the Department of Chemistry, <a href="http://chem.kuleuven.be/en/research/bmsb/index.html">section Biochemistry, Molecular and Structural Biology</a> at the KU Leuven. He is leading the Laboratory of Molecular and Synthetic Biology which currently focusses on polymerases and artificial nucleic acids. His main expertise is in molecular biology, protein engineering, genomics and proteomics. He was scientific advisor of the KU Leuven iGEM student teams in <a href="https://2008.igem.org/Team:KULeuven">2008</a>, <a href="https://2009.igem.org/Team:KULeuven">2009</a> and <a href="https://2011.igem.org/Team:KULeuven">2011</a>.</p><br />
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<p align="justify">Sam is currently doing a Master in Philosophy at the KU Leuven. As advisor of the iGEM team he hopes to gain an understanding of the scientific process in the field of synthetic biology, and more specifically, the role of ethics within this process.</p><br />
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<h3>Other Advisors</h3><br />
<p align="justify">Thanks to <a href="http://www.kuleuven.be/wieiswie/en/person/00076107">Misha Soskine</a> for his unending tips, tricks, suggestions, wacky stories and ideas!</p><br />
<p align="justify">The KU Leuven iGEM alumni also came out in full force again - a huge Mexican wave of "thank you hands" goes, in no particular order, to <a href="http://www.kuleuven.be/wieiswie/en/person/00054594">Ruben Ghillebert</a>; <a href="http://www.kuleuven.be/wieiswie/en/person/00075708">Hanne Tytgat</a>; <a href="http://www.kuleuven.be/wieiswie/en/person/00073575">Tom Broeckx</a> for his help to the sponsor team; <a href="http://www.kuleuven.be/wieiswie/en/person/00064495">Marian Crabbe</a>; <a href="http://www.kuleuven.be/wieiswie/en/person/00061171">Lyn Venken</a>; and all others who added in a word of advice through the past months.</p></p><br />
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</html></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Ecological/ModellingTeam:KU Leuven/Project/Ecological/Modelling2013-10-29T03:50:45Z<p>Veerledewever: </p>
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Ultimately our project aims to reduce crop loss because of aphid infestation. Given the time span of the competition, it is impossible to conduct field experiments. Therefore we attempt to <b>predict the effect of our pheromones on the surroundings</b> through a series of modelling steps. Eventually, we might be able to calculate the optimal spacing of the stickers with BanAphids to be maximally effective.</p><br />
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The first step is to <b>calculate the concentration of the pheromones released in the environment</b>. When a colony of <b>BanAphids</b> is placed in a field, the produced substances (pheromones, ...) will be transported in the air by diffusion and convection. Diffusion is almost always present, whereas the convection term depends on the presence of a source. In our case the wind is responsible for convection, so we searched an appropriate term for wind convection. To establish a realistic model, certain parameters are needed. Therefore, approximate diffusion coefficients and air velocity were searched. Production of the pheromones by the colony was coupled with <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeS/Modelling">another modelling approach</a>.<br /><br />
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The formula above is commonly known as the convection-diffusion equation, which is a partial differential equation in c (the concentration of the pheromones in our case). D is the diffusion coefficient and u the velocity of the solvent (which is the air).<br />
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<p align="justify">Because we found no measured diffusion coefficients of the pheromones in literature, estimations were made with a <a href="http://www.epa.gov/athens/learn2model/part-two/onsite/estdiffusion-ext.html">calculator</a> based on methods described in (Lyman, Reehl & Rosenblatt, 1982). Using the average supplied by the calculator, the results are 4.62 x 10<sup>-6</sup> m<sup>2</sup>/s for E-beta-farnesene and 6.33 x 10<sup>-6</sup> m<sup>2</sup>/s for methyl salicylate. The conditions supplied were a pressure of 1 atm and a temperature of 15 &deg;C.</p><br />
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<p>Figure 1 ǀ Wind profile for a crop height of 2 m and a wind speed of 3.39 m/s at a height of 10 m.</p><br />
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Because of friction and obstacles on the earth’s surface, wind speed varies with altitude. Generally, the velocity increases with increasing altitude. <b>A logarithmic wind profile is appropriate for the part above the crops</b> (Goudriaan, 1977, p. 96). The formula for this profile is<br/><br />
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with u representing the velocity. Here d accounts for an upward shift above a vegetative cover. The relation d=0.63 x z<sub>c</sub> is suggested, where z<sub>c</sub> is the height of the crops. The length z<sub>0</sub> is called the roughness length and is often supposed to be about one tenth of z<sub>c</sub>.<br /><br /><br />
For the part inside the canopy, the profile is exponential.</p><br />
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As can be noticed, the wind speed decreases exponentially with extinction factor a when approaching ground level and thus going deeper into the canopy. u<sub>c</sub> is the speed at height z<sub>c</sub> and can easily be calculated by the formula of the logarithmic wind profile.<br /><br /><br />
Evidently, <b>the wind direction changes in time. Most regions however tend to have a dominant wind</b> (“Prevailing winds,” Wikipedia, 2013), at least during a certain time period. This is the reason our model only incorporates convection in one direction, which greatly simplifies calculation. Furthermore, average wind speeds are measured and published monthly by the Royal Meteorological Institute (KMI) in Belgium (“Maandelijkse normalen - KMI,” 2013). These are measured at 10 m above the ground, making it possible to calculate u*/k used in the log law. Now, all parameters for convection are known when the crop height is given. The wind profile was entered in the software using a piecewise function and self-defined parameters, making it easy to change wind speed and crop height. An example of the profile is plotted in Figure 1.</p><br />
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In order to solve our model, boundary conditions need to be specified. <b>We'll represent the environment as a square box (5x5x5m) with 6 faces</b>. Within this environment box, we assume a bacterium box (5x5x1cm), suspended in "mid air", with its own 6 (smaller) faces.<br />
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<p align="justify">For the environment box, the faces perpendicular to the wind direction were specified as an inflow (with a concentration of 0 mol/m<sup>3</sup>), respectively an outflow.</p><br />
<h4>No flux</h4><br />
<p align="justify">Within the environment box, the face representing the ground will have no flux through it, because we assume that the diffusion coefficient of our pheromones is much larger in soil than in air. Within the bacterium box, all faces but one (pheromone outlet) are given zero flux as well.<br />
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<p align="justify">The remaining 3 faces of the environment box remain "open" since the pheromone flux through these faces is unknown at this point.</p><br />
<h4>Specified flux</h4> <p align="justify">The upper face of the bacterium container is the only place where pheromones are released in the air. Under the assumption that the pheromone production and vaporisation is at steady state, we can set the flux at this surface equal to the production rate of the entire colony.<br /><br /><br />
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Once we know the production per cell (by the procedure in <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeS/Modelling">MeS Modelling</a>) it is straightforward to calculate the output of the whole colony by using an average cell density in the appropriate growth phase. This output represented as an amount per time can then be converted to a flux through the contact surface with the air, which can be entered in the simulation program. </p><br />
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<p>Figure 2 ǀ Geometry for the model: the blue dot is the container with <b>BanAphids</b>, <b>a</b> is an influx plane, <b>b</b> an open boundary and <b>c</b> a face with no flux.</p><br />
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Although we lacked real values for the production rates of the pheromones, we ran the model with a "ballparked" flux of 1 mmol/(m<sup>2</sup>s) to get an idea of the shape of the solution. We used a simple raised cosine function to capture the oscillating behaviour of the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Oscillator">oscillator</a> with a period of 1 hour. As can be seen in the animated figure below, <b>convection by wind is the dominant effect on dispersion of the pheromones</B>. Therefore the solution follows the oscillations quite well and lag or local accumulation of pheromones is not a problem. Note that the solution has negative values at some points, which is physically impossible of course. This is solely due to numerical errors and can be avoided by the use of a finer mesh (at a large computational cost).<br />
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<p>Figure 3 ǀ Vertical slice of the geometry showing the concentration of methyl salicylate. Time is in seconds.</p><br />
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One of the missing parameters in our model was the <b>sensitivity of aphids for E-beta-farnesene</b>. No values for the air concentration threshold could be found in literature, so we decided to use our model to calculate this. From the work of (Nault, Edwards & Styer, 1973) we found that the effect of E-beta-farnesene released by aphids can reach up to 3 cm far. Using the real-time emission analysis of (Schwartzberg et al., 2008) we fitted a decreasing exponential function through these points. The result of this is (0.34+0.7273*exp(-0.109/60*t))/2 ng/s for one aphid. To use this as a flux boundary condition in our model, some conversions have to be done. First, we'll assume that the pheromone is released from a cornicle represented by a sphere with a radius of 0.1 mm. When combining this with the molar mass of EBF, the result is 0.00012233*(0.17+0.36365*exp(-0.00181667*t))/Pi.<br/><br />
When looking at a distance of 3 cm from the "cornicle" we find an average <b>EBF air concentration of 3.36*10<sup>-6</sup> mol/m<sup>3</sup></b>, which can be taken as the <b>threshold value for aphid EBF perception</b>. These values can be used towards calculating the optimal distance between stickers, placed in individual plants. Please check out the practical uses below for further details.<br />
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At first, we used Mathworks Matlab with the Partial Differential Equations Toolbox, but this software was limited to 2D geometries and more stringent boundary conditions. Later on, we noticed that the Department of Chemical Engineering of our university provided COMSOL Multiphysics. This program was very suitable for our purposes, as it provides a model for &ldquo;Transport of Diluted Species&rdquo;. Our complete model can be downloaded <a href="https://static.igem.org/mediawiki/2013/4/4d/3D_diffusion_convection_clean.mph.zip">here</a>.<br />
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Apart from the shape of the pheromone cloud which will arise, more interesting conclusions can be drawn from these results.<br />
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<p align = "justify"> <b>Ideally we would know the threshold concentration for which the aphids and/or predators are sensitive. Using this, we can calculate how far apart the stickers with BanAphids can be</b>. The opposite is also possible: if we would like to have a specific distance between the stickers, it is possible to find the appropriate colony size in one sticker.<br />
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We can even go further and integrate data about the behaviour and reproduction of the insects to create a population model. This would allow to fully measure the effect on the environment and the crops.<br />
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Goudriaan, J. (1977). Crop micrometeorology : a simulation study. Wageningen University, Wageningen.<br /><br />
Lyman, W. J., Reehl, W. F., & Rosenblatt, D. H. (1982). Handbook of chemical property estimation methods: environmental behavior of organic compounds. McGraw-Hill.<br /><br />
Maandelijkse normalen - KMI. (n.d.). Meteo. Retrieved August 22, 2013, from http://www.meteo.be/meteo/view/nl/65239-Home.html<br /><br />
Prevailing winds. (2013, August 21). In Wikipedia, the free encyclopedia. Retrieved from http://en.wikipedia.org/w/index.php?title=Prevailing_winds&oldid=569524163<br /><br />
Nault LR, Edwards LJ, Styer WE (1973) Aphid alarm pheromones: secretion and reception. Environ Entomol 2: 101–105.<br/><br />
Schwartzberg et al. (2008), “Real-Time Analysis of Alarm Pheromone Emission by the Pea Aphid (Acyrthosiphon Pisum) Under Predation,” Journal of Chemical Ecology 34, no. 1: 76–81<br />
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Welcome to our data page! Here we will summarise everything we achieved this summer. Of course, if you want a more extensive explanation, please check out the corresponding wiki page.<br/><br />
Since the European Jamboree, we characterized our bricks further via <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Characterisation">Mass Spectrometry studies of MeS</a> and <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#Characterisation">quantitated the aphid response to the EBF producing BanAphids</a>. <br />
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<p align="justify">Our project aims to <b>reduce aphid infestations and thus improve crop yields for the industrial end-user and the private customer</b>. With an environmental project like ours, the importance of the computer and the feedback from our future end-users should not be underestimated. We adapted our project according to survey information and modelling results. Ultimately, we hope to reduce the costs of field tests via our <i>in silico</i> work.<br/><br/><br />
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First, we started our modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Cellular_Level" target="_blank">cellular level</a></b>. We must figure out the <b>impact of E-β-farnesene and methyl salicylate production on <i>E. coli</i></b>. Thus, we performed a <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA" target="_blank">Flux Balance Analysis</a>. Results were compared with wetlab data such as growth curves and GC-MS data.<br />
We also aimed to <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling" target="_blank">predict the exact amounts produced</a> and find the rate limiting steps</b>. Here we fed wetlab data into our algorithms. The outcome will define/defined our choice of promoters, plasmid copy number and additive requirements. </br></br><br />
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Secondly we did a lot of modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Colony_Level">colony level</a></b>. We designed an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Oscillator/Modelling" target="_blank">oscillating transcription factor network</a> to <b>regulate pheromone production for the "sticker enclosed" BanAphids</b>. This oscillator network allows communication between cells, enforcing a synchronised but oscillating production rhythm onto the whole colony. This will optimise the impact of our BanAphids on aphids and ladybugs even though a direct contact cue between aphids and BanAphids is prevented. We designed this model to answer the concerns of the private end-users regarding the spray (or honeydew) system.<br/><br/><br />
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Finally, we must know the <b>effect of our pheromones on <a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Ecosystem_Level" target="_blank">the ecosystem</a></b>. We performed a series of modelling steps which you can find in our <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Modelling" target="_blank">ecological model page</a>. This information is essential in several ways : </br><br />
It will define the choice of pheromone production rate, which we can regulate through eg. promoter ranges.</br><br />
Dispersion data will indicate the optimal spacing of the BanAphid stickers, key information for the end-user.<br/><br/><br />
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Summarised, <b>these algorithms allow us to model our system from the cellular metabolism throughout to the environmental impact</b>. Based on our models, we continuously adapted the actual building of the system towards the most effective circuit. This will reduce costs and save time when our <b>BanAphids</b> are ready for field tests, and later for actual use.</p><br />
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<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS">methyl salicylate bricks</a></b></li><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF">E-beta farnesene bricks</a></b></li><br />
<li> <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab">Ecological work</a></b>, testing pheromone impact on the ecosystem (i.e. plants, ladybugs, ...). Here, we found <b>industrial partners</b> in the companies Biobest and pcfruit.</li><br />
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Throughout the summer, we made <b>4 different BioBricks</b> involved in the production of methyl salicylate. After finding out that the MIT 2006 brick (<a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J45700" target="_blank">BBa_J45700</a>) only produced weak amounts of the wintergreen scent (MeS), we dove into the literature and discovered a possible lack of chorismate present in the bacteria. We tried to overcome this problem by overexpressing <i>aroG</i> in <i>E. coli</i>. In the literature, we found two mutations that could make DAHP synthase insensitive to allosteric inhibition. <b>We succeeded in biobricking the normal <i>aroG</i> gene</b>, which gives future teams the opportunity to introduce mutations themselves to overcome the chorismate problem. <b>We characterised our bricks with a renewed <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Smell Test">smell test</a>, <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Headspace GC">GC</a> and an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#SDS PAGE">SDS-PAGE</a> analysis.</b><br />
</p><br />
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</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>(E)-β-farnesene Experiments</h3><br />
<p align="justify">After the whole summer's work, we finally made <b>5 BioBricks</b> for this section.<br />
Our favourites are:</p><br />
<ol><br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> contains an open reading frame that codes for (E)-β-farnesene synthase from <i>Artemisia annua</i>. The enzyme converts farnesyl diphosphate into E-β-farnesene. It was a milestone in our project work. <b>We succeeded to remove an <i>EcoRI</i> site in the gene (AY835398.1). This gave us one of the basic parts we needed to create our system</b>. </li><br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> is a construct that constitutively expresses β-farnesene synthase. <b>This was the final device used for our</b> <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF" target="_blank">Aphid experiments</a>.</li><br />
<br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> is similar to <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a>. However, in this biobrick we added a <i>lac</i> operator in front of the β-farnesene synthase. <b>This makes it possible to switch of (E)-β-farnesene production by using biosensors expressing LacI.</b></li><br />
</ol><br />
<br />
<p align="justify">Our pilot studies with these biobricks and the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#aphid experiments">aphids</a> are promising and we now backed it up with quantitative data. Apart from this <i>in vivo</i> characterisation, we also initiated an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#SDSPAGE">SDS-PAGE</a> analysis.<b> Taken together this indicates that the EBF synthase is produced</b>. </p><br />
<br />
</div><br />
</div><br />
<br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Ecosystem Experiments</h3><br />
<p align="justify">Two companies (<b>Biobest and pcfruit</b>) specialised in biological pest management, were very <b>interested in our project</b> and invited us to perform experiments at their facilities. <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab" target="_blank">These experiments</a> demonstrated the effect of MeS in inducing plant defence mechanisms and that this has an effect on the aphid population</b>. <br/><br />
</div><br />
</div><br />
<br />
<!--HumanPratices--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices"><br />
<h3 class="bg-red">Human Practices</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">As a result of our <b>unique collaboration between philosophers and scientists within our team</b>, we formulated a new additional approach: <b>Bottom-up or ethics from within</b>. This approach improves the communication between different stakeholders and achieves reciprocal understanding in early stages of the project. From this approach the government, scientists, the public and even the industry can benefit. This new approach is bottom-up structured where the dialogue between philosophers, scientists and the general public is central. We have developed a new and strong framework for this approach in which we also explained why we should inform the general public using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HannahArendt" target="_blank">Hannah Arendt</a> and we examined the responsibility of synthetic biologists using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HansJonas">Hans Jonas</a>. <b>This is not only a first time in the iGEM competition that these well-known philosophers are read to provide a strong foundation, but has also never been done in the scientific literature!</b><br/><br />
In short, we consider this bottom-up collaboration between students a new starting point for approaching human practices within the iGEM competition.<br/><br />
</p><br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<br />
<p align="justify"><br />
Thanks to extensive dialogue we designed a product that interests both relevant industries like Biobest & pcfruit and the public. <b>Furthermore, we also asked <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/EndUser">end users</a> (farmers) if they would use our BanAphids and the results were very positive!</b><br/><br/><br />
<br />
<b>Our symposium for the general public</b> was another opportunity for interaction with the public. <b>We invited the other Benelux (Belgium, The Netherlands, Luxembourg) teams to present their project.</b> We hope that this symposium will become a yearly tradition in the iGEM competition.<br/><br/><br />
<br />
We also go the youth interested in synthetic biology! We went to <b>high schools</b> with our very own <a href="https://static.igem.org/mediawiki/2013/8/84/Plexiglas_system.JPG"><b>Plexiglas "biobricks"</b></a> which the students can use to work get familiar with the BioBrick system and we made a <a href="https://static.igem.org/mediawiki/2013/5/57/3D_bacterial_model.JPG" target="_blank"><b>3D-bacterial model</b></a>, which gives the students an idea of what a bacterium looks like.</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/DataPageTeam:KU Leuven/Project/DataPage2013-10-29T03:42:08Z<p>Veerledewever: </p>
<hr />
<div>{{:Team:KU Leuven/Template:Header}}<br />
{{:Team:KU Leuven/Template:Style}}<br />
{{:Team:KU Leuven/Template:Menu}}<br />
<br />
<html><br />
<br />
<body><br />
<div id="container"><br />
<div class="container"><br />
<br />
<!-- TITLE --><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-purple">Data Page</h3><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify"><br />
Welcome to our data page! Here we will summarise everything we achieved this summer. Of course, if you want a more extensive explanation, please check out the corresponding wiki page.<br/><br />
Since the European Jamboree, we characterized our bricks further via <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Characterisation">Mass Spectrometry studies of MeS</a> and quantitated the aphid response to the EBF producing BanAphids. <br />
</p><br />
</div><br />
</div><br />
<br />
<!--Modelling--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Project/modelling"><br />
<h3 class="bg-blue">Modelling</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">Our project aims to <b>reduce aphid infestations and thus improve crop yields for the industrial end-user and the private customer</b>. With an environmental project like ours, the importance of the computer and the feedback from our future end-users should not be underestimated. We adapted our project according to survey information and modelling results. Ultimately, we hope to reduce the costs of field tests via our <i>in silico</i> work.<br/><br/><br />
<br />
First, we started our modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Cellular_Level" target="_blank">cellular level</a></b>. We must figure out the <b>impact of E-β-farnesene and methyl salicylate production on <i>E. coli</i></b>. Thus, we performed a <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA" target="_blank">Flux Balance Analysis</a>. Results were compared with wetlab data such as growth curves and GC-MS data.<br />
We also aimed to <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling" target="_blank">predict the exact amounts produced</a> and find the rate limiting steps</b>. Here we fed wetlab data into our algorithms. The outcome will define/defined our choice of promoters, plasmid copy number and additive requirements. </br></br><br />
<br />
Secondly we did a lot of modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Colony_Level">colony level</a></b>. We designed an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Oscillator/Modelling" target="_blank">oscillating transcription factor network</a> to <b>regulate pheromone production for the "sticker enclosed" BanAphids</b>. This oscillator network allows communication between cells, enforcing a synchronised but oscillating production rhythm onto the whole colony. This will optimise the impact of our BanAphids on aphids and ladybugs even though a direct contact cue between aphids and BanAphids is prevented. We designed this model to answer the concerns of the private end-users regarding the spray (or honeydew) system.<br/><br/><br />
<br />
Finally, we must know the <b>effect of our pheromones on <a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Ecosystem_Level" target="_blank">the ecosystem</a></b>. We performed a series of modelling steps which you can find in our <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Modelling" target="_blank">ecological model page</a>. This information is essential in several ways : </br><br />
It will define the choice of pheromone production rate, which we can regulate through eg. promoter ranges.</br><br />
Dispersion data will indicate the optimal spacing of the BanAphid stickers, key information for the end-user.<br/><br/><br />
<br />
Summarised, <b>these algorithms allow us to model our system from the cellular metabolism throughout to the environmental impact</b>. Based on our models, we continuously adapted the actual building of the system towards the most effective circuit. This will reduce costs and save time when our <b>BanAphids</b> are ready for field tests, and later for actual use.</p><br />
</div><br />
</div><br />
<br />
<!--Wetlab--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Project"><br />
<h3 class="bg-green">Wetlab</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify"> Our wetlab work consists of 3 experimental parts :</br><br />
</p><br />
<ol><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS">methyl salicylate bricks</a></b></li><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF">E-beta farnesene bricks</a></b></li><br />
<li> <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab">Ecological work</a></b>, testing pheromone impact on the ecosystem (i.e. plants, ladybugs, ...). Here, we found <b>industrial partners</b> in the companies Biobest and pcfruit.</li><br />
</ol><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Methyl Salicylate Experiments</h3><br />
<p align="justify"><br />
Throughout the summer, we made <b>4 different BioBricks</b> involved in the production of methyl salicylate. After finding out that the MIT 2006 brick (<a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J45700" target="_blank">BBa_J45700</a>) only produced weak amounts of the wintergreen scent (MeS), we dove into the literature and discovered a possible lack of chorismate present in the bacteria. We tried to overcome this problem by overexpressing <i>aroG</i> in <i>E. coli</i>. In the literature, we found two mutations that could make DAHP synthase insensitive to allosteric inhibition. <b>We succeeded in biobricking the normal <i>aroG</i> gene</b>, which gives future teams the opportunity to introduce mutations themselves to overcome the chorismate problem. <b>We characterised our bricks with a renewed <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Smell Test">smell test</a>, <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Headspace GC">GC</a> and an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#SDS PAGE">SDS-PAGE</a> analysis.</b><br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>(E)-β-farnesene Experiments</h3><br />
<p align="justify">After the whole summer's work, we finally made <b>5 BioBricks</b> for this section.<br />
Our favourites are:</p><br />
<ol><br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> contains an open reading frame that codes for (E)-β-farnesene synthase from <i>Artemisia annua</i>. The enzyme converts farnesyl diphosphate into E-β-farnesene. It was a milestone in our project work. <b>We succeeded to remove an <i>EcoRI</i> site in the gene (AY835398.1). This gave us one of the basic parts we needed to create our system</b>. </li><br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> is a construct that constitutively expresses β-farnesene synthase. <b>This was the final device used for our</b> <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF" target="_blank">Aphid experiments</a>.</li><br />
<br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> is similar to <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a>. However, in this biobrick we added a <i>lac</i> operator in front of the β-farnesene synthase. <b>This makes it possible to switch of (E)-β-farnesene production by using biosensors expressing LacI.</b></li><br />
</ol><br />
<br />
<p align="justify">Our pilot studies with these biobricks and the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#aphid experiments">aphids</a> are promising and we now backed it up with quantitive data. Apart from this <i>in vivo</i> characterisation, we also initiated an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#SDSPAGE">SDS-PAGE</a> analysis.<b> Taken together this indicates that the EBF synthase is produced</b>. </p><br />
<br />
</div><br />
</div><br />
<br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Ecosystem Experiments</h3><br />
<p align="justify">Two companies (<b>Biobest and pcfruit</b>) specialised in biological pest management, were very <b>interested in our project</b> and invited us to perform experiments at their facilities. <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab" target="_blank">These experiments</a> demonstrated the effect of MeS in inducing plant defence mechanisms and that this has an effect on the aphid population</b>. <br/><br />
</div><br />
</div><br />
<br />
<!--HumanPratices--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices"><br />
<h3 class="bg-red">Human Practices</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">As a result of our <b>unique collaboration between philosophers and scientists within our team</b>, we formulated a new additional approach: <b>Bottom-up or ethics from within</b>. This approach improves the communication between different stakeholders and achieves reciprocal understanding in early stages of the project. From this approach the government, scientists, the public and even the industry can benefit. This new approach is bottom-up structured where the dialogue between philosophers, scientists and the general public is central. We have developed a new and strong framework for this approach in which we also explained why we should inform the general public using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HannahArendt" target="_blank">Hannah Arendt</a> and we examined the responsibility of synthetic biologists using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HansJonas">Hans Jonas</a>. <b>This is not only a first time in the iGEM competition that these well-known philosophers are read to provide a strong foundation, but has also never been done in the scientific literature!</b><br/><br />
In short, we consider this bottom-up collaboration between students a new starting point for approaching human practices within the iGEM competition.<br/><br />
</p><br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<br />
<p align="justify"><br />
Thanks to extensive dialogue we designed a product that interests both relevant industries like Biobest & pcfruit and the public. <b>Furthermore, we also asked <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/EndUser">end users</a> (farmers) if they would use our BanAphids and the results were very positive!</b><br/><br/><br />
<br />
<b>Our symposium for the general public</b> was another opportunity for interaction with the public. <b>We invited the other Benelux (Belgium, The Netherlands, Luxembourg) teams to present their project.</b> We hope that this symposium will become a yearly tradition in the iGEM competition.<br/><br/><br />
<br />
We also go the youth interested in synthetic biology! We went to <b>high schools</b> with our very own <a href="https://static.igem.org/mediawiki/2013/8/84/Plexiglas_system.JPG"><b>Plexiglas "biobricks"</b></a> which the students can use to work get familiar with the BioBrick system and we made a <a href="https://static.igem.org/mediawiki/2013/5/57/3D_bacterial_model.JPG" target="_blank"><b>3D-bacterial model</b></a>, which gives the students an idea of what a bacterium looks like.</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/DataPageTeam:KU Leuven/Project/DataPage2013-10-29T03:41:42Z<p>Veerledewever: </p>
<hr />
<div>{{:Team:KU Leuven/Template:Header}}<br />
{{:Team:KU Leuven/Template:Style}}<br />
{{:Team:KU Leuven/Template:Menu}}<br />
<br />
<html><br />
<br />
<body><br />
<div id="container"><br />
<div class="container"><br />
<br />
<!-- TITLE --><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-purple">Data Page</h3><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify"><br />
Welcome to our data page! Here we will summarise everything we achieved this summer. Of course, if you want a more extensive explanation, please check out the corresponding wiki page.<br/><br />
Since the European Jamboree, we characterized our bricks further via <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Characterisation">Mass Spectrometry studies of MeS<a/> and quantitated the aphid response to the EBF producing BanAphids. <br />
</p><br />
</div><br />
</div><br />
<br />
<!--Modelling--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Project/modelling"><br />
<h3 class="bg-blue">Modelling</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">Our project aims to <b>reduce aphid infestations and thus improve crop yields for the industrial end-user and the private customer</b>. With an environmental project like ours, the importance of the computer and the feedback from our future end-users should not be underestimated. We adapted our project according to survey information and modelling results. Ultimately, we hope to reduce the costs of field tests via our <i>in silico</i> work.<br/><br/><br />
<br />
First, we started our modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Cellular_Level" target="_blank">cellular level</a></b>. We must figure out the <b>impact of E-β-farnesene and methyl salicylate production on <i>E. coli</i></b>. Thus, we performed a <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA" target="_blank">Flux Balance Analysis</a>. Results were compared with wetlab data such as growth curves and GC-MS data.<br />
We also aimed to <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling" target="_blank">predict the exact amounts produced</a> and find the rate limiting steps</b>. Here we fed wetlab data into our algorithms. The outcome will define/defined our choice of promoters, plasmid copy number and additive requirements. </br></br><br />
<br />
Secondly we did a lot of modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Colony_Level">colony level</a></b>. We designed an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Oscillator/Modelling" target="_blank">oscillating transcription factor network</a> to <b>regulate pheromone production for the "sticker enclosed" BanAphids</b>. This oscillator network allows communication between cells, enforcing a synchronised but oscillating production rhythm onto the whole colony. This will optimise the impact of our BanAphids on aphids and ladybugs even though a direct contact cue between aphids and BanAphids is prevented. We designed this model to answer the concerns of the private end-users regarding the spray (or honeydew) system.<br/><br/><br />
<br />
Finally, we must know the <b>effect of our pheromones on <a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Ecosystem_Level" target="_blank">the ecosystem</a></b>. We performed a series of modelling steps which you can find in our <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Modelling" target="_blank">ecological model page</a>. This information is essential in several ways : </br><br />
It will define the choice of pheromone production rate, which we can regulate through eg. promoter ranges.</br><br />
Dispersion data will indicate the optimal spacing of the BanAphid stickers, key information for the end-user.<br/><br/><br />
<br />
Summarised, <b>these algorithms allow us to model our system from the cellular metabolism throughout to the environmental impact</b>. Based on our models, we continuously adapted the actual building of the system towards the most effective circuit. This will reduce costs and save time when our <b>BanAphids</b> are ready for field tests, and later for actual use.</p><br />
</div><br />
</div><br />
<br />
<!--Wetlab--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Project"><br />
<h3 class="bg-green">Wetlab</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify"> Our wetlab work consists of 3 experimental parts :</br><br />
</p><br />
<ol><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS">methyl salicylate bricks</a></b></li><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF">E-beta farnesene bricks</a></b></li><br />
<li> <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab">Ecological work</a></b>, testing pheromone impact on the ecosystem (i.e. plants, ladybugs, ...). Here, we found <b>industrial partners</b> in the companies Biobest and pcfruit.</li><br />
</ol><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Methyl Salicylate Experiments</h3><br />
<p align="justify"><br />
Throughout the summer, we made <b>4 different BioBricks</b> involved in the production of methyl salicylate. After finding out that the MIT 2006 brick (<a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J45700" target="_blank">BBa_J45700</a>) only produced weak amounts of the wintergreen scent (MeS), we dove into the literature and discovered a possible lack of chorismate present in the bacteria. We tried to overcome this problem by overexpressing <i>aroG</i> in <i>E. coli</i>. In the literature, we found two mutations that could make DAHP synthase insensitive to allosteric inhibition. <b>We succeeded in biobricking the normal <i>aroG</i> gene</b>, which gives future teams the opportunity to introduce mutations themselves to overcome the chorismate problem. <b>We characterised our bricks with a renewed <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Smell Test">smell test</a>, <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Headspace GC">GC</a> and an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#SDS PAGE">SDS-PAGE</a> analysis.</b><br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
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<h3>(E)-β-farnesene Experiments</h3><br />
<p align="justify">After the whole summer's work, we finally made <b>5 BioBricks</b> for this section.<br />
Our favourites are:</p><br />
<ol><br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> contains an open reading frame that codes for (E)-β-farnesene synthase from <i>Artemisia annua</i>. The enzyme converts farnesyl diphosphate into E-β-farnesene. It was a milestone in our project work. <b>We succeeded to remove an <i>EcoRI</i> site in the gene (AY835398.1). This gave us one of the basic parts we needed to create our system</b>. </li><br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> is a construct that constitutively expresses β-farnesene synthase. <b>This was the final device used for our</b> <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF" target="_blank">Aphid experiments</a>.</li><br />
<br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> is similar to <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a>. However, in this biobrick we added a <i>lac</i> operator in front of the β-farnesene synthase. <b>This makes it possible to switch of (E)-β-farnesene production by using biosensors expressing LacI.</b></li><br />
</ol><br />
<br />
<p align="justify">Our pilot studies with these biobricks and the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#aphid experiments">aphids</a> are promising and we now backed it up with quantitive data. Apart from this <i>in vivo</i> characterisation, we also initiated an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#SDSPAGE">SDS-PAGE</a> analysis.<b> Taken together this indicates that the EBF synthase is produced</b>. </p><br />
<br />
</div><br />
</div><br />
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<h3>Ecosystem Experiments</h3><br />
<p align="justify">Two companies (<b>Biobest and pcfruit</b>) specialised in biological pest management, were very <b>interested in our project</b> and invited us to perform experiments at their facilities. <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab" target="_blank">These experiments</a> demonstrated the effect of MeS in inducing plant defence mechanisms and that this has an effect on the aphid population</b>. <br/><br />
</div><br />
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<a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices"><br />
<h3 class="bg-red">Human Practices</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">As a result of our <b>unique collaboration between philosophers and scientists within our team</b>, we formulated a new additional approach: <b>Bottom-up or ethics from within</b>. This approach improves the communication between different stakeholders and achieves reciprocal understanding in early stages of the project. From this approach the government, scientists, the public and even the industry can benefit. This new approach is bottom-up structured where the dialogue between philosophers, scientists and the general public is central. We have developed a new and strong framework for this approach in which we also explained why we should inform the general public using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HannahArendt" target="_blank">Hannah Arendt</a> and we examined the responsibility of synthetic biologists using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HansJonas">Hans Jonas</a>. <b>This is not only a first time in the iGEM competition that these well-known philosophers are read to provide a strong foundation, but has also never been done in the scientific literature!</b><br/><br />
In short, we consider this bottom-up collaboration between students a new starting point for approaching human practices within the iGEM competition.<br/><br />
</p><br />
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</p><br />
</div><br />
</div><br />
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<div class="row-fluid"><br />
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<p align="justify"><br />
Thanks to extensive dialogue we designed a product that interests both relevant industries like Biobest & pcfruit and the public. <b>Furthermore, we also asked <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/EndUser">end users</a> (farmers) if they would use our BanAphids and the results were very positive!</b><br/><br/><br />
<br />
<b>Our symposium for the general public</b> was another opportunity for interaction with the public. <b>We invited the other Benelux (Belgium, The Netherlands, Luxembourg) teams to present their project.</b> We hope that this symposium will become a yearly tradition in the iGEM competition.<br/><br/><br />
<br />
We also go the youth interested in synthetic biology! We went to <b>high schools</b> with our very own <a href="https://static.igem.org/mediawiki/2013/8/84/Plexiglas_system.JPG"><b>Plexiglas "biobricks"</b></a> which the students can use to work get familiar with the BioBrick system and we made a <a href="https://static.igem.org/mediawiki/2013/5/57/3D_bacterial_model.JPG" target="_blank"><b>3D-bacterial model</b></a>, which gives the students an idea of what a bacterium looks like.</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/DataPageTeam:KU Leuven/Project/DataPage2013-10-29T03:31:50Z<p>Veerledewever: </p>
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Welcome to our data page! Here we will summarise everything we achieved this summer. Of course, if you want a more extensive explanation, please check out the corresponding wiki page.<br />
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<p align="justify">Our project aims to <b>reduce aphid infestations and thus improve crop yields for the industrial end-user and the private customer</b>. With an environmental project like ours, the importance of the computer and the feedback from our future end-users should not be underestimated. We adapted our project according to survey information and modelling results. Ultimately, we hope to reduce the costs of field tests via our <i>in silico</i> work.<br/><br/><br />
<br />
First, we started our modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Cellular_Level" target="_blank">cellular level</a></b>. We must figure out the <b>impact of E-β-farnesene and methyl salicylate production on <i>E. coli</i></b>. Thus, we performed a <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA" target="_blank">Flux Balance Analysis</a>. Results were compared with wetlab data such as growth curves and GC-MS data.<br />
We also aimed to <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling" target="_blank">predict the exact amounts produced</a> and find the rate limiting steps</b>. Here we fed wetlab data into our algorithms. The outcome will define/defined our choice of promoters, plasmid copy number and additive requirements. </br></br><br />
<br />
Secondly we did a lot of modelling on the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Colony_Level">colony level</a></b>. We designed an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Oscillator/Modelling" target="_blank">oscillating transcription factor network</a> to <b>regulate pheromone production for the "sticker enclosed" BanAphids</b>. This oscillator network allows communication between cells, enforcing a synchronised but oscillating production rhythm onto the whole colony. This will optimise the impact of our BanAphids on aphids and ladybugs even though a direct contact cue between aphids and BanAphids is prevented. We designed this model to answer the concerns of the private end-users regarding the spray (or honeydew) system.<br/><br/><br />
<br />
Finally, we must know the <b>effect of our pheromones on <a href="https://2013.igem.org/Team:KU_Leuven/Project/Modelling/Ecosystem_Level" target="_blank">the ecosystem</a></b>. We performed a series of modelling steps which you can find in our <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Modelling" target="_blank">ecological model page</a>. This information is essential in several ways : </br><br />
It will define the choice of pheromone production rate, which we can regulate through eg. promoter ranges.</br><br />
Dispersion data will indicate the optimal spacing of the BanAphid stickers, key information for the end-user.<br/><br/><br />
<br />
Summarised, <b>these algorithms allow us to model our system from the cellular metabolism throughout to the environmental impact</b>. Based on our models, we continuously adapted the actual building of the system towards the most effective circuit. This will reduce costs and save time when our <b>BanAphids</b> are ready for field tests, and later for actual use.</p><br />
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<p align="justify"> Our wetlab work consists of 3 experimental parts :</br><br />
</p><br />
<ol><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS">methyl salicylate bricks</a></b></li><br />
<li> The production and testing of the <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF">E-beta farnesene bricks</a></b></li><br />
<li> <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab">Ecological work</a></b>, testing pheromone impact on the ecosystem (i.e. plants, ladybugs, ...). Here, we found <b>industrial partners</b> in the companies Biobest and pcfruit.</li><br />
</ol><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Methyl Salicylate Experiments</h3><br />
<p align="justify"><br />
Throughout the summer, we made <b>4 different BioBricks</b> involved in the production of methyl salicylate. After finding out that the MIT 2006 brick (<a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J45700" target="_blank">BBa_J45700</a>) only produced weak amounts of the wintergreen scent (MeS), we dove into the literature and discovered a possible lack of chorismate present in the bacteria. We tried to overcome this problem by overexpressing <i>aroG</i> in <i>E. coli</i>. In the literature, we found two mutations that could make DAHP synthase insensitive to allosteric inhibition. <b>We succeeded in biobricking the normal <i>aroG</i> gene</b>, which gives future teams the opportunity to introduce mutations themselves to overcome the chorismate problem. <b>We characterised our bricks with a renewed <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Smell Test">smell test</a>, <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#Headspace GC">GC</a> and an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#SDS PAGE">SDS-PAGE</a> analysis.</b><br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>(E)-β-farnesene Experiments</h3><br />
<p align="justify">After the whole summer's work, we finally made <b>5 BioBricks</b> for this section.<br />
Our favourites are:</p><br />
<ol><br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> contains an open reading frame that codes for (E)-β-farnesene synthase from <i>Artemisia annua</i>. The enzyme converts farnesyl diphosphate into E-β-farnesene. It was a milestone in our project work. <b>We succeeded to remove an <i>EcoRI</i> site in the gene (AY835398.1). This gave us one of the basic parts we needed to create our system</b>. </li><br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> is a construct that constitutively expresses β-farnesene synthase. <b>This was the final device used for our</b> <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF" target="_blank">Aphid experiments</a>.</li><br />
<br />
<br />
<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> is similar to <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a>. However, in this biobrick we added a <i>lac</i> operator in front of the β-farnesene synthase. <b>This makes it possible to switch of (E)-β-farnesene production by using biosensors expressing LacI.</b></li><br />
</ol><br />
<br />
<p align="justify">Our pilot studies with these biobricks and the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#aphid experiments">aphids</a> are promising and we now backed it up with quantitive data. Apart from this <i>in vivo</i> characterisation, we also initiated an <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#SDSPAGE">SDS-PAGE</a> analysis.<b> Taken together this indicates that the EBF synthase is produced</b>. </p><br />
<br />
</div><br />
</div><br />
<br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<h3>Ecosystem Experiments</h3><br />
<p align="justify">Two companies (<b>Biobest and pcfruit</b>) specialised in biological pest management, were very <b>interested in our project</b> and invited us to perform experiments at their facilities. <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlab" target="_blank">These experiments</a> demonstrated the effect of MeS in inducing plant defence mechanisms and that this has an effect on the aphid population</b>. <br/><br />
</div><br />
</div><br />
<br />
<!--HumanPratices--><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices"><br />
<h3 class="bg-red">Human Practices</h3></a><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align="justify">As a result of our <b>unique collaboration between philosophers and scientists within our team</b>, we formulated a new additional approach: <b>Bottom-up or ethics from within</b>. This approach improves the communication between different stakeholders and achieves reciprocal understanding in early stages of the project. From this approach the government, scientists, the public and even the industry can benefit. This new approach is bottom-up structured where the dialogue between philosophers, scientists and the general public is central. We have developed a new and strong framework for this approach in which we also explained why we should inform the general public using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HannahArendt" target="_blank">Hannah Arendt</a> and we examined the responsibility of synthetic biologists using the ideas of philosopher <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/HansJonas">Hans Jonas</a>. <b>This is not only a first time in the iGEM competition that these well-known philosophers are read to provide a strong foundation, but has also never been done in the scientific literature!</b><br/><br />
In short, we consider this bottom-up collaboration between students a new starting point for approaching human practices within the iGEM competition.<br/><br />
</p><br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<br />
<p align="justify"><br />
Thanks to extensive dialogue we designed a product that interests both relevant industries like Biobest & pcfruit and the public. <b>Furthermore, we also asked <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/EndUser">end users</a> (farmers) if they would use our BanAphids and the results were very positive!</b><br/><br/><br />
<br />
<b>Our symposium for the general public</b> was another opportunity for interaction with the public. <b>We invited the other Benelux (Belgium, The Netherlands, Luxembourg) teams to present their project.</b> We hope that this symposium will become a yearly tradition in the iGEM competition.<br/><br/><br />
<br />
We also go the youth interested in synthetic biology! We went to <b>high schools</b> with our very own <a href="https://static.igem.org/mediawiki/2013/8/84/Plexiglas_system.JPG"><b>Plexiglas "biobricks"</b></a> which the students can use to work get familiar with the BioBrick system and we made a <a href="https://static.igem.org/mediawiki/2013/5/57/3D_bacterial_model.JPG" target="_blank"><b>3D-bacterial model</b></a>, which gives the students an idea of what a bacterium looks like.</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Ecological/wetlabTeam:KU Leuven/Project/Ecological/wetlab2013-10-29T03:28:01Z<p>Veerledewever: </p>
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<h3>Background</h3> </a><br />
<p>BanAphids' effect on plants and insects</p><br />
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<p>You are here!</p><br />
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<h3 class="bg-green">Insect & Plant Experiments</h3><br />
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<p align = "justify">To validate our <b>BanAphids</b> model, we needed to investigate the effect of E-β-farnesene (EBF) and Methyl Salicylate (MeS) on aphids and plants. We need to know the working concentrations of our two substances in pure form and to examine the behaviour of aphids and their predators under different conditions and environments. Moreover, we studied the impact of pure MeS and/or EBF on plant growth.</br></br><br />
We noticed quickly that this agricultural problem is looked at by various companies, although from a different angle. <a href="http://www.bbsrc.ac.uk/organisation/institutes/institutes-of-bbsrc/rothamsted.aspx">Rothamsted</a>, a UK based research station, is currently investigating the use of EBF to repel aphids as well, but in the form of GM crops. Locally, several companies were interested in our project. Biobest, worldwide leader in sustainable crop protection and pcfruit, a research company focussing on biological fruit growing and new crop protection methods both invited us to discuss our project.</br><br />
<br />
After fruitful visits, Biobest and pcfruit opened their research departments for us. We could perform our insect experiments there to validate our <b>BanAphids</b> model. With the data we have collected so far, we can observe that these two substances indeed do have an effect on aphids. <br />
</p><br />
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<img src="https://static.igem.org/mediawiki/2013/9/9c/Biobest.png"/><br />
<p></p><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/PcFruitLogo.png"/><br />
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<h3>Index</h3><br />
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<a href="#Behaviour"><h4>Behavioural experiments</h4></a><br />
<a href="#MeS effect on predators"><p>Natural Enemies</p></a> <br />
<a href="#MeS effect on aphids"><p>Repellent effect of EBF</p></a> <br />
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<a href="#Induced plants aphid"><h4>Effect of MeS induced plant</h4></a><br />
<a href="#Aphid preference root1"><p>Total aphid population - root induced</p></a><br />
<a href="#Aphid preference leaf1"><p>Total aphid population - leaf induced</p></a><br />
<a href="#Aphid preference root2"><p>Aphid distribution - root induced</p></a><br />
<a href="#Aphid preference leaf2"><p>Aphid distribution - leaf induced</p></a><br />
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<a href="#Predator preference leaf1"><h4>Predator preference - in vivo situation</h4></a><br />
<a href="#Fitness impact"><h4>Fitness impact</h4></a><br />
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<h3>Defining the working range</h3><br />
<p align="justify"><b>E-β-farnesene (EBF) is the most common compound of the aphid's alarm pheromones</b>. The general response to this stress-signal is a change in gene expression and subsequent protein expression, causing the aphids to become restless, form wings and leave the plant. Here, we used synthetic EBF (Sigma-Aldrich, farnesene, mixture of isomers) to study these effects. Check out our <b><a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF">E-beta-farnesene construction page</a></b> for the characterization of the EBF producing biobrick. The result from a BanAphid exposure on aphids can also be seen here below.</br></br><br />
Methyl salicylate (MeS) on the other hand attracts the natural predators of the aphids. We tried to prove this <b>kairomone effect of methyl salicylate</b> with a synthetic variant of MeS (Sigma-Aldrich, methyl salicylate ≥99% pure).</br></br><br />
We experimented with three different set-ups to determine a working range of MeS and EBF. Our <b>BanAphids</b> are built to produce an alternative source of EBF and MeS but with the same effects as the synthetic counterparts. We also needed the aphids' natural enemies (ladybugs, green lacewings and parasitic wasps), which we received from biobest. Their description is given <a href="https://2013.igem.org/Team:KU_Leuven/Project/aphidbiology#Model%20Organisms"> here.</a> </p><br />
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<p><B>Second set-up, with wind tunnel</B></p><br />
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<h3>Attracting Natural Enemies with synthetic MeS</h3><br />
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1. The aphid's natural enemies were subjected to <b>a choice experiment</b>. Starting from a determined point on our set-up, the insects were released and given a choice between one of the five MeS concentrations and a control solution, which was the solvent used (in this case ethanol). The insects were given 1 to 3 mins to make a choice. After our first day at Biobest we had very disappointing results, there was no clear reaction to be seen to either the MeS or the control solution. This could be the result of performing the experiments under a fume hood, this might have resulted in the rapid elimination of MeS.<br/><br />
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2. With advice from Biobest we did some troubleshooting, we diluted MeS and EBF in other solvents, in particular, hexane and paraffin oil. We chose to use <b>paraffin oil </b>because MeS is a volatile and paraffin oil would allow the slow release of this volatile, giving the insects more time to respond to MeS. Under the advice from prof. dr. Felix Wäckers and dr. Veerle Mommaerts, we also tried to prime the insects first. Practically, priming the insects means that we let a few predator/parasitoid insects loose on a washed aphid-infested leaf. The insects that showed interest in the leaf, this means that the insect were walking around in search of aphids, were further used in the behavioural experiments. We also tried the experiment with a fan, mimicking a wind tunnel, that blows in the direction of the insects so that the MeS fumes are blown toward the insect. In these set of experiments we used a washed <b>aphid-infested leaf as a control compared to responses to the infested leaf introduced with one of the five MeS or EBF concentrations </b>. This however did not demonstrate any clear behaviour form the insects other than random movement. We contemplated which parameters we could change, we thought that the insects were maybe more interested in their new 'strange' environment, the set-up, than the substances but other than changing the set-up, the concentrations we used and the solvent, we didn't see many other variables that could have affected our results. <br/><br/><br />
3. After talking to Tim Belien of pcfruit, he offered us their <b>Y-tube olfactometer and their lab</b>. We compared again all the five MeS concentrations with the control in a Y-tube olfactometer. The lady bug adults were first released into the Y-tube to become habituated with the set-up, before being exposed to MeS or EBF. We could see an obvious attraction of three ladybugs in a row at a MeS concentration of 0,1 ng/ml. But after changing the side of the Y-tube in which MeS was released, we could not see the attraction anymore. We think that the Y-tube olfactometer was not clean enough and that there was still some MeS on the other tube as well. We saw repulsion of all ladybugs at a MeS concentration of 100 ng/ml, demonstrating the upper limit of MeS concentrations. These experiments gave us an indication of the working MeS concentrations.</p><br />
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<h3>Aphid repellent effect of BanAphid EBF</h3><br />
<p align="justify">In our first setup we tried releasing several EBF concentrations below a sweet pepper plant, infested with aphids. The behaviour of the aphids was observed for an hour after the release of the alarm pheromone EBF. We first used pure EBF because we were afraid that there would be no response. In the beginning of the experiment, we could see some movement of the aphids, but not for all of them. After 20 min, they were back on the plant sucking sap. The extremely high concentration of EBF most likely made them insensitive to an EBF effect.<br />
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We then decided to test EBF concentrations that are more realistic to what individual aphids could produce. Luckily, our bacterial EBF construct was ready to go! So we decided to perform a second aphid experiment with our <b>BanAphids</b>, to read more about these setup, click <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBF#aphid%20experiments">here</a>. We saw a positive result from one of these setups, as can be seen on the video. Aphids are sitting calmly on the leaf (sedentary insects) and from the moment the EBF producing BanAphids are released, we see the aphids becoming mobile.</p><br />
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<h3>What we expected the plants to do</h3><br />
<p align="justify">The effects of EBF on aphids are described thoroughly in different articles, contrary to their responses to MeS. MeS is a critical signal molecule in the induction of plant defence mechanisms and therefore it should have a negative effect on the aphids. According to the optimal defence theory, a plant will protect its most valuable parts when under attack, for instance, their reproductive parts and terminal leaves. We therefore expected to see a redistribution of the aphids to the lower leaves after induction with MeS. A plant has different defence pathways depending on the site of infection (i.e. roots or leaves). We therefore used <b>two different induction methods</b>. We induced the plants with five different concentrations of MeS through the roots with water and via spraying with ethanol on the leaves. If you would like to find out how to practically induce a plant, please look at our <a href="https://2013.igem.org/Team:KU_Leuven/Protocols#Aphid_population_preference"> protocol</a>. 48h (day 2 of the experiment) post-MeS induction, 15 of the smallest aphids were placed on the head of the plant so that the following generation would be the F1 generation and the plants were placed individually in nets. The distribution and total number of aphids were examined on day 7 and day 10.</p><br />
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<h3>Total aphid population - root induced</h3><br />
<p align="justify">On day 7 and 10, we examined the total amount of aphids on the plants. We expected to see no change in the total amount of aphids on the plants (15 aphids), since the aphids should not be able to reproduce yet. <br />
We indeed see that the amount of aphids is around 15 aphids per plant, demonstrating that replication has not occurred yet neither amongst the control plants nor at the MeS induced plant. We do see a small trend at day 7: aphids on the plants induced with the lowest MeS concentrations (0,01 and 0,1 ng/ml) seemed to show no difference when compared to the control. Amongst the plants induced with the highest MeS concentrations (0,4; 0,8 and 1 ng/ml), there seem to be a slightly higher amount of aphids. Their origin is unclear, possibly we did not put the youngest aphids possible on the leaf so some may have reproduced in the days past after all. This needs to be compared to data from day 10 before we can draw any conclusions. On some plants there is much more than 15 aphids but this is probably the result of contamination while placing the aphids on the plant. On day 10 we see that now the lower concentrations of MeS contain more aphids than the higher MeS concentrations and the control.</p><br />
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<p><B>MeS induction via the roots on day 7 - total aphid population</B></p><br />
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<p><B>MeS induction via the roots on day 10 - total aphid population</B></p><br />
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<p align="justify">We see here approximately the same results as the total aphid population via root induction. On day 7 we see again a slight increase in aphid population amongst the plants induced with the highest MeS concentrations (0,4; 0,8 and 1 ng/ml), but on day 10 we see that the aphid population seems to be the highest amongst plants induced with the lowest MeS concentrations (0,01 and 0,1 ng/ml). </p><br />
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<p><B>MeS induction via the leaf on day 7 - total aphid population</B></p><br />
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<p><B>MeS induction via the leaf on day 10 - total aphid population</B></p><br />
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<p align="justify">On day 7, there is a small variation in aphid population but not so different from the control. This is expected since reproduction should not be possible yet. On day 10, we see that the plants induced with highest MeS concentrations (0,4; 0,8 and 1 ng/ml) are less populated than the control plant. This could be a demonstration of the plant's defence against aphids, reducing the reproductive capacity of the aphids. We also noticed an increased amount of winged aphids on these highest MeS concentration induced plants compared to the lowest concentrations and the control. This indicates that the aphids were motivated to leave the plant. The plants induced with the lowest MeS concentrations (0,01 and 0,1 ng/ml) did not show much change in aphid population compared to the control. It is possible that these concentrations of MeS were too low to induce the plant's defence mechanisms; the plant remained neutral and results don't differ much from the control. Finally, our observations showed similar results, irrespective of the chosen induction method (roots or leaves).</p><br />
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<h3>Aphid distribution on the plant - root induced</h3><br />
<p align="justify">After counting all the aphids on the plant, we calculated the number of aphids on the head of the plant. Normally, aphids prefer young leaves since they have the least "woody" elements. these young leaves are most prominent at the the head of the plant. According to the optimal defence theory, we expect the MeS induced plant to protect it's most valuable parts, this includes the head of the plant, so that these parts become unattractive for aphids and they redistribute themselves. We can see that the control has the highest percentage of aphids in the top of the plant, compared to the MeS induced plants. The difference is most clear with plants induced through the roots. </p><br />
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<p><B>MeS induction via the roots on day 7 - aphid distribution</B></p><br />
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<h3>Aphid distribution on the plant - leaf induced</h3><br />
<p align="justify">Induction of the plants with MeS results in a redistribution of the aphids to the lower leaves, just like the optimal defence theory predicts. The younger leaves are more valuable for the plants and are therefore better provided with defence mechanisms. The redistribution of the aphids is more clear in the plants induced through the roots, in contrary to the plants induced via spraying on the leaves. The percentage of aphids in the head of the plant in leaf-induced control plants is significantly less than in root-induced control plants. It is difficult to explain why this is, but it does also mean that aphid distribution doesn't differ much from the control amongst the leaf-induced plants. In the root-induced plants, the percentage of aphids on the head of the plant is , as expected, less than in the control plant. Possibly, induction through the roots feeds immediately into the vertical axis (root to head) which is a better route for stimulating the plant defence mechanism than a horizontal route (via the leaves).</p><br />
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<p><B>MeS induction via the leaf on day 7 - aphid distribution</B></p><br />
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<p><B>MeS induction via the leaf on day 10 - aphid distribution</B></p><br />
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<h3>Fitness impact of the MeS induction</h3><br />
<p align="justify"> During the experiment we noticed a clear difference in plant growth rates between the two methods of induction. The plants induced via their leaves were much bigger than the plants induced through their roots, even though the plants induced via the leaf suffered actual ethanol damage.</p><br />
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<p><B>root induced control plant vs. root induced plant</B></p><br />
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We were therefore curious about the effect of MeS on the root growth. All the roots of the induced plants were washed, dried and weighed. The most important parameters were measured: the primary root, the width of the top of the primary root and the longest secondary root. To learn more about the procedure, click <a href="https://2013.igem.org/Team:KU_Leuven/Protocols#Root_measurements">here</a>. Just as there were differences in plant growth, the same was observed in the growth of the roots. The plants induced through their roots had much shorter and thinner roots in contrast to the plants induced via the leaves. The decrease in growth of the plants induced via the roots can be the result of the method of induction. The other possibility is that root induced plants activate aboveground and belowground mechanisms and therefore more resources are used for the plant defences instead of the plant growth. <br/><br />
By comparing the leaf induced control plant and the root induced control plant, we can see that the root induction results in smaller plants due to the thinner and shorter roots. This suggests that root induction does cause a certain amount of damage to the plant and this can account for a certain percentage of the reduction in growth amongst the root induced plants. However, after comparing the roots of the root induced control plant and a MeS root induced plant, we can see that the MeS induced plant has smaller and thinner roots than the control. This suggests that the decreased growth is probably due to the MeS induction accounting for the remaining percentage. Induction of the plant defences requires a lot of energy, so that the plant suffers a fitness impact, having less energy to spend on their growth. There are no clear differences observable in root length or thickness between the different MeS leaf induced plants compared to the control. This yet again demonstrates the difference in effect between the two induction methods, and that it could account for a difference in aphid behaviour.</p><br />
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<p><B>Comparison of root induced plant with its control</B></p><br />
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<p><B>Comparison between leaf control and root control</B></p><br />
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<p><B>Comparison between the two induction methods</B></p><br />
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<h3>Predator preference to MeS induced plants</h3><br />
<p align="justify"> After the last aphid count on day 10, we used the plants for a cafeteria experiment with predators. We wanted to examine the effect of the different MeS inductions on the predator preference. In this experiment we used <a href="https://2013.igem.org/Team:KU_Leuven/Project/aphidbiology#Macrolophus%20pygmaeus"><i>Macrolophus</i></a>, a recently identified natural enemy of the aphids. The predators were subject to a choice experiment. A plant of every MeS concentration and a control was placed in a circle and approximately 15 <i>Macrolophus</i> were released in the middle of the circle. To know exactly how we performed this experiment, click <a href="https://2013.igem.org/Team:KU_Leuven/Protocols#Predator_attraction_to_MeS_induced_aphid_infested_plants">here</a>. The number of predators on every plant was counted after 45 min. Next, the predators were shaken off the plant. This was repeated twice. To determine the effect on a longer time span, we also counted the aphids after 24h.</p><br />
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<p><B>Predator preference after 45 min</B></p><br />
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<p><B>Predator preference after 24 hours</B></p><br />
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<p align="justify"> After 45 mins, root-induced plants with the highest MeS concentrations (0,8 and 1 ng/ml) contained more predators than the control plant, conversely the leaf-induced plants had less predators with higher concentrations. After 24 hours, the levels of predators present on induced vs. control plants equalised for both conditions. The results shown in the graph are the mean percentage of the three counts.</p><br />
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<p><B>Predator preference after 45 min</B></p><br />
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<p><B>Predator preference after 24 hours</B></p><br />
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<p align="justify">It is difficult to draw a conclusion. It is possible that the different methods of induction stimulate a different activation of defence mechanism, therefore leading to changes in the interactions with aphids and predators. Possibly the MeS induction effect has petered out by this time (day 13-14). We could test this by a secondary MeS induction at day 7. Once again, more work for the future.</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/ProtocolsTeam:KU Leuven/Protocols2013-10-29T01:43:17Z<p>Veerledewever: </p>
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<a href="#Aphid population preference">Aphid population preference</a><br><br />
<a href="#Aphid mobility experiment">Aphid mobility experiment</a><br><br />
<a href="#Chemically competent E.coli cells: CaCl2 method">Chemically competent E.coli cells: CaCl2 method</a><br><br />
<a href="#Chemically competent E.coli cells: Inoue method">Chemically competent E.coli cells: Inoue method</a><br><br />
<a href="#Colony PCR for Streptomyces">Colony PCR for Streptomyces</a><br><br />
<a href="#Digestion and ligation">Digestion and ligation</a><br><br />
<a href="#DNA extraction from agarose gels">DNA extraction from agarose gels</a><br><br />
<a href="#GC-MS analysis">GC-MS analysis</a><br><br />
<a href="#Grow electrocompetent cells">Grow electrocompetent cells</a><br><br />
<a href="#Headspace GC">Headspace GC</a><br><br />
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<div class="span6 greytext"><br />
<a href="#Isolation of plasmid DNA from E. coli (mini prep)">Isolation of plasmid DNA from E. coli (mini prep)</a><br><br />
<a href="#PCR clean-up">PCR clean-up</a><br><br />
<a href="#PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer">PCR Protocol for Taq DNA Polymerase</a><br><br />
<a href="#Plasmid DNA isolation">Plasmid DNA isolation</a><br><br />
<a href="#Predator attraction to MeS induced aphid infested plants">Predator attraction to MeS induced aphid infested plants</a><br><br />
<a href="#Predator attraction to Methyl Salicylate">Predator attraction to Methyl Salicylate</a><br><br />
<a href="#Protein Extraction">Protein Extraction</a><br><br />
<a href="#qRT-PCR Protocol">qRT-PCR Protocol</a><br><br />
<a href="#Root measurements">Root measurements</a><br><br />
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<a id="Aphid population preference"> </a><br />
<h3 class="bg-yellow">Aphid population preference</h3><br />
</div><br />
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<b>Aim</b>: To determine the effect on aphid population on Methyl Salicylate (MeS) induced plants.<br />
<br/> <br/><br />
Experimental setup: We induced five plants per concentration<br />
*Plants: Small potted paprika plants in two ways: via the root or byspraying; 60 in total. Paprika plants don’t make MeS naturally but do produce salicylic acid. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml ,0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 97% pure ethanol or water. <br />
<br><br />
<b>Procedure</b><br />
<br />
Induction requires 48h<br />
*Induction via the roots<br />
#Uproot the plant, clean off the dirt with water because compost can interfere with the uptake of MeS<br />
#Wipe off the water before placing the roots in a cup with the desired concentration for 10 minutes. MeSA was first suspended in ethanol before being further diluted to the desired concentration in 50ml water<br />
#Re-pot the plant and divide the remainder of the MeSA solution amongst the 5 plants <br />
[[File:Rootinduction.png|200px|Root induction]]<br />
<br />
*Induction via the leaf<br />
#The desired concentrations were diluted in 15ml ethanol, resulting in 3ml per plant. Ethanol has been shown to have no plant induction properties<br />
#Each leaf of the plant is sprayed above and underneath<br />
[[File:Leafinduction.png|200px|Leaf induction]]<br />
<br/><br />
*Place aphids on MeS induced plants 48h post-induction<br />
#Place 15 green peach aphids on the head of each induced plant<br />
#Take the smallest aphids present on the aphid-infested leaf. To be sure that it is a first generation aphid<br />
#Place each plant in a separate net<br />
#Plants of the same concentration, induced by spraying or via the roots, are placed in the same row; 10 plants per row. The rows are roughly 1 metre apart<br />
* Counting aphids on day 7<br />
#Following a form, count how many aphids are on Cotyledon, separate true leafs and the head of the induced plants.<br />
#See <a href"> results</a><br />
#There is a possibility of contamination while placing the aphids on the plant, meaning that older aphids and/or flying aphids crawled onto the plant by accident. These were removed from the plant so that the amount of aphids on day 10 would not be tainted<br />
<br />
[[File:Howtocountaphids.png|300px|How to count aphids]]<br />
<br />
* Counting aphids on day 10<br />
#We count the aphids for a second time to allow the second generation to develop. This way we investigate whether MeS has an effect on the aphid’s behaviour. That they are encouraged to leave the plant, reproduce less or generate mobile (flying) aphids. <br />
#The head of the plant is where the majority of the secondary metabolites gather; hence we expect to see an effect on the distribution of the aphid population.<br />
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<a id="Aphid mobility experiment"> </a><br />
<h3 class="bg-yellow">Aphid mobility experiment</h3><br />
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<br/><b>Aim</b>: To determine the effect of EBF on aphid mobility.<br />
<br/> <br/><br />
<b>Experimental setup</b>: Leaves infested with aphids were divided in two glass containers (not sealed) randomly (n=3; 3 leaves per group). After 1h plates with bacteria, either control (BL21) or BL21 with EBF construct, were put under each leaf.<br />
<br />
<br><br />
<b>Measurement</b>: Aphids that were on the top of each leaf were counted at start and at each time point during the experiment.<br/><br />
At each time point the amount of aphids moving on the leaf were also counted. The time points used were 0, 50, 150 and 200 minutes.<br />
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<a id="Chemically competent E.coli cells: CaCl2 method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: CaCl2 method</h3><br />
</div><br />
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<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells!''' <br\><br />
'''Do not shock freeze (liqN2) – just transfer from ice to -80°C!''' <br\><br />
'''Work sterile!'''<br />
<br />
#Inoculate '''3 ml''' growth medium with your cells of choice ('''DH5alpha''' or '''TOP10''' for plasmid maintenance & cloning)<br />
#Grow overnight at '''37°C''' with sufficient aeration<br />
#Inoculate '''100 ml LB''' with '''1 ml''' of overnight culture<br />
#Grow at '''37°C''' to an OD 600nm of approx '''0.5 to 0.8''' (usually '''2-3 hrs''')<br />
#Centrifuge cells ('''3700-4000 rpm 4°C 12 min''' – sterile 50ml tube)<br />
#Resuspend pellet on ice with FSB to '''15 ml''' (cold) for each '''100 ml''' pellet<br />
#Incubate cells '''10 min''' on ice<br />
#Centrifuge cells ('''3700 – 4000 rpm 4°C 10 min''')<br />
#Re-suspend pellet on ice in '''4-8 ml''' FSB (cold) for each '''100 ml pellet'''<br />
#Aliquot cells appropriately ('''200-400 µl aliquots''') and freeze aliquots at '''-80°C'''<br />
<br><br />
<b>Buffers and solutions</b><br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Growth medium<br />
**LB 25 g/l<br />
*Frozen Storage Buffer (FSB)<br />
**10 mM Potassium Acetate<br />
**10% glycerol<br />
**10 mM KCl<br />
**50 mM CaCl2<br />
**Check pH – must be around 6.2 – if need be adjust with AcAc (HCl) or KOH<br />
**Buffer should be filter-sterilized (0.45 micrometer filter)<br />
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<a id="Chemically competent E.coli cells: Inoue method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: Inoue method</h3><br />
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<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells.'''<br/><br />
'''Work sterile'''<br/><br />
#Pick a single colony from a freshly transformed plate (after overnight growth '''at 37 °C''')<br />
#Transfer the colony to '''25 ml growth medium''' in a sterile '''250 ml''' erlenmeyer<br />
#Incubate the culture '''at 37°C''' for '''6 – 8 hrs''' under vigorous shaking ('''250 – 300 rpm''')<br />
#Prepare '''3 1L flasks''' with '''250 ml growth medium''' in each<br />
#Inoculate the flasks with 10, 4 or 2 ml of the dayculture -> you create 3 different starting optical densities.<br />
#Incubate the cultures at '''18-22°C overnight''' under moderate shaking ('''180 – 220 rpm''')<br />
#Monitor the '''OD600nm''' until it reaches '''0.55'''<br />
#Place cells in an ice-water bath to cool them down quickly (-> swirl occasionally, keep them in for approx 10 min) <br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''80 ml icecold inoue transformation buffer'''<br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''20 ml icecold inoue transformation buffer'''<br />
#Add '''1.5 ml 100% DMSO''' – mix by swirling<br />
#Store whole on ice for approx '''10 minutes'''<br />
#Aliquot as quickly as possible '''100 – 200 µl aliquots''' into '''1.5 ml tubes''' (precooled on ice) and snapfreeze them into a liquid N2 bath<br />
<br />
<b>Buffers and solutions</b><br />
<br />
*Growth medium<br />
*Inoue transformation buffer<br />
{| class="wikitable"<br />
| '''Reagent''' || '''Final concentration (mM)''' || '''Amount per liter''' <br />
|-<br />
| MnCl2 || 55 || 10.88 g (from MnCl2*4H2O) <br />
|-<br />
| CaCl2 || 15 || 2.20 g (from CaCl2*2H2O) <br />
|-<br />
| KCl || 250 || 18.65 g (from KCl) <br />
|-<br />
| PIPES || 10 || 20 ml (from 0.5M stock solution) <br />
|-<br />
| H2O || to 1 liter || <br />
|}<br />
Filter sterilize with a 0.45 µm nalgene filter<br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Stock 0.5 M PIPES (piperazine-1,2-bis[2-ethanesulfonic acid]) pH 6.7<br />
**Dissolve 15.1 g PIPES in 80ml MilliQ H2O<br />
**Adjust pH to 6.7 with 5M KOH<br />
**Bring volume to 100 ml with MilliQ H2O<br />
**Filter sterilize with a 0.45 µm nalgene filter<br />
**Aliquot (5 times) and store at -20°C<br />
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<a id="Colony PCR for Streptomyces"> </a><br />
<h3 class="bg-yellow">Colony PCR for <i>Streptomyces</i></h3><br />
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<b>Pretreatment of ''Streptomyces''</b><br />
<br />
Because of the fact that ''Streptomyces'' are Gram-positive bacteria with a thick peptidoglycan layer, we performed 4 ways to pretreat the cells for colony PCR (all pretreatments gave positive results in the end):<br />
*microwave ''Streptomyces'' for 4 mins<br />
*mix ''Streptomyces'' with water and 0.2% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with 1% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with TE buffer, 0.2% SDS, microwave for 4 mins<br />
<br><br />
<b>PCR mixture</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br />
{| class="wikitable"<br />
| '''Components''' || '''Amount''' <br />
|-<br />
| 2x fusion master mix (add in the end) || 25 µl <br />
|-<br />
| forward primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| reverse primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| template DNA || 1 µl <br />
|-<br />
| DMSO (recommended for high GC content) || 1.5 µl <br />
|-<br />
| H2O (PCR certified, no contamination) || add to final volume of 50µl <br />
|}<br />
'''Keep tubes on ice at all times!''' <br/><br />
'''Be sure to put Phusion Master Mix immediately back at -20!'''<br />
<br />
<b>Cycling instruction</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| 1 || 95°C || 6'<br />
|-<br />
| 2<br/> cycle 29x || 95°C<br/>55°C<br/>72°C || 30"<br/>30"<br/>45"<br/><br />
|-<br />
| 3 || 72°C || 10'<br />
|-<br />
| 4 || 12°C || infinite/hold<br />
|}<br />
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<a id="Digestion and ligation"> </a><br />
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<h3 class="bg-yellow">Digestion and ligation</h3><br />
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<b>Consumables and equipment</b><br />
<br />
* Restriction enzymes (EcoRI, Xbal, Spel, Pstl), NEBuffer 2.1<br />
* 10x T4 DNA ligase Reaction Buffer, T4 DNA Ligase<br />
* Keep all enzymes on ice; make sure buffers have no precipitation<br />
* H20<br />
* Small PCR Tubes or eppendorfs<br />
* 2 µl, 200 µl pipette tips<br />
* Destination plasmid as purified DNA<br />
* Upstream and downstream part as purified DNA<br />
* 2 µl and 20 µl pipette<br />
* Heat block 37° and 80°C<br />
* Timer<br />
* Rack for small PCR tubes<br />
* -20°C freezer + freeze box<br />
<br><br />
<b>Digestion</b><br />
<br />
* Mark PCR tubes or eppendorfs<br />
a. U= upstream part : E + S restriction enzymes<br />
b. D= downstream part : X + P restriction enzymes<br />
c. P= plasmid (destination) : E + P restriction enzymes<br />
d. NB: if only one part for insertion insert I= Insert : E + P restriction enzymes<br />
* In each tube 500 ng DNA for digestion + H20 until total volume is 43 µl<br />
* Add 5 µl of NEBuffer 2.1 to each tube<br />
* Add 1 µl of first restriction enzyme<br />
* Add 1 µl of the second restriction enzyme '''TOTAL VOLUME = 50 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at 37°C for 20 min. (officially 15 min)<br />
* Incubate at 80°C for 20 min.<br />
a. OPTIONAL: run 10-20 µl on 1% agarose gel and look for expected bands as confirmation<br />
b. OPTIONAL: store at -20°C or proceed to ligation immediately<br />
<br><br />
<b>Ligation</b><br />
<br />
* Add 13 µl of H2O to a 200 µl PCR tube or eppendorf<br />
* Add 2 µl of each part you want to ligate<br />
* Add 2 µl of 10X T4 DNA Ligase Reaction Buffer to the tube<br />
* Add 1 µl of the T4 DNA Ligase to the tube '''TOTAL VOLUME = 20 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at room temperature for 10 min<br />
a. Incubate at 80°C for 20 min. <br />
b. Store the ligation mix at -20°C or proceed immediately to the transformation step.<br />
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<a id="DNA extraction from agarose gels"> </a><br />
<h3 class="bg-yellow">DNA extraction from agarose gels</h3><br />
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(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br />
<br><br />
<b>Procedure</b><br />
<br />
#Excise DNA fragment/solubilize gel slice: take a clean scalpel to excise the DNA fragment from an agarose gel, remove all excess agarose. For each '''100mg of agarose gel < 2%''' add '''200µl buffer NTI''', for gels containing > 2% agarose, double the volume of buffer NTI. Incubate sample for '''5-10 min''' at '''50°C''', vortex the sample briefly every 2-3 min until the gel slice is '''completely''' dissolved.<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2ml) and load up to 700µl sample, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''700µl buffer NT3''' to the column, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1min''' at '''11000g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5ml microcentrifuge tube, add '''15-30µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000g'''.<br />
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<a id="GC-MS analysis"></a><br />
<h3 class="bg-yellow">GC-MS analysis</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with or without 0.1 mM salicylic acid and left to grow for overnight. Samples were induced with 0.2 mM IPTG 6 hours post inoculation.<br />
#Cultures were then chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filter sterilized (0.22µm).<br />
#2 ml of this filtersterilized supernatant was then extracted with 1 ml of hexane<br />
#Extractions were done in glass tubes with rigorous vortexing for 10 minutes<br />
#The upper phase was transferred to a new glass vial<br />
#The extraction was repeated twice (total of 3 extractions with 1 ml of hexane)<br />
#The resulting ± 3 ml of extract were then reduced by evaporation under a nitrogen flow and redissolved in 50 µl of hexane.<br />
<br/><br/><br />
<br />
<b>Gas chromatography:</b><br />
GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br/><br/><br />
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<a id="Grow electrocompetent cells"> </a><br />
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<h3 class="bg-yellow">Grow electrocompetent cells</h3><br />
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(source: adapted from openwetware.org) <br />
<br />
<br/><br />
<b>Materials</b><br />
<br />
*GYT (glycerol, yeast extract, tryptone)<br />
**10%(v/v) glycerol <br />
**0.125% (w/v) yeast extract <br />
**0.25% (w/v) tryptone <br />
<br />
*DI water<br />
*10% Glycerol<br />
<br />
<br />
<b>Special Equipment</b><br />
*Centrifuge<br />
*Ice water bath<br />
*Liquid nitrogen<br />
<br />
<br />
<b>Procedure</b><br />
<br />
Important: All steps in this protocol should be carried out aseptically<br />
<br />
*Inoculate: Prepare flask containing 5 ml of LB medium. Pick up a single colony of cells from plate (using a sterile toothpick) and swirl around inside flask. Incubate the culture overnight at 37°C with vigorous aeration (250 pm in a rotary shaker). <br />
<br />
*Dilute and incubate: Inoculate two aliquots of 495 ml of prewarmed LB medium in separate 2-liter flasks with 5 ml of the overnight bacterial culture. Incubate the flasks at 37°C with agitation (300 cycles/min in a rotary shaker). Measure the OD-600 every twenty minutes (this step will take around 1.5-2 hrs). (or judge the OD by eyes to avoid always taking the sample to disturb the growth as well as avoiding the contamination)<br />
<br />
*Rapidly cool culture: Once the OD-600 of the culture reaches 0.6-1.0 (Molecular Cloning recommends 0.4), rapidly transfer the flasks to the pre-made ice-water bath for 15-30 minutes. Swirl the culture occasionally to ensure that cooling occurs evenly. In preparation for the next step, place the centrifuge bottles in the ice-water bath as well. <br />
<br />
Note: After this point, do not let your cells warm up past 4°C '''always keep on ice'''<br />
<br />
Note: When harvesting cells by decanting, be very careful not to disturb the pellet-- this could result in a much lower yield. If necessary, aspirate instead or decant the supernatant. Ask someone to show you how to aspirate. Also, if the pellet seems loose, sometimes it is helpful to re-spin the cells down.<br />
<br />
*Centrifuge 1: Transfer the cultures to ice-cold centrifuge bottles. Harvest the cells by centrifugation at 1000 g (2500 rpm) for 15 minutes at 4°C. Decant the supernantant and resuspend the cell pellet in 20 ml of ice-cold 10% glycerol. Note: this should be done for each of the two 500ml cultures, i.e this is a 1:1 resuspension rather than a concentration by a factor of 2 BC. <br />
<br />
*Centrifuge 2 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 20 ml ice-cold 10% glycerol. <br />
<br />
*Centrifuge 3 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 10 ml ice-cold 10% glycerol.<br />
<br />
*Centrifuge 4 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Carefully decant the supernatant and use a Pasteur pipette attached to a vacuum line to remove any remaining drops of buffer. <br />
<br />
*Resuspend in GYT: Resuspend in 1 ml ice cold GYT. This is best done by gently swirling rather pipetting or vortexing. <br />
<br />
*Test for arcing: Transfer 40 µl of the suspension to an ice-cold electroporation cuvette and test whether arcing occurs when an electrical discharge is applied. Place the cuvette in the holder attached to the machine. Go to option 4, Pre-set protocols; choose bacterial; choose the correct choice for your size cuvette, probably the first option for a .1 cm cuvette. If arcing occurs, wash the remainder of the cell suspension once more with ice-cold GYT medium to ensure that the conductivity of the bacterial suspension is sufficiently low (<5 mEq). (or check the pulse time, if the pulse time < 4, redo the wash, if the pulse time > 4, it's ok)<br />
<br />
*Storage: Store cells at -80°C until they are required for use. For storage, dispense 40 µl aliquots of the cell suspension into sterile, ice-cold .5 ml microcentrifuge tubes, drop into a bath of '''liquid nitrogen''' and transfer to a -80°C freezer. To remove the tubes from the liquid nitrogen bath, bring out into the hall along with a storage box, and pour the tubes and liquid nitrogen into the box. Once all the tubes are out, close the box most of the way and let the liquid run out into the hallway. Try not to do this in the very center of the walkway! <br />
<br />
*To use frozen cells: Remove an appropriate number of aliquots of cells from the -80°C freezer. Thaw the tubes on ice.<br/><br />
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<a id="Headspace GC"></a><br />
<h3 class="bg-yellow">Headspace GC</h3><br />
</div><br />
</div></html><br />
<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with 0 or 0.1 mM of salicylic acid and left to grow for 7 hours.<br />
#Cultures were chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filterstelized (0.22µm).<br />
#Salt was added to 5 ml of this filtersterilized supernatant<br/><br/><br />
<br />
<b>Gass chromatography:</b><br />
Samples were analyzed with a calibrated Autosystem XL gas chromatograph with a headspace sampler (HS40; Perkin-Elmer, Wellesley, Mass.) and equipped with a CP-Wax 52 CB column (length, 50 m; internal diameter, 0.32 mm; layer thickness, 1.2 μm; Chrompack; Varian, Palo Alto, Calif.). Samples were heated for 16 min at 72°C in the headspace autosampler. The injection block and flame ionization detector (FID) temperatures were kept constant at 180 and 250°C, respectively; helium was used as the carrier gas. The oven temperature was 75°C held for 6 min and then increased to 110°C at 25°C min−1 and held at 100°C for 3.5 min. Results were analyzed with Perkin-Elmer Turbochrom Navigator software.<br />
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<a id="Isolation of plasmid DNA from E. coli (mini prep)"> </a><br />
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<h3 class="bg-yellow">Isolation of plasmid DNA from <i> E. coli</i> (mini prep)</h3><br />
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(source: NucleoSpin® plasmid - Macherey-Nagel)<br />
<br />
<br><br />
<b>Nanodrop protocol</b><br />
<br><br><br />
Nanodrop can be used to measure the DNA, RNA and protein <br />
Measure the concentration and purity of extracted DNA using absorbance (using the automated nanodrop machine!) <br />
<br />
Method:<br />
#Log onto computer and select Nanodrop program from the desktop (ND 1000) <br />
#To clean Nanodrop machine wipe pedestal and top and add 3 µl of water to nib of pedestal. Press blank. <br />
#Wipe the water off, to initialize/equalize the equipment add 3 μl of the elution buffer [EB] used in the sample and press blank. Set to DNA-50 for DNA. <br />
#Wipe to remove buffer and apply 3 μl of sample to nib. Press measure. <br />
#If dealing with multiple samples, clean the equipment with water at regular intervals (about every 10 samples). <br />
#After measurements, clean the equipment with 3 μl of water on the spectrometer and press blank. Wipe and log off. <br />
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<h3 class="bg-yellow">PCR clean-up</h3><br />
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(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br/><br />
<br />
This is used for PCR clean-up as well as DNA concentration and removal of salts, enzymes, etc. from enzymatic reactions (SDS<0.1%)<br />
#Adjust DNA binding condition: mix '''1 volume of sample''' with '''2 volumes of buffer NTI''' (eg. mix 100 µl PCR reaction and 200 µl buffer NTI).<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2 ml) and load up to 700 µl sample, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''600µl buffer NT3''' to the column, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1 min''' at '''11000 g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5 ml microcentrifuge tube, add '''50 µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000 g'''.<br />
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<a id="PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer"> </a><br />
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<h3 class="bg-yellow">PCR protocol for Taq DNA polymerase with standard Taq Buffer</h3><br />
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<b>Reaction set up</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br/><br />
'''We recommend assembling all reaction components on ice and quickly transferring the reactions to a thermocycler preheated to the denaturation temperature (95°C).'''<br />
{| class="wikitable"<br />
| '''Components''' || '''25 μl reaction''' || '''50 μl reaction''' || '''Final concentration'''<br />
|-<br />
| 10X Standard Taq Reaction Buffer || 2.5 µl || 5 µl || 1X<br />
|-<br />
| 10 mM dNTPs || 0.5 µl || 1 µl || 200 µM<br />
|-<br />
| 10 µM Forward Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| 10 µM Reverse Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| template DNA || variable || variable || <1,000 ng <br />
|-<br />
| Taq DNA Polymerase || 0.125 µl || 0.25 µl || 1.25 units/50 µl PCR<br />
|-<br />
| Nuclease-free water || to 25 µl || to 50 µl || <br />
|}<br />
<br />
Notes: Gently mix the reaction. Collect all liquid to the bottom of the tube by a quick spin if necessary. Overlay the sample with mineral oil if using a PCR machine without a heated lid.<br />
Transfer PCR tubes from ice to a PCR machine with the block preheated to 95°C and begin thermocycling.<br />
<br />
<b>Thermocyclingconditions for a routine PCR</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| Initial denaturation || 95°C || 30"<br />
|-<br />
| 30 cycles || 95°C<br/>48-65°C<br/>68°C || 15-30"<br/>15-60"<br/>1min/kb<br />
|-<br />
| Final extension || 68°C || 5'<br />
|-<br />
| Hold || 12°C || infinite/hold<br />
|}<br />
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<a id="Plasmid DNA isolation"> </a><br />
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<h3 class="bg-yellow">Plasmid DNA isolation</h3><br />
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<b>Procedure</b><br />
<br />
[https://static.igem.org/mediawiki/2013/8/8f/Risk_assessment_Plasmid_DNA_Purification_kit_KULeuven.pdf Risk assessment for plasmid DNA purification kit]<br />
#Bring '''1.5 ml culture''' in an eppendorf, centrifuge for '''1 min with maximum speed'''<br />
#Pour away the supernatant<br />
#Bring another '''1.5 ml culture''' into the same eppendorf, centrifuge for '''1 min''' and pour away supernatant<br />
#Resuspend the pellet with '''200µl GTE-solution''' we made earlier<br />
#Add '''4 µl RNase A (10mg/ml)'''<br />
#Add '''400 µl premade solution''' (contains 0.2M NaOH and 1%SDS in sterile water)<br />
#Mix them well, place on ice for '''5 min'''<br />
#Add '''300 µl ice cold 7.5 M ammonium acetate''', vortex for 10 s, place on ice for '''5 mins'''<br />
#Centrifuge for '''5min with 13000 rpm'''<br />
#Bring the supernatant into a new eppendorf<br />
#Centrifuge this supernatant for a second time ('''5 min, 13000 rpm''') and bring the supernatant in a new eppendorf<br />
#Add isopropanol to the supernatant (60% in volume of the supernatant), leave '''at room temp. for 5 min'''<br />
#Centrifuge for '''10 min with 13000 rpm''', immediately remove the supernatant, keep the transparent pellet in the tube, put the tube upside down on a tissue to dry it<br />
#Add '''1 ml of cold 70% ethanol''' to the pellet, invert 5 times<br />
#Centrifuge '''3 min with 13000 rpm'''<br />
#Remove supernatant, the droplet on the tube wall can be removed by tissue<br />
#Let the pellet dry<br />
#Add '''50 µl elution buffer''' (or sterile water) to the pellet<br />
<br />
<b>Buffers and Solutions</b><br />
<br />
*GTE-buffer<br />
**50 mM glucose<br />
**25 mM Tris-Cl (pH 8.0)<br />
**10 mM EDTA<br />
**4 mg/ml lysozyme<br />
<br />
*IPTG stock solution<br />
**238 mg in 10 ml AD<br />
**Filter sterilize<br />
**Split into 1 ml aliquots<br />
**Store in -20 freezer<br />
<br />
Final concentration/work concentration in agar plates = 0.1mM – 1 mM <br/><br />
Sigma recommends 0.2 mM for blue-white screening <br/><br />
Thermo Scientific recommends 0.1 mM<br />
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<a id="Predator attraction to MeS induced aphid infested plants"></a><br />
<h3 class="bg-yellow">Predator attraction to MeS induced aphid infested plants</h3><br />
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<b>Aim</b>: To determine the effect on aphid predators’ attraction to Methyl Salicylate (MeS) induced plants.<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Macrolophus</i> adults<br />
*Plants: Small potted MeS induced paprika plants in two ways: via the root or via the leaf, 60 in total. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)). <br />
<br><br />
<b>Cafetaria model</b><br />
<br />
[[File:Cafetariamodel.png|100px|Leafinduction]] [[File:Cafetariamodel1.png|200px|Leafinduction]]<br />
<br />
<br/><br />
In the cafeteria model shown above, each concentration will be placed, plus the control, randomly in a circle. The plants should not be close to the edge of the cage in which the experiments are carried out. The plants should be placed in rotation with every repeat of the set-up to eliminate other factors. <br />
*Releasing the <i>Macrolophus</i>:<br />
#10 <i>Macrolophus</i> (adult) are shaken out of the pot and placed in the middle of the cafetaria model (see picture) <br />
#We release around 50 <i>Macrolophus</i> (adult) per set-up<br />
[[File:Macrolophus2.png|200px|Leafinduction]]<br />
*Counting the <i>Macrolophus</i>: <br />
# After every 45 min from the moment we released the <i>Macrolophus</i>, the amount per plant is counted and recorded<br />
# The <i>Macrolophus</i> are then shaken off the plant back into the middle of the circle so that they can make their choice again<br />
# We will take 3 recordings<br />
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<h3 class="bg-yellow">Predator attraction to methyl salicylate</h3><br />
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<b>Aim</b>: To determine a working concentration of methyl salicylate (MeS) concentration which attracts predators .<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Adalia bipunctata</i> (ladybugs): adult and larvae <br />
*Concentration: 1000ng/ml, 100ng/ml, 10ng/ml, 1ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 96% ethanol. <br />
<br><br />
<b>Biobest setup</b><br />
<br />
[[File:Biobestsetup.png|200px|Biobeststetup]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure EtOH). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
<br />
<b>pcfruit setup</b><br />
<br />
[[File:Y-tubeolfactormeter.png|200px|Leafinduction]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure hexane or paraffin oil). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
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<h3 class="bg-yellow">Protein Extraction</h3><br />
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<br/><b>Aim</b>: To extract proteins from our <i>E. coli</i> for SDS-PAGE analysis.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#E.coli cells transformed with the indicated plasmids were grown either to mid-exponential phase (OD600nm ~ 1.0 for endpoint assays) or to the indicated optical densities.<br/><br />
#Samples were taken and spun down (3min, 4000 rpm, 4°C).<br/><br />
#Growth medium was removed and cell pellets frozen (-80 °C)<br/><br />
#Cell pellets were thawed on ice and resupended in equal volumes extraction buffer.<br/><br />
#Suspensions were incubated on ice for 10min and subsequently sonicated (3X 10” pulses) with ice cooling in between.<br/><br />
#Suspensions were spun down (10min, 14000 rpm, 4°C).<br/><br />
#Aliquots were mixed with 5X sample buffer and boiled (5min 95°C).<br/><br />
#Samples were loaded on precast SDS-PAGE systems (Biorad) according to the manufacturer's instructions.<br />
<br/><br />
<b>Extraction buffer </b>:<br/><br />
: 25mM Tris pH 8.0; 0.1%(v/v) NP40; 5 mM EDTA; 50 mM NaCl; protease inhibitor cocktail (Benzamidine; PMSF; Leupeptin).<br />
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<a id="qRT-PCR Protocol"></a><br />
<h3 class="bg-yellow">qRT-PCR protocol</h3><br />
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General experiment: 3 biological repeats of stationary phase cultures 16 hrs growth.<br />
<br />
<b>Sample preparation</b><br />
<br />
# Inoculate fresh LB plate with the strain harbouring the MS brick <br />
# Isolate plasmid DNA, MS brick<br />
# Nanodrop plasmid (minimum concentration 20 ng/µl) = 155 ng/µl <br />
# Design primers for pcha/pchb and for bsmt1 using Primer express. Primer sequence can be found in document “Genes_qPCR_primers_IC.docx”.<br />
# Inoculate E. Coli with Methylsalicylate brick (MIT 2006) on LB plate.<br />
# Add 5 µl of Kanamicin (50 mg/ml) and 10 µl of IPTG (100 mM) to 5 ml of LB medium (tubes).<br />
# Incubate 16h at 37 °C under shaking conditions.<br />
# Measure OD of 3 cultures.<br />
# Account for differences in OD: ex. If you have an OD of 1 take 1 ml of this sample, if you have an OD of 0,8 take 1,2 ml.<br />
# Add 1/5 volume of stopsolution (95% ethanol, 5% phenol).<br />
# Freeze 5 minutes in liquid Nitrogen.<br />
# Centrifuge needed amount of cells.<br />
<br />
<b>mRNA isolation</b><br />
<br />
For mRNA isolation use the Promega SV total RNA isolation kit ([http://be.promega.com/resources/protocols/technical-manuals/0/sv-total-rna-isolation-system-protocol/?origUrl=http%3a%2f%2fwww.promega.com%2fresources%2fprotocols%2ftechnical-manuals%2f0%2fsv-total-rna-isolation-system-protocol%2f Promega SV total RNA isolation protocol])<br />
# Section 8.C (Isolation of RNA from Gram-positive and Gram-negative bacteria)<br />
# Section 4.E (RNA Purification by Centrifugation) We did not do step 4 and 5 (addition of DNase) but instead used the DNase protocol of [http://products.invitrogen.com/ivgn/product/AM2238 Ambion Turbo DNase]<br />
For 100 µl of RNA:<br />
## Add 10 µl 10x buffer. Add 1 µl Turbo DNase and mix gently.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 1 µl of Turbo DNase.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 20 µl of DNase inactivation reagent and mix well.<br />
## Put on room temperature for 5 min. Mix occasionally by flicking the tube<br />
## Centrifuge on 10000 g for 2 min.<br />
## Transfer the RNA to a new tube.<br />
<br />
<b>Create cDNA</b><br />
<br />
Use the Fermentas Revert aid H kit ([http://www.thermoscientificbio.com/reverse-transcription-rtpcr-rtqpcr/revertaid-h-minus-first-strand-cdna-synthesis-kit/ Fermentas Revert aid H protocol])<br />
The protocol for RT-PCR (I. First Strand Synthesis) was followed<br />
<br />
<b>qPCR</b><br />
<br />
qPCR kit was not yet decided.<br />
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<a id="Root measurements"></a><br />
<h3 class="bg-yellow">Root measurements</h3><br />
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<b>Aim</b>: To determine whether the different induction methods or the Methyl Salicylate (MeS) had an effect on plant growth.<br />
<br/><br/><br />
Experimental setup: We measured the roots of the same plants used in the experiments to determine aphid population and predator preference (see above). <br />
<br/><br />
[[File:Uppot.png|200px|Uppot]]<br />
[[File:Wash.png|200px|Root wash]]<br />
[[File:Dryandweigh.png|200px|Dry roots]]<br />
<br />
<br/><br />
The plants were cut off at the bottom of the stem and removed from the pot. <br />
<br/>After most of the dirt has been removed, they were carefully washed to remove the rest of the dirt.<br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>The roots were then dried and weighed. <br />
<br><br />
[[File:Rootmeasurement.png|200px|Root measurement]]<br />
[[File:Rootmeasurement1.png|200px|Root measurement 1]]<br />
<br/>The roots were then measured and recorded.<br />
<br />
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<br/><br/></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/ProtocolsTeam:KU Leuven/Protocols2013-10-29T01:28:24Z<p>Veerledewever: </p>
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<h3>Index</h3><br />
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<a href="#Aphid population preference">Aphid population preference</a><br><br />
<a href="#Aphid mobility experiment">Aphid mobility experiment</a><br><br />
<a href="#Chemically competent E.coli cells: CaCl2 method">Chemically competent E.coli cells: CaCl2 method</a><br><br />
<a href="#Chemically competent E.coli cells: Inoue method">Chemically competent E.coli cells: Inoue method</a><br><br />
<a href="#Colony PCR for Streptomyces">Colony PCR for Streptomyces</a><br><br />
<a href="#Digestion and ligation">Digestion and ligation</a><br><br />
<a href="#DNA extraction from agarose gels">DNA extraction from agarose gels</a><br><br />
<a href="#Grow electrocompetent cells">Grow electrocompetent cells</a><br><br />
<a href="#Isolation of plasmid DNA from E. coli (mini prep)">Isolation of plasmid DNA from E. coli (mini prep)</a><br><br />
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<div class="span6 greytext"><br />
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<a href="#PCR clean-up">PCR clean-up</a><br><br />
<a href="#PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer">PCR Protocol for Taq DNA Polymerase</a><br><br />
<a href="#Plasmid DNA isolation">Plasmid DNA isolation</a><br><br />
<a href="#Predator attraction to MeS induced aphid infested plants">Predator attraction to MeS induced aphid infested plants</a><br><br />
<a href="#Predator attraction to Methyl Salicylate">Predator attraction to Methyl Salicylate</a><br><br />
<a href="#Protein Extraction">Protein Extraction</a><br><br />
<a href="#qRT-PCR Protocol">qRT-PCR Protocol</a><br><br />
<a href="#Root measurements">Root measurements</a><br><br />
<a href="#Headspace GC">Headspace GC</a><br><br />
<a href="#GC-MS analysis">GC-MS analysis</a><br><br />
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<a id="Aphid population preference"> </a><br />
<h3 class="bg-yellow">Aphid population preference</h3><br />
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<b>Aim</b>: To determine the effect on aphid population on Methyl Salicylate (MeS) induced plants.<br />
<br/> <br/><br />
Experimental setup: We induced five plants per concentration<br />
*Plants: Small potted paprika plants in two ways: via the root or byspraying; 60 in total. Paprika plants don’t make MeS naturally but do produce salicylic acid. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml ,0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 97% pure ethanol or water. <br />
<br><br />
<b>Procedure</b><br />
<br />
Induction requires 48h<br />
*Induction via the roots<br />
#Uproot the plant, clean off the dirt with water because compost can interfere with the uptake of MeS<br />
#Wipe off the water before placing the roots in a cup with the desired concentration for 10 minutes. MeSA was first suspended in ethanol before being further diluted to the desired concentration in 50ml water<br />
#Re-pot the plant and divide the remainder of the MeSA solution amongst the 5 plants <br />
[[File:Rootinduction.png|200px|Root induction]]<br />
<br />
*Induction via the leaf<br />
#The desired concentrations were diluted in 15ml ethanol, resulting in 3ml per plant. Ethanol has been shown to have no plant induction properties<br />
#Each leaf of the plant is sprayed above and underneath<br />
[[File:Leafinduction.png|200px|Leaf induction]]<br />
<br/><br />
*Place aphids on MeS induced plants 48h post-induction<br />
#Place 15 green peach aphids on the head of each induced plant<br />
#Take the smallest aphids present on the aphid-infested leaf. To be sure that it is a first generation aphid<br />
#Place each plant in a separate net<br />
#Plants of the same concentration, induced by spraying or via the roots, are placed in the same row; 10 plants per row. The rows are roughly 1 metre apart<br />
* Counting aphids on day 7<br />
#Following a form, count how many aphids are on Cotyledon, separate true leafs and the head of the induced plants.<br />
#See <a href"> results</a><br />
#There is a possibility of contamination while placing the aphids on the plant, meaning that older aphids and/or flying aphids crawled onto the plant by accident. These were removed from the plant so that the amount of aphids on day 10 would not be tainted<br />
<br />
[[File:Howtocountaphids.png|300px|How to count aphids]]<br />
<br />
* Counting aphids on day 10<br />
#We count the aphids for a second time to allow the second generation to develop. This way we investigate whether MeS has an effect on the aphid’s behaviour. That they are encouraged to leave the plant, reproduce less or generate mobile (flying) aphids. <br />
#The head of the plant is where the majority of the secondary metabolites gather; hence we expect to see an effect on the distribution of the aphid population.<br />
<br/><br />
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<a id="Aphid mobility experiment"> </a><br />
<h3 class="bg-yellow">Aphid mobility experiment</h3><br />
</div><br />
</div></html><br />
<br/><b>Aim</b>: To determine the effect of EBF on aphid mobility.<br />
<br/> <br/><br />
<b>Experimental setup</b>: Leaves infested with aphids were divided in two glass containers (not sealed) randomly (n=3; 3 leaves per group). After 1h plates with bacteria, either control (BL21) or BL21 with EBF construct, were put under each leaf.<br />
<br />
<br><br />
<b>Measurement</b>: Aphids that were on the top of each leaf were counted at start and at each time point during the experiment.<br/><br />
At each time point the amount of aphids moving on the leaf were also counted. The time points used were 0, 50, 150 and 200 minutes.<br />
<br />
<br />
<br />
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<a id="Chemically competent E.coli cells: CaCl2 method"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: CaCl2 method</h3><br />
</div><br />
</div></html><br />
<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells!''' <br\><br />
'''Do not shock freeze (liqN2) – just transfer from ice to -80°C!''' <br\><br />
'''Work sterile!'''<br />
<br />
#Inoculate '''3 ml''' growth medium with your cells of choice ('''DH5alpha''' or '''TOP10''' for plasmid maintenance & cloning)<br />
#Grow overnight at '''37°C''' with sufficient aeration<br />
#Inoculate '''100 ml LB''' with '''1 ml''' of overnight culture<br />
#Grow at '''37°C''' to an OD 600nm of approx '''0.5 to 0.8''' (usually '''2-3 hrs''')<br />
#Centrifuge cells ('''3700-4000 rpm 4°C 12 min''' – sterile 50ml tube)<br />
#Resuspend pellet on ice with FSB to '''15 ml''' (cold) for each '''100 ml''' pellet<br />
#Incubate cells '''10 min''' on ice<br />
#Centrifuge cells ('''3700 – 4000 rpm 4°C 10 min''')<br />
#Re-suspend pellet on ice in '''4-8 ml''' FSB (cold) for each '''100 ml pellet'''<br />
#Aliquot cells appropriately ('''200-400 µl aliquots''') and freeze aliquots at '''-80°C'''<br />
<br><br />
<b>Buffers and solutions</b><br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Growth medium<br />
**LB 25 g/l<br />
*Frozen Storage Buffer (FSB)<br />
**10 mM Potassium Acetate<br />
**10% glycerol<br />
**10 mM KCl<br />
**50 mM CaCl2<br />
**Check pH – must be around 6.2 – if need be adjust with AcAc (HCl) or KOH<br />
**Buffer should be filter-sterilized (0.45 micrometer filter)<br />
<br />
<br/><br />
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<a id="Chemically competent E.coli cells: Inoue method"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: Inoue method</h3><br />
</div><br />
</div><br />
</html><br />
<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells.'''<br/><br />
'''Work sterile'''<br/><br />
#Pick a single colony from a freshly transformed plate (after overnight growth '''at 37 °C''')<br />
#Transfer the colony to '''25 ml growth medium''' in a sterile '''250 ml''' erlenmeyer<br />
#Incubate the culture '''at 37°C''' for '''6 – 8 hrs''' under vigorous shaking ('''250 – 300 rpm''')<br />
#Prepare '''3 1L flasks''' with '''250 ml growth medium''' in each<br />
#Inoculate the flasks with 10, 4 or 2 ml of the dayculture -> you create 3 different starting optical densities.<br />
#Incubate the cultures at '''18-22°C overnight''' under moderate shaking ('''180 – 220 rpm''')<br />
#Monitor the '''OD600nm''' until it reaches '''0.55'''<br />
#Place cells in an ice-water bath to cool them down quickly (-> swirl occasionally, keep them in for approx 10 min) <br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''80 ml icecold inoue transformation buffer'''<br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''20 ml icecold inoue transformation buffer'''<br />
#Add '''1.5 ml 100% DMSO''' – mix by swirling<br />
#Store whole on ice for approx '''10 minutes'''<br />
#Aliquot as quickly as possible '''100 – 200 µl aliquots''' into '''1.5 ml tubes''' (precooled on ice) and snapfreeze them into a liquid N2 bath<br />
<br />
<b>Buffers and solutions</b><br />
<br />
*Growth medium<br />
*Inoue transformation buffer<br />
{| class="wikitable"<br />
| '''Reagent''' || '''Final concentration (mM)''' || '''Amount per liter''' <br />
|-<br />
| MnCl2 || 55 || 10.88 g (from MnCl2*4H2O) <br />
|-<br />
| CaCl2 || 15 || 2.20 g (from CaCl2*2H2O) <br />
|-<br />
| KCl || 250 || 18.65 g (from KCl) <br />
|-<br />
| PIPES || 10 || 20 ml (from 0.5M stock solution) <br />
|-<br />
| H2O || to 1 liter || <br />
|}<br />
Filter sterilize with a 0.45 µm nalgene filter<br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Stock 0.5 M PIPES (piperazine-1,2-bis[2-ethanesulfonic acid]) pH 6.7<br />
**Dissolve 15.1 g PIPES in 80ml MilliQ H2O<br />
**Adjust pH to 6.7 with 5M KOH<br />
**Bring volume to 100 ml with MilliQ H2O<br />
**Filter sterilize with a 0.45 µm nalgene filter<br />
**Aliquot (5 times) and store at -20°C<br />
<br />
<br/><br />
<br />
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<div class="span12"><br />
<a id="Colony PCR for Streptomyces"> </a><br />
<h3 class="bg-yellow">Colony PCR for <i>Streptomyces</i></h3><br />
</div><br />
</div><br />
</html><br />
<br />
<b>Pretreatment of ''Streptomyces''</b><br />
<br />
Because of the fact that ''Streptomyces'' are Gram-positive bacteria with a thick peptidoglycan layer, we performed 4 ways to pretreat the cells for colony PCR (all pretreatments gave positive results in the end):<br />
*microwave ''Streptomyces'' for 4 mins<br />
*mix ''Streptomyces'' with water and 0.2% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with 1% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with TE buffer, 0.2% SDS, microwave for 4 mins<br />
<br><br />
<b>PCR mixture</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br />
{| class="wikitable"<br />
| '''Components''' || '''Amount''' <br />
|-<br />
| 2x fusion master mix (add in the end) || 25 µl <br />
|-<br />
| forward primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| reverse primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| template DNA || 1 µl <br />
|-<br />
| DMSO (recommended for high GC content) || 1.5 µl <br />
|-<br />
| H2O (PCR certified, no contamination) || add to final volume of 50µl <br />
|}<br />
'''Keep tubes on ice at all times!''' <br/><br />
'''Be sure to put Phusion Master Mix immediately back at -20!'''<br />
<br />
<b>Cycling instruction</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| 1 || 95°C || 6'<br />
|-<br />
| 2<br/> cycle 29x || 95°C<br/>55°C<br/>72°C || 30"<br/>30"<br/>45"<br/><br />
|-<br />
| 3 || 72°C || 10'<br />
|-<br />
| 4 || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<a id="Digestion and ligation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Digestion and ligation</h3><br />
</div><br />
</div></html><br />
<b>Consumables and equipment</b><br />
<br />
* Restriction enzymes (EcoRI, Xbal, Spel, Pstl), NEBuffer 2.1<br />
* 10x T4 DNA ligase Reaction Buffer, T4 DNA Ligase<br />
* Keep all enzymes on ice; make sure buffers have no precipitation<br />
* H20<br />
* Small PCR Tubes or eppendorfs<br />
* 2 µl, 200 µl pipette tips<br />
* Destination plasmid as purified DNA<br />
* Upstream and downstream part as purified DNA<br />
* 2 µl and 20 µl pipette<br />
* Heat block 37° and 80°C<br />
* Timer<br />
* Rack for small PCR tubes<br />
* -20°C freezer + freeze box<br />
<br><br />
<b>Digestion</b><br />
<br />
* Mark PCR tubes or eppendorfs<br />
a. U= upstream part : E + S restriction enzymes<br />
b. D= downstream part : X + P restriction enzymes<br />
c. P= plasmid (destination) : E + P restriction enzymes<br />
d. NB: if only one part for insertion insert I= Insert : E + P restriction enzymes<br />
* In each tube 500 ng DNA for digestion + H20 until total volume is 43 µl<br />
* Add 5 µl of NEBuffer 2.1 to each tube<br />
* Add 1 µl of first restriction enzyme<br />
* Add 1 µl of the second restriction enzyme '''TOTAL VOLUME = 50 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at 37°C for 20 min. (officially 15 min)<br />
* Incubate at 80°C for 20 min.<br />
a. OPTIONAL: run 10-20 µl on 1% agarose gel and look for expected bands as confirmation<br />
b. OPTIONAL: store at -20°C or proceed to ligation immediately<br />
<br><br />
<b>Ligation</b><br />
<br />
* Add 13 µl of H2O to a 200 µl PCR tube or eppendorf<br />
* Add 2 µl of each part you want to ligate<br />
* Add 2 µl of 10X T4 DNA Ligase Reaction Buffer to the tube<br />
* Add 1 µl of the T4 DNA Ligase to the tube '''TOTAL VOLUME = 20 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at room temperature for 10 min<br />
a. Incubate at 80°C for 20 min. <br />
b. Store the ligation mix at -20°C or proceed immediately to the transformation step.<br />
<br />
<br/><br />
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<div class="span12"><br />
<a id="DNA extraction from agarose gels"> </a><br />
<h3 class="bg-yellow">DNA extraction from agarose gels</h3><br />
</div><br />
</div></html><br />
(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br />
<br><br />
<b>Procedure</b><br />
<br />
#Excise DNA fragment/solubilize gel slice: take a clean scalpel to excise the DNA fragment from an agarose gel, remove all excess agarose. For each '''100mg of agarose gel < 2%''' add '''200µl buffer NTI''', for gels containing > 2% agarose, double the volume of buffer NTI. Incubate sample for '''5-10 min''' at '''50°C''', vortex the sample briefly every 2-3 min until the gel slice is '''completely''' dissolved.<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2ml) and load up to 700µl sample, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''700µl buffer NT3''' to the column, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1min''' at '''11000g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5ml microcentrifuge tube, add '''15-30µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000g'''.<br />
<br />
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<a id="Grow electrocompetent cells"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Grow electrocompetent cells</h3><br />
</div><br />
</div></html><br />
<br />
(source: adapted from openwetware.org) <br />
<br />
<br/><br />
<b>Materials</b><br />
<br />
*GYT (glycerol, yeast extract, tryptone)<br />
**10%(v/v) glycerol <br />
**0.125% (w/v) yeast extract <br />
**0.25% (w/v) tryptone <br />
<br />
*DI water<br />
*10% Glycerol<br />
<br />
<br />
<b>Special Equipment</b><br />
*Centrifuge<br />
*Ice water bath<br />
*Liquid nitrogen<br />
<br />
<br />
<b>Procedure</b><br />
<br />
Important: All steps in this protocol should be carried out aseptically<br />
<br />
*Inoculate: Prepare flask containing 5 ml of LB medium. Pick up a single colony of cells from plate (using a sterile toothpick) and swirl around inside flask. Incubate the culture overnight at 37°C with vigorous aeration (250 pm in a rotary shaker). <br />
<br />
*Dilute and incubate: Inoculate two aliquots of 495 ml of prewarmed LB medium in separate 2-liter flasks with 5 ml of the overnight bacterial culture. Incubate the flasks at 37°C with agitation (300 cycles/min in a rotary shaker). Measure the OD-600 every twenty minutes (this step will take around 1.5-2 hrs). (or judge the OD by eyes to avoid always taking the sample to disturb the growth as well as avoiding the contamination)<br />
<br />
*Rapidly cool culture: Once the OD-600 of the culture reaches 0.6-1.0 (Molecular Cloning recommends 0.4), rapidly transfer the flasks to the pre-made ice-water bath for 15-30 minutes. Swirl the culture occasionally to ensure that cooling occurs evenly. In preparation for the next step, place the centrifuge bottles in the ice-water bath as well. <br />
<br />
Note: After this point, do not let your cells warm up past 4°C '''always keep on ice'''<br />
<br />
Note: When harvesting cells by decanting, be very careful not to disturb the pellet-- this could result in a much lower yield. If necessary, aspirate instead or decant the supernatant. Ask someone to show you how to aspirate. Also, if the pellet seems loose, sometimes it is helpful to re-spin the cells down.<br />
<br />
*Centrifuge 1: Transfer the cultures to ice-cold centrifuge bottles. Harvest the cells by centrifugation at 1000 g (2500 rpm) for 15 minutes at 4°C. Decant the supernantant and resuspend the cell pellet in 20 ml of ice-cold 10% glycerol. Note: this should be done for each of the two 500ml cultures, i.e this is a 1:1 resuspension rather than a concentration by a factor of 2 BC. <br />
<br />
*Centrifuge 2 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 20 ml ice-cold 10% glycerol. <br />
<br />
*Centrifuge 3 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 10 ml ice-cold 10% glycerol.<br />
<br />
*Centrifuge 4 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Carefully decant the supernatant and use a Pasteur pipette attached to a vacuum line to remove any remaining drops of buffer. <br />
<br />
*Resuspend in GYT: Resuspend in 1 ml ice cold GYT. This is best done by gently swirling rather pipetting or vortexing. <br />
<br />
*Test for arcing: Transfer 40 µl of the suspension to an ice-cold electroporation cuvette and test whether arcing occurs when an electrical discharge is applied. Place the cuvette in the holder attached to the machine. Go to option 4, Pre-set protocols; choose bacterial; choose the correct choice for your size cuvette, probably the first option for a .1 cm cuvette. If arcing occurs, wash the remainder of the cell suspension once more with ice-cold GYT medium to ensure that the conductivity of the bacterial suspension is sufficiently low (<5 mEq). (or check the pulse time, if the pulse time < 4, redo the wash, if the pulse time > 4, it's ok)<br />
<br />
*Storage: Store cells at -80°C until they are required for use. For storage, dispense 40 µl aliquots of the cell suspension into sterile, ice-cold .5 ml microcentrifuge tubes, drop into a bath of '''liquid nitrogen''' and transfer to a -80°C freezer. To remove the tubes from the liquid nitrogen bath, bring out into the hall along with a storage box, and pour the tubes and liquid nitrogen into the box. Once all the tubes are out, close the box most of the way and let the liquid run out into the hallway. Try not to do this in the very center of the walkway! <br />
<br />
*To use frozen cells: Remove an appropriate number of aliquots of cells from the -80°C freezer. Thaw the tubes on ice.<br />
<br />
<br />
<br/><br />
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<a id="Isolation of plasmid DNA from E. coli (mini prep)"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Isolation of plasmid DNA from <i> E. coli</i> (mini prep)</h3><br />
</div><br />
</div></html><br />
<br />
(source: NucleoSpin® plasmid - Macherey-Nagel)<br />
<br />
<br><br />
<b>Nanodrop protocol</b><br />
<br><br><br />
Nanodrop can be used to measure the DNA, RNA and protein <br />
Measure the concentration and purity of extracted DNA using absorbance (using the automated nanodrop machine!) <br />
<br />
Method:<br />
#Log onto computer and select Nanodrop program from the desktop (ND 1000) <br />
#To clean Nanodrop machine wipe pedestal and top and add 3 µl of water to nib of pedestal. Press blank. <br />
#Wipe the water off, to initialize/equalize the equipment add 3 μl of the elution buffer [EB] used in the sample and press blank. Set to DNA-50 for DNA. <br />
#Wipe to remove buffer and apply 3 μl of sample to nib. Press measure. <br />
#If dealing with multiple samples, clean the equipment with water at regular intervals (about every 10 samples). <br />
#After measurements, clean the equipment with 3 μl of water on the spectrometer and press blank. Wipe and log off. <br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="PCR clean-up"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR clean-up</h3><br />
</div><br />
</div></html><br />
(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br/><br />
<br />
This is used for PCR clean-up as well as DNA concentration and removal of salts, enzymes, etc. from enzymatic reactions (SDS<0.1%)<br />
#Adjust DNA binding condition: mix '''1 volume of sample''' with '''2 volumes of buffer NTI''' (eg. mix 100 µl PCR reaction and 200 µl buffer NTI).<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2 ml) and load up to 700 µl sample, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''600µl buffer NT3''' to the column, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1 min''' at '''11000 g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5 ml microcentrifuge tube, add '''50 µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000 g'''.<br />
<br />
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<div id="header" class="row-fluid"><br />
<a id="PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR protocol for Taq DNA polymerase with standard Taq Buffer</h3><br />
</div><br />
</div></html><br />
<br />
<b>Reaction set up</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br/><br />
'''We recommend assembling all reaction components on ice and quickly transferring the reactions to a thermocycler preheated to the denaturation temperature (95°C).'''<br />
{| class="wikitable"<br />
| '''Components''' || '''25 μl reaction''' || '''50 μl reaction''' || '''Final concentration'''<br />
|-<br />
| 10X Standard Taq Reaction Buffer || 2.5 µl || 5 µl || 1X<br />
|-<br />
| 10 mM dNTPs || 0.5 µl || 1 µl || 200 µM<br />
|-<br />
| 10 µM Forward Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| 10 µM Reverse Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| template DNA || variable || variable || <1,000 ng <br />
|-<br />
| Taq DNA Polymerase || 0.125 µl || 0.25 µl || 1.25 units/50 µl PCR<br />
|-<br />
| Nuclease-free water || to 25 µl || to 50 µl || <br />
|}<br />
<br />
Notes: Gently mix the reaction. Collect all liquid to the bottom of the tube by a quick spin if necessary. Overlay the sample with mineral oil if using a PCR machine without a heated lid.<br />
Transfer PCR tubes from ice to a PCR machine with the block preheated to 95°C and begin thermocycling.<br />
<br />
<b>Thermocyclingconditions for a routine PCR</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| Initial denaturation || 95°C || 30"<br />
|-<br />
| 30 cycles || 95°C<br/>48-65°C<br/>68°C || 15-30"<br/>15-60"<br/>1min/kb<br />
|-<br />
| Final extension || 68°C || 5'<br />
|-<br />
| Hold || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="Plasmid DNA isolation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Plasmid DNA isolation</h3><br />
</div><br />
</div></html><br />
<br />
<b>Procedure</b><br />
<br />
[https://static.igem.org/mediawiki/2013/8/8f/Risk_assessment_Plasmid_DNA_Purification_kit_KULeuven.pdf Risk assessment for plasmid DNA purification kit]<br />
#Bring '''1.5 ml culture''' in an eppendorf, centrifuge for '''1 min with maximum speed'''<br />
#Pour away the supernatant<br />
#Bring another '''1.5 ml culture''' into the same eppendorf, centrifuge for '''1 min''' and pour away supernatant<br />
#Resuspend the pellet with '''200µl GTE-solution''' we made earlier<br />
#Add '''4 µl RNase A (10mg/ml)'''<br />
#Add '''400 µl premade solution''' (contains 0.2M NaOH and 1%SDS in sterile water)<br />
#Mix them well, place on ice for '''5 min'''<br />
#Add '''300 µl ice cold 7.5 M ammonium acetate''', vortex for 10 s, place on ice for '''5 mins'''<br />
#Centrifuge for '''5min with 13000 rpm'''<br />
#Bring the supernatant into a new eppendorf<br />
#Centrifuge this supernatant for a second time ('''5 min, 13000 rpm''') and bring the supernatant in a new eppendorf<br />
#Add isopropanol to the supernatant (60% in volume of the supernatant), leave '''at room temp. for 5 min'''<br />
#Centrifuge for '''10 min with 13000 rpm''', immediately remove the supernatant, keep the transparent pellet in the tube, put the tube upside down on a tissue to dry it<br />
#Add '''1 ml of cold 70% ethanol''' to the pellet, invert 5 times<br />
#Centrifuge '''3 min with 13000 rpm'''<br />
#Remove supernatant, the droplet on the tube wall can be removed by tissue<br />
#Let the pellet dry<br />
#Add '''50 µl elution buffer''' (or sterile water) to the pellet<br />
<br />
<b>Buffers and Solutions</b><br />
<br />
*GTE-buffer<br />
**50 mM glucose<br />
**25 mM Tris-Cl (pH 8.0)<br />
**10 mM EDTA<br />
**4 mg/ml lysozyme<br />
<br />
*IPTG stock solution<br />
**238 mg in 10 ml AD<br />
**Filter sterilize<br />
**Split into 1 ml aliquots<br />
**Store in -20 freezer<br />
<br />
Final concentration/work concentration in agar plates = 0.1mM – 1 mM <br/><br />
Sigma recommends 0.2 mM for blue-white screening <br/><br />
Thermo Scientific recommends 0.1 mM<br />
<br />
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<a id="Predator attraction to MeS induced aphid infested plants"></a><br />
<h3 class="bg-yellow">Predator attraction to MeS induced aphid infested plants</h3><br />
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<b>Aim</b>: To determine the effect on aphid predators’ attraction to Methyl Salicylate (MeS) induced plants.<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Macrolophus</i> adults<br />
*Plants: Small potted MeS induced paprika plants in two ways: via the root or via the leaf, 60 in total. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)). <br />
<br><br />
<b>Cafetaria model</b><br />
<br />
[[File:Cafetariamodel.png|100px|Leafinduction]] [[File:Cafetariamodel1.png|200px|Leafinduction]]<br />
<br />
<br/><br />
In the cafeteria model shown above, each concentration will be placed, plus the control, randomly in a circle. The plants should not be close to the edge of the cage in which the experiments are carried out. The plants should be placed in rotation with every repeat of the set-up to eliminate other factors. <br />
*Releasing the <i>Macrolophus</i>:<br />
#10 <i>Macrolophus</i> (adult) are shaken out of the pot and placed in the middle of the cafetaria model (see picture) <br />
#We release around 50 <i>Macrolophus</i> (adult) per set-up<br />
[[File:Macrolophus2.png|200px|Leafinduction]]<br />
*Counting the <i>Macrolophus</i>: <br />
# After every 45 min from the moment we released the <i>Macrolophus</i>, the amount per plant is counted and recorded<br />
# The <i>Macrolophus</i> are then shaken off the plant back into the middle of the circle so that they can make their choice again<br />
# We will take 3 recordings<br />
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<h3 class="bg-yellow">Predator attraction to methyl salicylate</h3><br />
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<b>Aim</b>: To determine a working concentration of methyl salicylate (MeS) concentration which attracts predators .<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Adalia bipunctata</i> (ladybugs): adult and larvae <br />
*Concentration: 1000ng/ml, 100ng/ml, 10ng/ml, 1ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 96% ethanol. <br />
<br><br />
<b>Biobest setup</b><br />
<br />
[[File:Biobestsetup.png|200px|Biobeststetup]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure EtOH). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
<br />
<b>pcfruit setup</b><br />
<br />
[[File:Y-tubeolfactormeter.png|200px|Leafinduction]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure hexane or paraffin oil). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
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<a id="qRT-PCR Protocol"></a><br />
<h3 class="bg-yellow">qRT-PCR protocol</h3><br />
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General experiment: 3 biological repeats of stationary phase cultures 16 hrs growth.<br />
<br />
<b>Sample preparation</b><br />
<br />
# Inoculate fresh LB plate with the strain harbouring the MS brick <br />
# Isolate plasmid DNA, MS brick<br />
# Nanodrop plasmid (minimum concentration 20 ng/µl) = 155 ng/µl <br />
# Design primers for pcha/pchb and for bsmt1 using Primer express. Primer sequence can be found in document “Genes_qPCR_primers_IC.docx”.<br />
# Inoculate E. Coli with Methylsalicylate brick (MIT 2006) on LB plate.<br />
# Add 5 µl of Kanamicin (50 mg/ml) and 10 µl of IPTG (100 mM) to 5 ml of LB medium (tubes).<br />
# Incubate 16h at 37 °C under shaking conditions.<br />
# Measure OD of 3 cultures.<br />
# Account for differences in OD: ex. If you have an OD of 1 take 1 ml of this sample, if you have an OD of 0,8 take 1,2 ml.<br />
# Add 1/5 volume of stopsolution (95% ethanol, 5% phenol).<br />
# Freeze 5 minutes in liquid Nitrogen.<br />
# Centrifuge needed amount of cells.<br />
<br />
<b>mRNA isolation</b><br />
<br />
For mRNA isolation use the Promega SV total RNA isolation kit ([http://be.promega.com/resources/protocols/technical-manuals/0/sv-total-rna-isolation-system-protocol/?origUrl=http%3a%2f%2fwww.promega.com%2fresources%2fprotocols%2ftechnical-manuals%2f0%2fsv-total-rna-isolation-system-protocol%2f Promega SV total RNA isolation protocol])<br />
# Section 8.C (Isolation of RNA from Gram-positive and Gram-negative bacteria)<br />
# Section 4.E (RNA Purification by Centrifugation) We did not do step 4 and 5 (addition of DNase) but instead used the DNase protocol of [http://products.invitrogen.com/ivgn/product/AM2238 Ambion Turbo DNase]<br />
For 100 µl of RNA:<br />
## Add 10 µl 10x buffer. Add 1 µl Turbo DNase and mix gently.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 1 µl of Turbo DNase.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 20 µl of DNase inactivation reagent and mix well.<br />
## Put on room temperature for 5 min. Mix occasionally by flicking the tube<br />
## Centrifuge on 10000 g for 2 min.<br />
## Transfer the RNA to a new tube.<br />
<br />
<b>Create cDNA</b><br />
<br />
Use the Fermentas Revert aid H kit ([http://www.thermoscientificbio.com/reverse-transcription-rtpcr-rtqpcr/revertaid-h-minus-first-strand-cdna-synthesis-kit/ Fermentas Revert aid H protocol])<br />
The protocol for RT-PCR (I. First Strand Synthesis) was followed<br />
<br />
<b>qPCR</b><br />
<br />
qPCR kit was not yet decided.<br />
<br />
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<a id="Root measurements"></a><br />
<h3 class="bg-yellow">Root measurements</h3><br />
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<b>Aim</b>: To determine whether the different induction methods or the Methyl Salicylate (MeS) had an effect on plant growth.<br />
<br/><br/><br />
Experimental setup: We measured the roots of the same plants used in the experiments to determine aphid population and predator preference (see above). <br />
<br/><br />
[[File:Uppot.png|200px|Uppot]]<br />
[[File:Wash.png|200px|Root wash]]<br />
[[File:Dryandweigh.png|200px|Dry roots]]<br />
<br />
<br/><br />
The plants were cut off at the bottom of the stem and removed from the pot. <br />
<br/>After most of the dirt has been removed, they were carefully washed to remove the rest of the dirt.<br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>The roots were then dried and weighed. <br />
<br><br />
[[File:Rootmeasurement.png|200px|Root measurement]]<br />
[[File:Rootmeasurement1.png|200px|Root measurement 1]]<br />
<br/>The roots were then measured and recorded.<br />
<br />
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<a id="Headspace GC"></a><br />
<h3 class="bg-yellow">Headspace GC</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with 0 or 0.1 mM of salicylic acid and left to grow for 7 hours.<br />
#Cultures were chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filterstelized (0.22µm).<br />
#Salt was added to 5 ml of this filtersterilized supernatant<br/><br/><br />
<br />
<b>Gass chromatography:</b><br />
Samples were analyzed with a calibrated Autosystem XL gas chromatograph with a headspace sampler (HS40; Perkin-Elmer, Wellesley, Mass.) and equipped with a CP-Wax 52 CB column (length, 50 m; internal diameter, 0.32 mm; layer thickness, 1.2 μm; Chrompack; Varian, Palo Alto, Calif.). Samples were heated for 16 min at 72°C in the headspace autosampler. The injection block and flame ionization detector (FID) temperatures were kept constant at 180 and 250°C, respectively; helium was used as the carrier gas. The oven temperature was 75°C held for 6 min and then increased to 110°C at 25°C min−1 and held at 100°C for 3.5 min. Results were analyzed with Perkin-Elmer Turbochrom Navigator software.<br />
<br />
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<a id="GC-MS analysis"></a><br />
<h3 class="bg-yellow">GC-MS analysis</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with or without 0.1 mM salicylic acid and left to grow for overnight. Samples were induced with 0.2 mM IPTG 6 hours post inoculation.<br />
#Cultures were then chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filter sterilized (0.22µm).<br />
#2 ml of this filtersterilized supernatant was then extracted with 1 ml of hexane<br />
#Extractions were done in glass tubes with rigorous vortexing for 10 minutes<br />
#The upper phase was transferred to a new glass vial<br />
#The extraction was repeated twice (total of 3 extractions with 1 ml of hexane)<br />
#The resulting ± 3 ml of extract were then reduced by evaporation under a nitrogen flow and redissolved in 50 µl of hexane.<br />
<br/><br/><br />
<br />
<b>Gass chromatography:</b><br />
GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
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<a id="Protein Extraction"></a><br />
<h3 class="bg-yellow">Protein Extraction</h3><br />
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<br/><b>Aim</b>: To extract proteins from our <i>E. coli</i> for SDS-PAGE analysis.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#E.coli cells transformed with the indicated plasmids were grown either to mid-exponential phase (OD600nm ~ 1.0 for endpoint assays) or to the indicated optical densities.<br/><br />
#Samples were taken and spun down (3min, 4000 rpm, 4°C).<br/><br />
#Growth medium was removed and cell pellets frozen (-80 °C)<br/><br />
#Cell pellets were thawed on ice and resupended in equal volumes extraction buffer.<br/><br />
#Suspensions were incubated on ice for 10min and subsequently sonicated (3X 10” pulses) with ice cooling in between.<br/><br />
#Suspensions were spun down (10min, 14000 rpm, 4°C).<br/><br />
#Aliquots were mixed with 5X sample buffer and boiled (5min 95°C).<br/><br />
<br />
: 25mM Tris pH 8.0; 0.1%9v/v) NP40; 5 mM EDTA; 50 mM NaCl; protease inhibitor cocktail (Benzamidine; PMSF; Leupeptin).<br />
<br />
<br />
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<br/><br/></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/ProtocolsTeam:KU Leuven/Protocols2013-10-29T01:25:15Z<p>Veerledewever: </p>
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<h3>Index</h3><br />
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<a href="#Aphid population preference">Aphid population preference</a><br><br />
<a href="#Aphid mobility experiment">Aphid mobility experiment</a><br><br />
<a href="#Chemically competent E.coli cells: CaCl2 method">Chemically competent E.coli cells: CaCl2 method</a><br><br />
<a href="#Chemically competent E.coli cells: Inoue method">Chemically competent E.coli cells: Inoue method</a><br><br />
<a href="#Colony PCR for Streptomyces">Colony PCR for Streptomyces</a><br><br />
<a href="#Digestion and ligation">Digestion and ligation</a><br><br />
<a href="#DNA extraction from agarose gels">DNA extraction from agarose gels</a><br><br />
<a href="#Grow electrocompetent cells">Grow electrocompetent cells</a><br><br />
<a href="#Isolation of plasmid DNA from E. coli (mini prep)">Isolation of plasmid DNA from E. coli (mini prep)</a><br><br />
</div><br />
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<div class="span6 greytext"><br />
<br />
<a href="#PCR clean-up">PCR clean-up</a><br><br />
<a href="#PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer">PCR Protocol for Taq DNA Polymerase</a><br><br />
<a href="#Plasmid DNA isolation">Plasmid DNA isolation</a><br><br />
<a href="#Predator attraction to MeS induced aphid infested plants">Predator attraction to MeS induced aphid infested plants</a><br><br />
<a href="#Predator attraction to Methyl Salicylate">Predator attraction to Methyl Salicylate</a><br><br />
<a href="#qRT-PCR Protocol">qRT-PCR Protocol</a><br><br />
<a href="#Root measurements">Root measurements</a><br><br />
<a href="#Headspace GC">Headspace GC</a><br><br />
<a href="#GC-MS analysis">GC-MS analysis</a><br><br />
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<a id="Aphid population preference"> </a><br />
<h3 class="bg-yellow">Aphid population preference</h3><br />
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<b>Aim</b>: To determine the effect on aphid population on Methyl Salicylate (MeS) induced plants.<br />
<br/> <br/><br />
Experimental setup: We induced five plants per concentration<br />
*Plants: Small potted paprika plants in two ways: via the root or byspraying; 60 in total. Paprika plants don’t make MeS naturally but do produce salicylic acid. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml ,0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 97% pure ethanol or water. <br />
<br><br />
<b>Procedure</b><br />
<br />
Induction requires 48h<br />
*Induction via the roots<br />
#Uproot the plant, clean off the dirt with water because compost can interfere with the uptake of MeS<br />
#Wipe off the water before placing the roots in a cup with the desired concentration for 10 minutes. MeSA was first suspended in ethanol before being further diluted to the desired concentration in 50ml water<br />
#Re-pot the plant and divide the remainder of the MeSA solution amongst the 5 plants <br />
[[File:Rootinduction.png|200px|Root induction]]<br />
<br />
*Induction via the leaf<br />
#The desired concentrations were diluted in 15ml ethanol, resulting in 3ml per plant. Ethanol has been shown to have no plant induction properties<br />
#Each leaf of the plant is sprayed above and underneath<br />
[[File:Leafinduction.png|200px|Leaf induction]]<br />
<br/><br />
*Place aphids on MeS induced plants 48h post-induction<br />
#Place 15 green peach aphids on the head of each induced plant<br />
#Take the smallest aphids present on the aphid-infested leaf. To be sure that it is a first generation aphid<br />
#Place each plant in a separate net<br />
#Plants of the same concentration, induced by spraying or via the roots, are placed in the same row; 10 plants per row. The rows are roughly 1 metre apart<br />
* Counting aphids on day 7<br />
#Following a form, count how many aphids are on Cotyledon, separate true leafs and the head of the induced plants.<br />
#See <a href"> results</a><br />
#There is a possibility of contamination while placing the aphids on the plant, meaning that older aphids and/or flying aphids crawled onto the plant by accident. These were removed from the plant so that the amount of aphids on day 10 would not be tainted<br />
<br />
[[File:Howtocountaphids.png|300px|How to count aphids]]<br />
<br />
* Counting aphids on day 10<br />
#We count the aphids for a second time to allow the second generation to develop. This way we investigate whether MeS has an effect on the aphid’s behaviour. That they are encouraged to leave the plant, reproduce less or generate mobile (flying) aphids. <br />
#The head of the plant is where the majority of the secondary metabolites gather; hence we expect to see an effect on the distribution of the aphid population.<br />
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<a id="Aphid mobility experiment"> </a><br />
<h3 class="bg-yellow">Aphid mobility experiment</h3><br />
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<br/><b>Aim</b>: To determine the effect of EBF on aphid mobility.<br />
<br/> <br/><br />
<b>Experimental setup</b>: Leaves infested with aphids were divided in two glass containers (not sealed) randomly (n=3; 3 leaves per group). After 1h plates with bacteria, either control (BL21) or BL21 with EBF construct, were put under each leaf.<br />
<br />
<br><br />
<b>Measurement</b>: Aphids that were on the top of each leaf were counted at start and at each time point during the experiment.<br/><br />
At each time point the amount of aphids moving on the leaf were also counted. The time points used were 0, 50, 150 and 200 minutes.<br />
<br />
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<a id="Chemically competent E.coli cells: CaCl2 method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: CaCl2 method</h3><br />
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<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells!''' <br\><br />
'''Do not shock freeze (liqN2) – just transfer from ice to -80°C!''' <br\><br />
'''Work sterile!'''<br />
<br />
#Inoculate '''3 ml''' growth medium with your cells of choice ('''DH5alpha''' or '''TOP10''' for plasmid maintenance & cloning)<br />
#Grow overnight at '''37°C''' with sufficient aeration<br />
#Inoculate '''100 ml LB''' with '''1 ml''' of overnight culture<br />
#Grow at '''37°C''' to an OD 600nm of approx '''0.5 to 0.8''' (usually '''2-3 hrs''')<br />
#Centrifuge cells ('''3700-4000 rpm 4°C 12 min''' – sterile 50ml tube)<br />
#Resuspend pellet on ice with FSB to '''15 ml''' (cold) for each '''100 ml''' pellet<br />
#Incubate cells '''10 min''' on ice<br />
#Centrifuge cells ('''3700 – 4000 rpm 4°C 10 min''')<br />
#Re-suspend pellet on ice in '''4-8 ml''' FSB (cold) for each '''100 ml pellet'''<br />
#Aliquot cells appropriately ('''200-400 µl aliquots''') and freeze aliquots at '''-80°C'''<br />
<br><br />
<b>Buffers and solutions</b><br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Growth medium<br />
**LB 25 g/l<br />
*Frozen Storage Buffer (FSB)<br />
**10 mM Potassium Acetate<br />
**10% glycerol<br />
**10 mM KCl<br />
**50 mM CaCl2<br />
**Check pH – must be around 6.2 – if need be adjust with AcAc (HCl) or KOH<br />
**Buffer should be filter-sterilized (0.45 micrometer filter)<br />
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<a id="Chemically competent E.coli cells: Inoue method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: Inoue method</h3><br />
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<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells.'''<br/><br />
'''Work sterile'''<br/><br />
#Pick a single colony from a freshly transformed plate (after overnight growth '''at 37 °C''')<br />
#Transfer the colony to '''25 ml growth medium''' in a sterile '''250 ml''' erlenmeyer<br />
#Incubate the culture '''at 37°C''' for '''6 – 8 hrs''' under vigorous shaking ('''250 – 300 rpm''')<br />
#Prepare '''3 1L flasks''' with '''250 ml growth medium''' in each<br />
#Inoculate the flasks with 10, 4 or 2 ml of the dayculture -> you create 3 different starting optical densities.<br />
#Incubate the cultures at '''18-22°C overnight''' under moderate shaking ('''180 – 220 rpm''')<br />
#Monitor the '''OD600nm''' until it reaches '''0.55'''<br />
#Place cells in an ice-water bath to cool them down quickly (-> swirl occasionally, keep them in for approx 10 min) <br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''80 ml icecold inoue transformation buffer'''<br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''20 ml icecold inoue transformation buffer'''<br />
#Add '''1.5 ml 100% DMSO''' – mix by swirling<br />
#Store whole on ice for approx '''10 minutes'''<br />
#Aliquot as quickly as possible '''100 – 200 µl aliquots''' into '''1.5 ml tubes''' (precooled on ice) and snapfreeze them into a liquid N2 bath<br />
<br />
<b>Buffers and solutions</b><br />
<br />
*Growth medium<br />
*Inoue transformation buffer<br />
{| class="wikitable"<br />
| '''Reagent''' || '''Final concentration (mM)''' || '''Amount per liter''' <br />
|-<br />
| MnCl2 || 55 || 10.88 g (from MnCl2*4H2O) <br />
|-<br />
| CaCl2 || 15 || 2.20 g (from CaCl2*2H2O) <br />
|-<br />
| KCl || 250 || 18.65 g (from KCl) <br />
|-<br />
| PIPES || 10 || 20 ml (from 0.5M stock solution) <br />
|-<br />
| H2O || to 1 liter || <br />
|}<br />
Filter sterilize with a 0.45 µm nalgene filter<br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Stock 0.5 M PIPES (piperazine-1,2-bis[2-ethanesulfonic acid]) pH 6.7<br />
**Dissolve 15.1 g PIPES in 80ml MilliQ H2O<br />
**Adjust pH to 6.7 with 5M KOH<br />
**Bring volume to 100 ml with MilliQ H2O<br />
**Filter sterilize with a 0.45 µm nalgene filter<br />
**Aliquot (5 times) and store at -20°C<br />
<br />
<br/><br />
<br />
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<div class="span12"><br />
<a id="Colony PCR for Streptomyces"> </a><br />
<h3 class="bg-yellow">Colony PCR for <i>Streptomyces</i></h3><br />
</div><br />
</div><br />
</html><br />
<br />
<b>Pretreatment of ''Streptomyces''</b><br />
<br />
Because of the fact that ''Streptomyces'' are Gram-positive bacteria with a thick peptidoglycan layer, we performed 4 ways to pretreat the cells for colony PCR (all pretreatments gave positive results in the end):<br />
*microwave ''Streptomyces'' for 4 mins<br />
*mix ''Streptomyces'' with water and 0.2% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with 1% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with TE buffer, 0.2% SDS, microwave for 4 mins<br />
<br><br />
<b>PCR mixture</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br />
{| class="wikitable"<br />
| '''Components''' || '''Amount''' <br />
|-<br />
| 2x fusion master mix (add in the end) || 25 µl <br />
|-<br />
| forward primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| reverse primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| template DNA || 1 µl <br />
|-<br />
| DMSO (recommended for high GC content) || 1.5 µl <br />
|-<br />
| H2O (PCR certified, no contamination) || add to final volume of 50µl <br />
|}<br />
'''Keep tubes on ice at all times!''' <br/><br />
'''Be sure to put Phusion Master Mix immediately back at -20!'''<br />
<br />
<b>Cycling instruction</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| 1 || 95°C || 6'<br />
|-<br />
| 2<br/> cycle 29x || 95°C<br/>55°C<br/>72°C || 30"<br/>30"<br/>45"<br/><br />
|-<br />
| 3 || 72°C || 10'<br />
|-<br />
| 4 || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="Digestion and ligation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Digestion and ligation</h3><br />
</div><br />
</div></html><br />
<b>Consumables and equipment</b><br />
<br />
* Restriction enzymes (EcoRI, Xbal, Spel, Pstl), NEBuffer 2.1<br />
* 10x T4 DNA ligase Reaction Buffer, T4 DNA Ligase<br />
* Keep all enzymes on ice; make sure buffers have no precipitation<br />
* H20<br />
* Small PCR Tubes or eppendorfs<br />
* 2 µl, 200 µl pipette tips<br />
* Destination plasmid as purified DNA<br />
* Upstream and downstream part as purified DNA<br />
* 2 µl and 20 µl pipette<br />
* Heat block 37° and 80°C<br />
* Timer<br />
* Rack for small PCR tubes<br />
* -20°C freezer + freeze box<br />
<br><br />
<b>Digestion</b><br />
<br />
* Mark PCR tubes or eppendorfs<br />
a. U= upstream part : E + S restriction enzymes<br />
b. D= downstream part : X + P restriction enzymes<br />
c. P= plasmid (destination) : E + P restriction enzymes<br />
d. NB: if only one part for insertion insert I= Insert : E + P restriction enzymes<br />
* In each tube 500 ng DNA for digestion + H20 until total volume is 43 µl<br />
* Add 5 µl of NEBuffer 2.1 to each tube<br />
* Add 1 µl of first restriction enzyme<br />
* Add 1 µl of the second restriction enzyme '''TOTAL VOLUME = 50 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at 37°C for 20 min. (officially 15 min)<br />
* Incubate at 80°C for 20 min.<br />
a. OPTIONAL: run 10-20 µl on 1% agarose gel and look for expected bands as confirmation<br />
b. OPTIONAL: store at -20°C or proceed to ligation immediately<br />
<br><br />
<b>Ligation</b><br />
<br />
* Add 13 µl of H2O to a 200 µl PCR tube or eppendorf<br />
* Add 2 µl of each part you want to ligate<br />
* Add 2 µl of 10X T4 DNA Ligase Reaction Buffer to the tube<br />
* Add 1 µl of the T4 DNA Ligase to the tube '''TOTAL VOLUME = 20 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at room temperature for 10 min<br />
a. Incubate at 80°C for 20 min. <br />
b. Store the ligation mix at -20°C or proceed immediately to the transformation step.<br />
<br />
<br/><br />
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<div class="span12"><br />
<a id="DNA extraction from agarose gels"> </a><br />
<h3 class="bg-yellow">DNA extraction from agarose gels</h3><br />
</div><br />
</div></html><br />
(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br />
<br><br />
<b>Procedure</b><br />
<br />
#Excise DNA fragment/solubilize gel slice: take a clean scalpel to excise the DNA fragment from an agarose gel, remove all excess agarose. For each '''100mg of agarose gel < 2%''' add '''200µl buffer NTI''', for gels containing > 2% agarose, double the volume of buffer NTI. Incubate sample for '''5-10 min''' at '''50°C''', vortex the sample briefly every 2-3 min until the gel slice is '''completely''' dissolved.<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2ml) and load up to 700µl sample, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''700µl buffer NT3''' to the column, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1min''' at '''11000g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5ml microcentrifuge tube, add '''15-30µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000g'''.<br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="Grow electrocompetent cells"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Grow electrocompetent cells</h3><br />
</div><br />
</div></html><br />
<br />
(source: adapted from openwetware.org) <br />
<br />
<br/><br />
<b>Materials</b><br />
<br />
*GYT (glycerol, yeast extract, tryptone)<br />
**10%(v/v) glycerol <br />
**0.125% (w/v) yeast extract <br />
**0.25% (w/v) tryptone <br />
<br />
*DI water<br />
*10% Glycerol<br />
<br />
<br />
<b>Special Equipment</b><br />
*Centrifuge<br />
*Ice water bath<br />
*Liquid nitrogen<br />
<br />
<br />
<b>Procedure</b><br />
<br />
Important: All steps in this protocol should be carried out aseptically<br />
<br />
*Inoculate: Prepare flask containing 5 ml of LB medium. Pick up a single colony of cells from plate (using a sterile toothpick) and swirl around inside flask. Incubate the culture overnight at 37°C with vigorous aeration (250 pm in a rotary shaker). <br />
<br />
*Dilute and incubate: Inoculate two aliquots of 495 ml of prewarmed LB medium in separate 2-liter flasks with 5 ml of the overnight bacterial culture. Incubate the flasks at 37°C with agitation (300 cycles/min in a rotary shaker). Measure the OD-600 every twenty minutes (this step will take around 1.5-2 hrs). (or judge the OD by eyes to avoid always taking the sample to disturb the growth as well as avoiding the contamination)<br />
<br />
*Rapidly cool culture: Once the OD-600 of the culture reaches 0.6-1.0 (Molecular Cloning recommends 0.4), rapidly transfer the flasks to the pre-made ice-water bath for 15-30 minutes. Swirl the culture occasionally to ensure that cooling occurs evenly. In preparation for the next step, place the centrifuge bottles in the ice-water bath as well. <br />
<br />
Note: After this point, do not let your cells warm up past 4°C '''always keep on ice'''<br />
<br />
Note: When harvesting cells by decanting, be very careful not to disturb the pellet-- this could result in a much lower yield. If necessary, aspirate instead or decant the supernatant. Ask someone to show you how to aspirate. Also, if the pellet seems loose, sometimes it is helpful to re-spin the cells down.<br />
<br />
*Centrifuge 1: Transfer the cultures to ice-cold centrifuge bottles. Harvest the cells by centrifugation at 1000 g (2500 rpm) for 15 minutes at 4°C. Decant the supernantant and resuspend the cell pellet in 20 ml of ice-cold 10% glycerol. Note: this should be done for each of the two 500ml cultures, i.e this is a 1:1 resuspension rather than a concentration by a factor of 2 BC. <br />
<br />
*Centrifuge 2 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 20 ml ice-cold 10% glycerol. <br />
<br />
*Centrifuge 3 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 10 ml ice-cold 10% glycerol.<br />
<br />
*Centrifuge 4 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Carefully decant the supernatant and use a Pasteur pipette attached to a vacuum line to remove any remaining drops of buffer. <br />
<br />
*Resuspend in GYT: Resuspend in 1 ml ice cold GYT. This is best done by gently swirling rather pipetting or vortexing. <br />
<br />
*Test for arcing: Transfer 40 µl of the suspension to an ice-cold electroporation cuvette and test whether arcing occurs when an electrical discharge is applied. Place the cuvette in the holder attached to the machine. Go to option 4, Pre-set protocols; choose bacterial; choose the correct choice for your size cuvette, probably the first option for a .1 cm cuvette. If arcing occurs, wash the remainder of the cell suspension once more with ice-cold GYT medium to ensure that the conductivity of the bacterial suspension is sufficiently low (<5 mEq). (or check the pulse time, if the pulse time < 4, redo the wash, if the pulse time > 4, it's ok)<br />
<br />
*Storage: Store cells at -80°C until they are required for use. For storage, dispense 40 µl aliquots of the cell suspension into sterile, ice-cold .5 ml microcentrifuge tubes, drop into a bath of '''liquid nitrogen''' and transfer to a -80°C freezer. To remove the tubes from the liquid nitrogen bath, bring out into the hall along with a storage box, and pour the tubes and liquid nitrogen into the box. Once all the tubes are out, close the box most of the way and let the liquid run out into the hallway. Try not to do this in the very center of the walkway! <br />
<br />
*To use frozen cells: Remove an appropriate number of aliquots of cells from the -80°C freezer. Thaw the tubes on ice.<br />
<br />
<br />
<br/><br />
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<a id="Isolation of plasmid DNA from E. coli (mini prep)"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Isolation of plasmid DNA from <i> E. coli</i> (mini prep)</h3><br />
</div><br />
</div></html><br />
<br />
(source: NucleoSpin® plasmid - Macherey-Nagel)<br />
<br />
<br><br />
<b>Nanodrop protocol</b><br />
<br><br><br />
Nanodrop can be used to measure the DNA, RNA and protein <br />
Measure the concentration and purity of extracted DNA using absorbance (using the automated nanodrop machine!) <br />
<br />
Method:<br />
#Log onto computer and select Nanodrop program from the desktop (ND 1000) <br />
#To clean Nanodrop machine wipe pedestal and top and add 3 µl of water to nib of pedestal. Press blank. <br />
#Wipe the water off, to initialize/equalize the equipment add 3 μl of the elution buffer [EB] used in the sample and press blank. Set to DNA-50 for DNA. <br />
#Wipe to remove buffer and apply 3 μl of sample to nib. Press measure. <br />
#If dealing with multiple samples, clean the equipment with water at regular intervals (about every 10 samples). <br />
#After measurements, clean the equipment with 3 μl of water on the spectrometer and press blank. Wipe and log off. <br />
<br />
<br/><br />
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<a id="PCR clean-up"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR clean-up</h3><br />
</div><br />
</div></html><br />
(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br/><br />
<br />
This is used for PCR clean-up as well as DNA concentration and removal of salts, enzymes, etc. from enzymatic reactions (SDS<0.1%)<br />
#Adjust DNA binding condition: mix '''1 volume of sample''' with '''2 volumes of buffer NTI''' (eg. mix 100 µl PCR reaction and 200 µl buffer NTI).<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2 ml) and load up to 700 µl sample, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''600µl buffer NT3''' to the column, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1 min''' at '''11000 g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5 ml microcentrifuge tube, add '''50 µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000 g'''.<br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR protocol for Taq DNA polymerase with standard Taq Buffer</h3><br />
</div><br />
</div></html><br />
<br />
<b>Reaction set up</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br/><br />
'''We recommend assembling all reaction components on ice and quickly transferring the reactions to a thermocycler preheated to the denaturation temperature (95°C).'''<br />
{| class="wikitable"<br />
| '''Components''' || '''25 μl reaction''' || '''50 μl reaction''' || '''Final concentration'''<br />
|-<br />
| 10X Standard Taq Reaction Buffer || 2.5 µl || 5 µl || 1X<br />
|-<br />
| 10 mM dNTPs || 0.5 µl || 1 µl || 200 µM<br />
|-<br />
| 10 µM Forward Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| 10 µM Reverse Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| template DNA || variable || variable || <1,000 ng <br />
|-<br />
| Taq DNA Polymerase || 0.125 µl || 0.25 µl || 1.25 units/50 µl PCR<br />
|-<br />
| Nuclease-free water || to 25 µl || to 50 µl || <br />
|}<br />
<br />
Notes: Gently mix the reaction. Collect all liquid to the bottom of the tube by a quick spin if necessary. Overlay the sample with mineral oil if using a PCR machine without a heated lid.<br />
Transfer PCR tubes from ice to a PCR machine with the block preheated to 95°C and begin thermocycling.<br />
<br />
<b>Thermocyclingconditions for a routine PCR</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| Initial denaturation || 95°C || 30"<br />
|-<br />
| 30 cycles || 95°C<br/>48-65°C<br/>68°C || 15-30"<br/>15-60"<br/>1min/kb<br />
|-<br />
| Final extension || 68°C || 5'<br />
|-<br />
| Hold || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<div id="header" class="row-fluid"><br />
<a id="Plasmid DNA isolation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Plasmid DNA isolation</h3><br />
</div><br />
</div></html><br />
<br />
<b>Procedure</b><br />
<br />
[https://static.igem.org/mediawiki/2013/8/8f/Risk_assessment_Plasmid_DNA_Purification_kit_KULeuven.pdf Risk assessment for plasmid DNA purification kit]<br />
#Bring '''1.5 ml culture''' in an eppendorf, centrifuge for '''1 min with maximum speed'''<br />
#Pour away the supernatant<br />
#Bring another '''1.5 ml culture''' into the same eppendorf, centrifuge for '''1 min''' and pour away supernatant<br />
#Resuspend the pellet with '''200µl GTE-solution''' we made earlier<br />
#Add '''4 µl RNase A (10mg/ml)'''<br />
#Add '''400 µl premade solution''' (contains 0.2M NaOH and 1%SDS in sterile water)<br />
#Mix them well, place on ice for '''5 min'''<br />
#Add '''300 µl ice cold 7.5 M ammonium acetate''', vortex for 10 s, place on ice for '''5 mins'''<br />
#Centrifuge for '''5min with 13000 rpm'''<br />
#Bring the supernatant into a new eppendorf<br />
#Centrifuge this supernatant for a second time ('''5 min, 13000 rpm''') and bring the supernatant in a new eppendorf<br />
#Add isopropanol to the supernatant (60% in volume of the supernatant), leave '''at room temp. for 5 min'''<br />
#Centrifuge for '''10 min with 13000 rpm''', immediately remove the supernatant, keep the transparent pellet in the tube, put the tube upside down on a tissue to dry it<br />
#Add '''1 ml of cold 70% ethanol''' to the pellet, invert 5 times<br />
#Centrifuge '''3 min with 13000 rpm'''<br />
#Remove supernatant, the droplet on the tube wall can be removed by tissue<br />
#Let the pellet dry<br />
#Add '''50 µl elution buffer''' (or sterile water) to the pellet<br />
<br />
<b>Buffers and Solutions</b><br />
<br />
*GTE-buffer<br />
**50 mM glucose<br />
**25 mM Tris-Cl (pH 8.0)<br />
**10 mM EDTA<br />
**4 mg/ml lysozyme<br />
<br />
*IPTG stock solution<br />
**238 mg in 10 ml AD<br />
**Filter sterilize<br />
**Split into 1 ml aliquots<br />
**Store in -20 freezer<br />
<br />
Final concentration/work concentration in agar plates = 0.1mM – 1 mM <br/><br />
Sigma recommends 0.2 mM for blue-white screening <br/><br />
Thermo Scientific recommends 0.1 mM<br />
<br />
<br/><br />
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<div class="span12"><br />
<a id="Predator attraction to MeS induced aphid infested plants"></a><br />
<h3 class="bg-yellow">Predator attraction to MeS induced aphid infested plants</h3><br />
</div><br />
</div></html><br />
<b>Aim</b>: To determine the effect on aphid predators’ attraction to Methyl Salicylate (MeS) induced plants.<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Macrolophus</i> adults<br />
*Plants: Small potted MeS induced paprika plants in two ways: via the root or via the leaf, 60 in total. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)). <br />
<br><br />
<b>Cafetaria model</b><br />
<br />
[[File:Cafetariamodel.png|100px|Leafinduction]] [[File:Cafetariamodel1.png|200px|Leafinduction]]<br />
<br />
<br/><br />
In the cafeteria model shown above, each concentration will be placed, plus the control, randomly in a circle. The plants should not be close to the edge of the cage in which the experiments are carried out. The plants should be placed in rotation with every repeat of the set-up to eliminate other factors. <br />
*Releasing the <i>Macrolophus</i>:<br />
#10 <i>Macrolophus</i> (adult) are shaken out of the pot and placed in the middle of the cafetaria model (see picture) <br />
#We release around 50 <i>Macrolophus</i> (adult) per set-up<br />
[[File:Macrolophus2.png|200px|Leafinduction]]<br />
*Counting the <i>Macrolophus</i>: <br />
# After every 45 min from the moment we released the <i>Macrolophus</i>, the amount per plant is counted and recorded<br />
# The <i>Macrolophus</i> are then shaken off the plant back into the middle of the circle so that they can make their choice again<br />
# We will take 3 recordings<br />
<!-- TITLE --><br />
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<div id="header" class="row-fluid"><br />
<a id="Predator attraction to Methyl Salicylate"></a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Predator attraction to methyl salicylate</h3><br />
</div><br />
</div></html><br />
<b>Aim</b>: To determine a working concentration of methyl salicylate (MeS) concentration which attracts predators .<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Adalia bipunctata</i> (ladybugs): adult and larvae <br />
*Concentration: 1000ng/ml, 100ng/ml, 10ng/ml, 1ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 96% ethanol. <br />
<br><br />
<b>Biobest setup</b><br />
<br />
[[File:Biobestsetup.png|200px|Biobeststetup]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure EtOH). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
<br />
<b>pcfruit setup</b><br />
<br />
[[File:Y-tubeolfactormeter.png|200px|Leafinduction]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure hexane or paraffin oil). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
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<a id="qRT-PCR Protocol"></a><br />
<h3 class="bg-yellow">qRT-PCR protocol</h3><br />
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General experiment: 3 biological repeats of stationary phase cultures 16 hrs growth.<br />
<br />
<b>Sample preparation</b><br />
<br />
# Inoculate fresh LB plate with the strain harbouring the MS brick <br />
# Isolate plasmid DNA, MS brick<br />
# Nanodrop plasmid (minimum concentration 20 ng/µl) = 155 ng/µl <br />
# Design primers for pcha/pchb and for bsmt1 using Primer express. Primer sequence can be found in document “Genes_qPCR_primers_IC.docx”.<br />
# Inoculate E. Coli with Methylsalicylate brick (MIT 2006) on LB plate.<br />
# Add 5 µl of Kanamicin (50 mg/ml) and 10 µl of IPTG (100 mM) to 5 ml of LB medium (tubes).<br />
# Incubate 16h at 37 °C under shaking conditions.<br />
# Measure OD of 3 cultures.<br />
# Account for differences in OD: ex. If you have an OD of 1 take 1 ml of this sample, if you have an OD of 0,8 take 1,2 ml.<br />
# Add 1/5 volume of stopsolution (95% ethanol, 5% phenol).<br />
# Freeze 5 minutes in liquid Nitrogen.<br />
# Centrifuge needed amount of cells.<br />
<br />
<b>mRNA isolation</b><br />
<br />
For mRNA isolation use the Promega SV total RNA isolation kit ([http://be.promega.com/resources/protocols/technical-manuals/0/sv-total-rna-isolation-system-protocol/?origUrl=http%3a%2f%2fwww.promega.com%2fresources%2fprotocols%2ftechnical-manuals%2f0%2fsv-total-rna-isolation-system-protocol%2f Promega SV total RNA isolation protocol])<br />
# Section 8.C (Isolation of RNA from Gram-positive and Gram-negative bacteria)<br />
# Section 4.E (RNA Purification by Centrifugation) We did not do step 4 and 5 (addition of DNase) but instead used the DNase protocol of [http://products.invitrogen.com/ivgn/product/AM2238 Ambion Turbo DNase]<br />
For 100 µl of RNA:<br />
## Add 10 µl 10x buffer. Add 1 µl Turbo DNase and mix gently.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 1 µl of Turbo DNase.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 20 µl of DNase inactivation reagent and mix well.<br />
## Put on room temperature for 5 min. Mix occasionally by flicking the tube<br />
## Centrifuge on 10000 g for 2 min.<br />
## Transfer the RNA to a new tube.<br />
<br />
<b>Create cDNA</b><br />
<br />
Use the Fermentas Revert aid H kit ([http://www.thermoscientificbio.com/reverse-transcription-rtpcr-rtqpcr/revertaid-h-minus-first-strand-cdna-synthesis-kit/ Fermentas Revert aid H protocol])<br />
The protocol for RT-PCR (I. First Strand Synthesis) was followed<br />
<br />
<b>qPCR</b><br />
<br />
qPCR kit was not yet decided.<br />
<br />
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<a id="Root measurements"></a><br />
<h3 class="bg-yellow">Root measurements</h3><br />
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<b>Aim</b>: To determine whether the different induction methods or the Methyl Salicylate (MeS) had an effect on plant growth.<br />
<br/><br/><br />
Experimental setup: We measured the roots of the same plants used in the experiments to determine aphid population and predator preference (see above). <br />
<br/><br />
[[File:Uppot.png|200px|Uppot]]<br />
[[File:Wash.png|200px|Root wash]]<br />
[[File:Dryandweigh.png|200px|Dry roots]]<br />
<br />
<br/><br />
The plants were cut off at the bottom of the stem and removed from the pot. <br />
<br/>After most of the dirt has been removed, they were carefully washed to remove the rest of the dirt.<br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>The roots were then dried and weighed. <br />
<br><br />
[[File:Rootmeasurement.png|200px|Root measurement]]<br />
[[File:Rootmeasurement1.png|200px|Root measurement 1]]<br />
<br/>The roots were then measured and recorded.<br />
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<a id="Headspace GC"></a><br />
<h3 class="bg-yellow">Headspace GC</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with 0 or 0.1 mM of salicylic acid and left to grow for 7 hours.<br />
#Cultures were chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filterstelized (0.22µm).<br />
#Salt was added to 5 ml of this filtersterilized supernatant<br/><br/><br />
<br />
<b>Gass chromatography:</b><br />
Samples were analyzed with a calibrated Autosystem XL gas chromatograph with a headspace sampler (HS40; Perkin-Elmer, Wellesley, Mass.) and equipped with a CP-Wax 52 CB column (length, 50 m; internal diameter, 0.32 mm; layer thickness, 1.2 μm; Chrompack; Varian, Palo Alto, Calif.). Samples were heated for 16 min at 72°C in the headspace autosampler. The injection block and flame ionization detector (FID) temperatures were kept constant at 180 and 250°C, respectively; helium was used as the carrier gas. The oven temperature was 75°C held for 6 min and then increased to 110°C at 25°C min−1 and held at 100°C for 3.5 min. Results were analyzed with Perkin-Elmer Turbochrom Navigator software.<br />
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<a id="GC-MS analysis"></a><br />
<h3 class="bg-yellow">GC-MS analysis</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with or without 0.1 mM salicylic acid and left to grow for overnight. Samples were induced with 0.2 mM IPTG 6 hours post inoculation.<br />
#Cultures were then chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filter sterilized (0.22µm).<br />
#2 ml of this filtersterilized supernatant was then extracted with 1 ml of hexane<br />
#Extractions were done in glass tubes with rigorous vortexing for 10 minutes<br />
#The upper phase was transferred to a new glass vial<br />
#The extraction was repeated twice (total of 3 extractions with 1 ml of hexane)<br />
#The resulting ± 3 ml of extract were then reduced by evaporation under a nitrogen flow and redissolved in 50 µl of hexane.<br />
<br/><br/><br />
<br />
<b>Gass chromatography:</b><br />
GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
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<h3 class="bg-yellow">Protein extraction</h3><br />
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<br/><b>Aim</b>: To extract proteins from our <i>E. coli</i> for SDS-PAGE analysis.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#E.coli cells transformed with the indicated plasmids were grown either to mid-exponential phase (OD600nm ~ 1.0 for endpoint assays) or to the indicated optical densities.<br/><br />
#Samples were taken and spun down (3min, 4000 rpm, 4°C).<br/><br />
#Growth medium was removed and cell pellets frozen (-80 °C)<br/><br />
#Cell pellets were thawed on ice and resupended in equal volumes extraction buffer.<br/><br />
#Suspensions were incubated on ice for 10min and subsequently sonicated (3X 10” pulses) with ice cooling in between.<br/><br />
#Suspensions were spun down (10min, 14000 rpm, 4°C).<br/><br />
#Aliquots were mixed with 5X sample buffer and boiled (5min 95°C).<br/><br />
<br />
: 25mM Tris pH 8.0; 0.1%9v/v) NP40; 5 mM EDTA; 50 mM NaCl; protease inhibitor cocktail (Benzamidine; PMSF; Leupeptin).<br />
<br />
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<br/><br/></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/ProtocolsTeam:KU Leuven/Protocols2013-10-29T01:23:12Z<p>Veerledewever: </p>
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<h3>Index</h3><br />
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<a href="#Aphid population preference">Aphid population preference</a><br><br />
<a href="#Aphid mobility experiment">Aphid mobility experiment</a><br><br />
<a href="#Chemically competent E.coli cells: CaCl2 method">Chemically competent E.coli cells: CaCl2 method</a><br><br />
<a href="#Chemically competent E.coli cells: Inoue method">Chemically competent E.coli cells: Inoue method</a><br><br />
<a href="#Colony PCR for Streptomyces">Colony PCR for Streptomyces</a><br><br />
<a href="#Digestion and ligation">Digestion and ligation</a><br><br />
<a href="#DNA extraction from agarose gels">DNA extraction from agarose gels</a><br><br />
<a href="#Grow electrocompetent cells">Grow electrocompetent cells</a><br><br />
<a href="#Isolation of plasmid DNA from E. coli (mini prep)">Isolation of plasmid DNA from E. coli (mini prep)</a><br><br />
</div><br />
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<div class="span6 greytext"><br />
<br />
<a href="#PCR clean-up">PCR clean-up</a><br><br />
<a href="#PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer">PCR Protocol for Taq DNA Polymerase</a><br><br />
<a href="#Plasmid DNA isolation">Plasmid DNA isolation</a><br><br />
<a href="#Predator attraction to MeS induced aphid infested plants">Predator attraction to MeS induced aphid infested plants</a><br><br />
<a href="#Predator attraction to Methyl Salicylate">Predator attraction to Methyl Salicylate</a><br><br />
<a href="#qRT-PCR Protocol">qRT-PCR Protocol</a><br><br />
<a href="#Root measurements">Root measurements</a><br><br />
<a href="#Headspace GC">Headspace GC</a><br><br />
<a href="#GC-MS analysis">GC-MS analysis</a><br><br />
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<a id="Aphid population preference"> </a><br />
<h3 class="bg-yellow">Aphid population preference</h3><br />
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<b>Aim</b>: To determine the effect on aphid population on Methyl Salicylate (MeS) induced plants.<br />
<br/> <br/><br />
Experimental setup: We induced five plants per concentration<br />
*Plants: Small potted paprika plants in two ways: via the root or byspraying; 60 in total. Paprika plants don’t make MeS naturally but do produce salicylic acid. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml ,0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 97% pure ethanol or water. <br />
<br><br />
<b>Procedure</b><br />
<br />
Induction requires 48h<br />
*Induction via the roots<br />
#Uproot the plant, clean off the dirt with water because compost can interfere with the uptake of MeS<br />
#Wipe off the water before placing the roots in a cup with the desired concentration for 10 minutes. MeSA was first suspended in ethanol before being further diluted to the desired concentration in 50ml water<br />
#Re-pot the plant and divide the remainder of the MeSA solution amongst the 5 plants <br />
[[File:Rootinduction.png|200px|Root induction]]<br />
<br />
*Induction via the leaf<br />
#The desired concentrations were diluted in 15ml ethanol, resulting in 3ml per plant. Ethanol has been shown to have no plant induction properties<br />
#Each leaf of the plant is sprayed above and underneath<br />
[[File:Leafinduction.png|200px|Leaf induction]]<br />
<br/><br />
*Place aphids on MeS induced plants 48h post-induction<br />
#Place 15 green peach aphids on the head of each induced plant<br />
#Take the smallest aphids present on the aphid-infested leaf. To be sure that it is a first generation aphid<br />
#Place each plant in a separate net<br />
#Plants of the same concentration, induced by spraying or via the roots, are placed in the same row; 10 plants per row. The rows are roughly 1 metre apart<br />
* Counting aphids on day 7<br />
#Following a form, count how many aphids are on Cotyledon, separate true leafs and the head of the induced plants.<br />
#See <a href"> results</a><br />
#There is a possibility of contamination while placing the aphids on the plant, meaning that older aphids and/or flying aphids crawled onto the plant by accident. These were removed from the plant so that the amount of aphids on day 10 would not be tainted<br />
<br />
[[File:Howtocountaphids.png|300px|How to count aphids]]<br />
<br />
* Counting aphids on day 10<br />
#We count the aphids for a second time to allow the second generation to develop. This way we investigate whether MeS has an effect on the aphid’s behaviour. That they are encouraged to leave the plant, reproduce less or generate mobile (flying) aphids. <br />
#The head of the plant is where the majority of the secondary metabolites gather; hence we expect to see an effect on the distribution of the aphid population.<br />
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<h3 class="bg-yellow">Aphid mobility experiment</h3><br />
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<br/><b>Aim</b>: To determine the effect of EBF on aphid mobility.<br />
<br/> <br/><br />
<b>Experimental setup</b>: Leaves infested with aphids were divided in two glass containers (not sealed) randomly (n=3; 3 leaves per group). After 1h plates with bacteria, either control (BL21) or BL21 with EBF construct, were put under each leaf.<br />
<br />
<br><br />
<b>Measurement</b>: Aphids that were on the top of each leaf were counted at start and at each time point during the experiment.<br/><br />
At each time point the amount of aphids moving on the leaf were also counted. The time points used were 0, 50, 150 and 200 minutes.<br />
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<a id="Chemically competent E.coli cells: CaCl2 method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: CaCl2 method</h3><br />
</div><br />
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<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells!''' <br\><br />
'''Do not shock freeze (liqN2) – just transfer from ice to -80°C!''' <br\><br />
'''Work sterile!'''<br />
<br />
#Inoculate '''3 ml''' growth medium with your cells of choice ('''DH5alpha''' or '''TOP10''' for plasmid maintenance & cloning)<br />
#Grow overnight at '''37°C''' with sufficient aeration<br />
#Inoculate '''100 ml LB''' with '''1 ml''' of overnight culture<br />
#Grow at '''37°C''' to an OD 600nm of approx '''0.5 to 0.8''' (usually '''2-3 hrs''')<br />
#Centrifuge cells ('''3700-4000 rpm 4°C 12 min''' – sterile 50ml tube)<br />
#Resuspend pellet on ice with FSB to '''15 ml''' (cold) for each '''100 ml''' pellet<br />
#Incubate cells '''10 min''' on ice<br />
#Centrifuge cells ('''3700 – 4000 rpm 4°C 10 min''')<br />
#Re-suspend pellet on ice in '''4-8 ml''' FSB (cold) for each '''100 ml pellet'''<br />
#Aliquot cells appropriately ('''200-400 µl aliquots''') and freeze aliquots at '''-80°C'''<br />
<br><br />
<b>Buffers and solutions</b><br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Growth medium<br />
**LB 25 g/l<br />
*Frozen Storage Buffer (FSB)<br />
**10 mM Potassium Acetate<br />
**10% glycerol<br />
**10 mM KCl<br />
**50 mM CaCl2<br />
**Check pH – must be around 6.2 – if need be adjust with AcAc (HCl) or KOH<br />
**Buffer should be filter-sterilized (0.45 micrometer filter)<br />
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<a id="Chemically competent E.coli cells: Inoue method"> </a><br />
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<h3 class="bg-yellow">Chemically competent <i>E.coli</i> cells: Inoue method</h3><br />
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<br />
<b>Procedure</b><br />
<br />
'''Perform every action on ice – also when resuspending your cells.'''<br/><br />
'''Work sterile'''<br/><br />
#Pick a single colony from a freshly transformed plate (after overnight growth '''at 37 °C''')<br />
#Transfer the colony to '''25 ml growth medium''' in a sterile '''250 ml''' erlenmeyer<br />
#Incubate the culture '''at 37°C''' for '''6 – 8 hrs''' under vigorous shaking ('''250 – 300 rpm''')<br />
#Prepare '''3 1L flasks''' with '''250 ml growth medium''' in each<br />
#Inoculate the flasks with 10, 4 or 2 ml of the dayculture -> you create 3 different starting optical densities.<br />
#Incubate the cultures at '''18-22°C overnight''' under moderate shaking ('''180 – 220 rpm''')<br />
#Monitor the '''OD600nm''' until it reaches '''0.55'''<br />
#Place cells in an ice-water bath to cool them down quickly (-> swirl occasionally, keep them in for approx 10 min) <br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''80 ml icecold inoue transformation buffer'''<br />
#Centrifuge cells '''at 4°C for 10 min at 2500 g'''<br />
#Pour off supernatant – make sure all remaining droplets are removed<br />
#Resuspend gently (swirl !) in '''20 ml icecold inoue transformation buffer'''<br />
#Add '''1.5 ml 100% DMSO''' – mix by swirling<br />
#Store whole on ice for approx '''10 minutes'''<br />
#Aliquot as quickly as possible '''100 – 200 µl aliquots''' into '''1.5 ml tubes''' (precooled on ice) and snapfreeze them into a liquid N2 bath<br />
<br />
<b>Buffers and solutions</b><br />
<br />
*Growth medium<br />
*Inoue transformation buffer<br />
{| class="wikitable"<br />
| '''Reagent''' || '''Final concentration (mM)''' || '''Amount per liter''' <br />
|-<br />
| MnCl2 || 55 || 10.88 g (from MnCl2*4H2O) <br />
|-<br />
| CaCl2 || 15 || 2.20 g (from CaCl2*2H2O) <br />
|-<br />
| KCl || 250 || 18.65 g (from KCl) <br />
|-<br />
| PIPES || 10 || 20 ml (from 0.5M stock solution) <br />
|-<br />
| H2O || to 1 liter || <br />
|}<br />
Filter sterilize with a 0.45 µm nalgene filter<br />
<br />
[https://static.igem.org/mediawiki/2013/1/1b/Risk_assessment_use_of_pH_electrode_and_preparation_of_buffers_KULeuven.pdf Risk assessment for pH electrode and preparation of buffers]<br />
*Stock 0.5 M PIPES (piperazine-1,2-bis[2-ethanesulfonic acid]) pH 6.7<br />
**Dissolve 15.1 g PIPES in 80ml MilliQ H2O<br />
**Adjust pH to 6.7 with 5M KOH<br />
**Bring volume to 100 ml with MilliQ H2O<br />
**Filter sterilize with a 0.45 µm nalgene filter<br />
**Aliquot (5 times) and store at -20°C<br />
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<a id="Colony PCR for Streptomyces"> </a><br />
<h3 class="bg-yellow">Colony PCR for <i>Streptomyces</i></h3><br />
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<b>Pretreatment of ''Streptomyces''</b><br />
<br />
Because of the fact that ''Streptomyces'' are Gram-positive bacteria with a thick peptidoglycan layer, we performed 4 ways to pretreat the cells for colony PCR (all pretreatments gave positive results in the end):<br />
*microwave ''Streptomyces'' for 4 mins<br />
*mix ''Streptomyces'' with water and 0.2% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with 1% SDS, microwave for 4 mins<br />
*mix ''Streptomyces'' with TE buffer, 0.2% SDS, microwave for 4 mins<br />
<br><br />
<b>PCR mixture</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br />
{| class="wikitable"<br />
| '''Components''' || '''Amount''' <br />
|-<br />
| 2x fusion master mix (add in the end) || 25 µl <br />
|-<br />
| forward primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| reverse primer (final conc. 0.5 µM) || 1.25µl (of 20 µM stock) <br />
|-<br />
| template DNA || 1 µl <br />
|-<br />
| DMSO (recommended for high GC content) || 1.5 µl <br />
|-<br />
| H2O (PCR certified, no contamination) || add to final volume of 50µl <br />
|}<br />
'''Keep tubes on ice at all times!''' <br/><br />
'''Be sure to put Phusion Master Mix immediately back at -20!'''<br />
<br />
<b>Cycling instruction</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| 1 || 95°C || 6'<br />
|-<br />
| 2<br/> cycle 29x || 95°C<br/>55°C<br/>72°C || 30"<br/>30"<br/>45"<br/><br />
|-<br />
| 3 || 72°C || 10'<br />
|-<br />
| 4 || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<a id="Digestion and ligation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Digestion and ligation</h3><br />
</div><br />
</div></html><br />
<b>Consumables and equipment</b><br />
<br />
* Restriction enzymes (EcoRI, Xbal, Spel, Pstl), NEBuffer 2.1<br />
* 10x T4 DNA ligase Reaction Buffer, T4 DNA Ligase<br />
* Keep all enzymes on ice; make sure buffers have no precipitation<br />
* H20<br />
* Small PCR Tubes or eppendorfs<br />
* 2 µl, 200 µl pipette tips<br />
* Destination plasmid as purified DNA<br />
* Upstream and downstream part as purified DNA<br />
* 2 µl and 20 µl pipette<br />
* Heat block 37° and 80°C<br />
* Timer<br />
* Rack for small PCR tubes<br />
* -20°C freezer + freeze box<br />
<br><br />
<b>Digestion</b><br />
<br />
* Mark PCR tubes or eppendorfs<br />
a. U= upstream part : E + S restriction enzymes<br />
b. D= downstream part : X + P restriction enzymes<br />
c. P= plasmid (destination) : E + P restriction enzymes<br />
d. NB: if only one part for insertion insert I= Insert : E + P restriction enzymes<br />
* In each tube 500 ng DNA for digestion + H20 until total volume is 43 µl<br />
* Add 5 µl of NEBuffer 2.1 to each tube<br />
* Add 1 µl of first restriction enzyme<br />
* Add 1 µl of the second restriction enzyme '''TOTAL VOLUME = 50 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at 37°C for 20 min. (officially 15 min)<br />
* Incubate at 80°C for 20 min.<br />
a. OPTIONAL: run 10-20 µl on 1% agarose gel and look for expected bands as confirmation<br />
b. OPTIONAL: store at -20°C or proceed to ligation immediately<br />
<br><br />
<b>Ligation</b><br />
<br />
* Add 13 µl of H2O to a 200 µl PCR tube or eppendorf<br />
* Add 2 µl of each part you want to ligate<br />
* Add 2 µl of 10X T4 DNA Ligase Reaction Buffer to the tube<br />
* Add 1 µl of the T4 DNA Ligase to the tube '''TOTAL VOLUME = 20 µl'''<br />
* Mix well by flicking each tube<br />
* Incubate at room temperature for 10 min<br />
a. Incubate at 80°C for 20 min. <br />
b. Store the ligation mix at -20°C or proceed immediately to the transformation step.<br />
<br />
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<a id="DNA extraction from agarose gels"> </a><br />
<h3 class="bg-yellow">DNA extraction from agarose gels</h3><br />
</div><br />
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(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br />
<br><br />
<b>Procedure</b><br />
<br />
#Excise DNA fragment/solubilize gel slice: take a clean scalpel to excise the DNA fragment from an agarose gel, remove all excess agarose. For each '''100mg of agarose gel < 2%''' add '''200µl buffer NTI''', for gels containing > 2% agarose, double the volume of buffer NTI. Incubate sample for '''5-10 min''' at '''50°C''', vortex the sample briefly every 2-3 min until the gel slice is '''completely''' dissolved.<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2ml) and load up to 700µl sample, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''700µl buffer NT3''' to the column, centrifuge for '''30s''' at '''11000g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1min''' at '''11000g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5ml microcentrifuge tube, add '''15-30µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000g'''.<br />
<br />
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<a id="Grow electrocompetent cells"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Grow electrocompetent cells</h3><br />
</div><br />
</div></html><br />
<br />
(source: adapted from openwetware.org) <br />
<br />
<br/><br />
<b>Materials</b><br />
<br />
*GYT (glycerol, yeast extract, tryptone)<br />
**10%(v/v) glycerol <br />
**0.125% (w/v) yeast extract <br />
**0.25% (w/v) tryptone <br />
<br />
*DI water<br />
*10% Glycerol<br />
<br />
<br />
<b>Special Equipment</b><br />
*Centrifuge<br />
*Ice water bath<br />
*Liquid nitrogen<br />
<br />
<br />
<b>Procedure</b><br />
<br />
Important: All steps in this protocol should be carried out aseptically<br />
<br />
*Inoculate: Prepare flask containing 5 ml of LB medium. Pick up a single colony of cells from plate (using a sterile toothpick) and swirl around inside flask. Incubate the culture overnight at 37°C with vigorous aeration (250 pm in a rotary shaker). <br />
<br />
*Dilute and incubate: Inoculate two aliquots of 495 ml of prewarmed LB medium in separate 2-liter flasks with 5 ml of the overnight bacterial culture. Incubate the flasks at 37°C with agitation (300 cycles/min in a rotary shaker). Measure the OD-600 every twenty minutes (this step will take around 1.5-2 hrs). (or judge the OD by eyes to avoid always taking the sample to disturb the growth as well as avoiding the contamination)<br />
<br />
*Rapidly cool culture: Once the OD-600 of the culture reaches 0.6-1.0 (Molecular Cloning recommends 0.4), rapidly transfer the flasks to the pre-made ice-water bath for 15-30 minutes. Swirl the culture occasionally to ensure that cooling occurs evenly. In preparation for the next step, place the centrifuge bottles in the ice-water bath as well. <br />
<br />
Note: After this point, do not let your cells warm up past 4°C '''always keep on ice'''<br />
<br />
Note: When harvesting cells by decanting, be very careful not to disturb the pellet-- this could result in a much lower yield. If necessary, aspirate instead or decant the supernatant. Ask someone to show you how to aspirate. Also, if the pellet seems loose, sometimes it is helpful to re-spin the cells down.<br />
<br />
*Centrifuge 1: Transfer the cultures to ice-cold centrifuge bottles. Harvest the cells by centrifugation at 1000 g (2500 rpm) for 15 minutes at 4°C. Decant the supernantant and resuspend the cell pellet in 20 ml of ice-cold 10% glycerol. Note: this should be done for each of the two 500ml cultures, i.e this is a 1:1 resuspension rather than a concentration by a factor of 2 BC. <br />
<br />
*Centrifuge 2 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 20 ml ice-cold 10% glycerol. <br />
<br />
*Centrifuge 3 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Decant the supernatant and resuspend the cell pellet in 10 ml ice-cold 10% glycerol.<br />
<br />
*Centrifuge 4 (10% glycerol): Harvest the cells by centrifugation at 1000 g for 20 minutes at 4°C. Carefully decant the supernatant and use a Pasteur pipette attached to a vacuum line to remove any remaining drops of buffer. <br />
<br />
*Resuspend in GYT: Resuspend in 1 ml ice cold GYT. This is best done by gently swirling rather pipetting or vortexing. <br />
<br />
*Test for arcing: Transfer 40 µl of the suspension to an ice-cold electroporation cuvette and test whether arcing occurs when an electrical discharge is applied. Place the cuvette in the holder attached to the machine. Go to option 4, Pre-set protocols; choose bacterial; choose the correct choice for your size cuvette, probably the first option for a .1 cm cuvette. If arcing occurs, wash the remainder of the cell suspension once more with ice-cold GYT medium to ensure that the conductivity of the bacterial suspension is sufficiently low (<5 mEq). (or check the pulse time, if the pulse time < 4, redo the wash, if the pulse time > 4, it's ok)<br />
<br />
*Storage: Store cells at -80°C until they are required for use. For storage, dispense 40 µl aliquots of the cell suspension into sterile, ice-cold .5 ml microcentrifuge tubes, drop into a bath of '''liquid nitrogen''' and transfer to a -80°C freezer. To remove the tubes from the liquid nitrogen bath, bring out into the hall along with a storage box, and pour the tubes and liquid nitrogen into the box. Once all the tubes are out, close the box most of the way and let the liquid run out into the hallway. Try not to do this in the very center of the walkway! <br />
<br />
*To use frozen cells: Remove an appropriate number of aliquots of cells from the -80°C freezer. Thaw the tubes on ice.<br />
<br />
<br />
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<a id="Isolation of plasmid DNA from E. coli (mini prep)"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Isolation of plasmid DNA from <i> E. coli</i> (mini prep)</h3><br />
</div><br />
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<br />
(source: NucleoSpin® plasmid - Macherey-Nagel)<br />
<br />
<br><br />
<b>Nanodrop protocol</b><br />
<br><br><br />
Nanodrop can be used to measure the DNA, RNA and protein <br />
Measure the concentration and purity of extracted DNA using absorbance (using the automated nanodrop machine!) <br />
<br />
Method:<br />
#Log onto computer and select Nanodrop program from the desktop (ND 1000) <br />
#To clean Nanodrop machine wipe pedestal and top and add 3 µl of water to nib of pedestal. Press blank. <br />
#Wipe the water off, to initialize/equalize the equipment add 3 μl of the elution buffer [EB] used in the sample and press blank. Set to DNA-50 for DNA. <br />
#Wipe to remove buffer and apply 3 μl of sample to nib. Press measure. <br />
#If dealing with multiple samples, clean the equipment with water at regular intervals (about every 10 samples). <br />
#After measurements, clean the equipment with 3 μl of water on the spectrometer and press blank. Wipe and log off. <br />
<br />
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<a id="PCR clean-up"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR clean-up</h3><br />
</div><br />
</div></html><br />
(source: NucleoSpin® Gel and PCR Clean-up - Macherey-Nagel) <br/><br />
<br />
This is used for PCR clean-up as well as DNA concentration and removal of salts, enzymes, etc. from enzymatic reactions (SDS<0.1%)<br />
#Adjust DNA binding condition: mix '''1 volume of sample''' with '''2 volumes of buffer NTI''' (eg. mix 100 µl PCR reaction and 200 µl buffer NTI).<br />
#Binding DNA: place a PCR clean-up column into a collection tube (2 ml) and load up to 700 µl sample, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube.<br />
#Wash silica membrane: add '''600µl buffer NT3''' to the column, centrifuge for '''30 s''' at '''11000 g''', discard flow-through and place the column back into the collection tube. Repeat the washing again.<br />
#Dry silica membrane: centrifuge for '''1 min''' at '''11000 g''' to remove '''buffer NT3''' completely. Make sure the spin column does not come in contact with the flow-through while removing it from the centrifuge and the collection tube.<br />
#Elute DNA: place the column into a '''new''' 1.5 ml microcentrifuge tube, add '''50 µl buffer NE''' and incubate at '''room temperature''' for '''1 min''', centrifuge for '''1 min''' at '''11000 g'''.<br />
<br />
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<a id="PCR Protocol for Taq DNA Polymerase with Standard Taq Buffer"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">PCR protocol for Taq DNA polymerase with standard Taq Buffer</h3><br />
</div><br />
</div></html><br />
<br />
<b>Reaction set up</b><br />
<br />
[https://static.igem.org/mediawiki/2013/7/7b/Risk_assessment_PCR_KULeuven.pdf Risk assessment for PCR]<br/><br />
'''We recommend assembling all reaction components on ice and quickly transferring the reactions to a thermocycler preheated to the denaturation temperature (95°C).'''<br />
{| class="wikitable"<br />
| '''Components''' || '''25 μl reaction''' || '''50 μl reaction''' || '''Final concentration'''<br />
|-<br />
| 10X Standard Taq Reaction Buffer || 2.5 µl || 5 µl || 1X<br />
|-<br />
| 10 mM dNTPs || 0.5 µl || 1 µl || 200 µM<br />
|-<br />
| 10 µM Forward Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| 10 µM Reverse Primer || 0.5 µl || 1 µl || 0.2 µM (0.05–1 µM) <br />
|-<br />
| template DNA || variable || variable || <1,000 ng <br />
|-<br />
| Taq DNA Polymerase || 0.125 µl || 0.25 µl || 1.25 units/50 µl PCR<br />
|-<br />
| Nuclease-free water || to 25 µl || to 50 µl || <br />
|}<br />
<br />
Notes: Gently mix the reaction. Collect all liquid to the bottom of the tube by a quick spin if necessary. Overlay the sample with mineral oil if using a PCR machine without a heated lid.<br />
Transfer PCR tubes from ice to a PCR machine with the block preheated to 95°C and begin thermocycling.<br />
<br />
<b>Thermocyclingconditions for a routine PCR</b><br />
{| class="wikitable"<br />
| '''Step''' || '''Temperature''' || '''Time'''<br />
|-<br />
| Initial denaturation || 95°C || 30"<br />
|-<br />
| 30 cycles || 95°C<br/>48-65°C<br/>68°C || 15-30"<br/>15-60"<br/>1min/kb<br />
|-<br />
| Final extension || 68°C || 5'<br />
|-<br />
| Hold || 12°C || infinite/hold<br />
|}<br />
<br />
<br/><br />
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<a id="Plasmid DNA isolation"> </a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Plasmid DNA isolation</h3><br />
</div><br />
</div></html><br />
<br />
<b>Procedure</b><br />
<br />
[https://static.igem.org/mediawiki/2013/8/8f/Risk_assessment_Plasmid_DNA_Purification_kit_KULeuven.pdf Risk assessment for plasmid DNA purification kit]<br />
#Bring '''1.5 ml culture''' in an eppendorf, centrifuge for '''1 min with maximum speed'''<br />
#Pour away the supernatant<br />
#Bring another '''1.5 ml culture''' into the same eppendorf, centrifuge for '''1 min''' and pour away supernatant<br />
#Resuspend the pellet with '''200µl GTE-solution''' we made earlier<br />
#Add '''4 µl RNase A (10mg/ml)'''<br />
#Add '''400 µl premade solution''' (contains 0.2M NaOH and 1%SDS in sterile water)<br />
#Mix them well, place on ice for '''5 min'''<br />
#Add '''300 µl ice cold 7.5 M ammonium acetate''', vortex for 10 s, place on ice for '''5 mins'''<br />
#Centrifuge for '''5min with 13000 rpm'''<br />
#Bring the supernatant into a new eppendorf<br />
#Centrifuge this supernatant for a second time ('''5 min, 13000 rpm''') and bring the supernatant in a new eppendorf<br />
#Add isopropanol to the supernatant (60% in volume of the supernatant), leave '''at room temp. for 5 min'''<br />
#Centrifuge for '''10 min with 13000 rpm''', immediately remove the supernatant, keep the transparent pellet in the tube, put the tube upside down on a tissue to dry it<br />
#Add '''1 ml of cold 70% ethanol''' to the pellet, invert 5 times<br />
#Centrifuge '''3 min with 13000 rpm'''<br />
#Remove supernatant, the droplet on the tube wall can be removed by tissue<br />
#Let the pellet dry<br />
#Add '''50 µl elution buffer''' (or sterile water) to the pellet<br />
<br />
<b>Buffers and Solutions</b><br />
<br />
*GTE-buffer<br />
**50 mM glucose<br />
**25 mM Tris-Cl (pH 8.0)<br />
**10 mM EDTA<br />
**4 mg/ml lysozyme<br />
<br />
*IPTG stock solution<br />
**238 mg in 10 ml AD<br />
**Filter sterilize<br />
**Split into 1 ml aliquots<br />
**Store in -20 freezer<br />
<br />
Final concentration/work concentration in agar plates = 0.1mM – 1 mM <br/><br />
Sigma recommends 0.2 mM for blue-white screening <br/><br />
Thermo Scientific recommends 0.1 mM<br />
<br />
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<a id="Predator attraction to MeS induced aphid infested plants"></a><br />
<h3 class="bg-yellow">Predator attraction to MeS induced aphid infested plants</h3><br />
</div><br />
</div></html><br />
<b>Aim</b>: To determine the effect on aphid predators’ attraction to Methyl Salicylate (MeS) induced plants.<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Macrolophus</i> adults<br />
*Plants: Small potted MeS induced paprika plants in two ways: via the root or via the leaf, 60 in total. <br />
*Concentration: 1ng/ml, 0,8ng/ml, 0,4ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)). <br />
<br><br />
<b>Cafetaria model</b><br />
<br />
[[File:Cafetariamodel.png|100px|Leafinduction]] [[File:Cafetariamodel1.png|200px|Leafinduction]]<br />
<br />
<br/><br />
In the cafeteria model shown above, each concentration will be placed, plus the control, randomly in a circle. The plants should not be close to the edge of the cage in which the experiments are carried out. The plants should be placed in rotation with every repeat of the set-up to eliminate other factors. <br />
*Releasing the <i>Macrolophus</i>:<br />
#10 <i>Macrolophus</i> (adult) are shaken out of the pot and placed in the middle of the cafetaria model (see picture) <br />
#We release around 50 <i>Macrolophus</i> (adult) per set-up<br />
[[File:Macrolophus2.png|200px|Leafinduction]]<br />
*Counting the <i>Macrolophus</i>: <br />
# After every 45 min from the moment we released the <i>Macrolophus</i>, the amount per plant is counted and recorded<br />
# The <i>Macrolophus</i> are then shaken off the plant back into the middle of the circle so that they can make their choice again<br />
# We will take 3 recordings<br />
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<a id="Predator attraction to Methyl Salicylate"></a><br />
<div class="span12"><br />
<h3 class="bg-yellow">Predator attraction to methyl salicylate</h3><br />
</div><br />
</div></html><br />
<b>Aim</b>: To determine a working concentration of methyl salicylate (MeS) concentration which attracts predators .<br />
<br/><br/><br />
Experimental setup: We continue with the same plants used in the experiments to determine aphid population preference (see above), this way we try to create an as close as possible in vivo situation. The predators were subjected to choice experiment - cafetaria model. <br />
*Predators: <i>Adalia bipunctata</i> (ladybugs): adult and larvae <br />
*Concentration: 1000ng/ml, 100ng/ml, 10ng/ml, 1ng/ml, 0.1ng/ml, 0.01ng/ml of MeS (Sigma-Aldrich, ReagentPlus®, ≥99% (GC)) in 96% ethanol. <br />
<br><br />
<b>Biobest setup</b><br />
<br />
[[File:Biobestsetup.png|200px|Biobeststetup]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure EtOH). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
<br />
<b>pcfruit setup</b><br />
<br />
[[File:Y-tubeolfactormeter.png|200px|Leafinduction]]<br />
<br/><br />
The predators were subjected to a choice experiment between one of the MeS concentrations and a control (pure hexane or paraffin oil). <br />
<br/>We repeated each dilution three times with different ladybugs. <br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>These experiments were performed in a chemical safety cabinet to prevent the distribution of MeS in the air.<br />
<br />
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<a id="qRT-PCR Protocol"></a><br />
<h3 class="bg-yellow">qRT-PCR protocol</h3><br />
</div><br />
</div></html><br />
General experiment: 3 biological repeats of stationary phase cultures 16 hrs growth.<br />
<br />
<b>Sample preparation</b><br />
<br />
# Inoculate fresh LB plate with the strain harbouring the MS brick <br />
# Isolate plasmid DNA, MS brick<br />
# Nanodrop plasmid (minimum concentration 20 ng/µl) = 155 ng/µl <br />
# Design primers for pcha/pchb and for bsmt1 using Primer express. Primer sequence can be found in document “Genes_qPCR_primers_IC.docx”.<br />
# Inoculate E. Coli with Methylsalicylate brick (MIT 2006) on LB plate.<br />
# Add 5 µl of Kanamicin (50 mg/ml) and 10 µl of IPTG (100 mM) to 5 ml of LB medium (tubes).<br />
# Incubate 16h at 37 °C under shaking conditions.<br />
# Measure OD of 3 cultures.<br />
# Account for differences in OD: ex. If you have an OD of 1 take 1 ml of this sample, if you have an OD of 0,8 take 1,2 ml.<br />
# Add 1/5 volume of stopsolution (95% ethanol, 5% phenol).<br />
# Freeze 5 minutes in liquid Nitrogen.<br />
# Centrifuge needed amount of cells.<br />
<br />
<b>mRNA isolation</b><br />
<br />
For mRNA isolation use the Promega SV total RNA isolation kit ([http://be.promega.com/resources/protocols/technical-manuals/0/sv-total-rna-isolation-system-protocol/?origUrl=http%3a%2f%2fwww.promega.com%2fresources%2fprotocols%2ftechnical-manuals%2f0%2fsv-total-rna-isolation-system-protocol%2f Promega SV total RNA isolation protocol])<br />
# Section 8.C (Isolation of RNA from Gram-positive and Gram-negative bacteria)<br />
# Section 4.E (RNA Purification by Centrifugation) We did not do step 4 and 5 (addition of DNase) but instead used the DNase protocol of [http://products.invitrogen.com/ivgn/product/AM2238 Ambion Turbo DNase]<br />
For 100 µl of RNA:<br />
## Add 10 µl 10x buffer. Add 1 µl Turbo DNase and mix gently.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 1 µl of Turbo DNase.<br />
## Put on 37 °C for 20 to 30 min.<br />
## Add 20 µl of DNase inactivation reagent and mix well.<br />
## Put on room temperature for 5 min. Mix occasionally by flicking the tube<br />
## Centrifuge on 10000 g for 2 min.<br />
## Transfer the RNA to a new tube.<br />
<br />
<b>Create cDNA</b><br />
<br />
Use the Fermentas Revert aid H kit ([http://www.thermoscientificbio.com/reverse-transcription-rtpcr-rtqpcr/revertaid-h-minus-first-strand-cdna-synthesis-kit/ Fermentas Revert aid H protocol])<br />
The protocol for RT-PCR (I. First Strand Synthesis) was followed<br />
<br />
<b>qPCR</b><br />
<br />
qPCR kit was not yet decided.<br />
<br />
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<a id="Root measurements"></a><br />
<h3 class="bg-yellow">Root measurements</h3><br />
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</div></html><br />
<b>Aim</b>: To determine whether the different induction methods or the Methyl Salicylate (MeS) had an effect on plant growth.<br />
<br/><br/><br />
Experimental setup: We measured the roots of the same plants used in the experiments to determine aphid population and predator preference (see above). <br />
<br/><br />
[[File:Uppot.png|200px|Uppot]]<br />
[[File:Wash.png|200px|Root wash]]<br />
[[File:Dryandweigh.png|200px|Dry roots]]<br />
<br />
<br/><br />
The plants were cut off at the bottom of the stem and removed from the pot. <br />
<br/>After most of the dirt has been removed, they were carefully washed to remove the rest of the dirt.<br />
<br/>After using the ladybugs, we collected them in a different tube and didn’t use them again. <br />
<br/>The roots were then dried and weighed. <br />
<br><br />
[[File:Rootmeasurement.png|200px|Root measurement]]<br />
[[File:Rootmeasurement1.png|200px|Root measurement 1]]<br />
<br/>The roots were then measured and recorded.<br />
<br />
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<a id="Headspace GC"></a><br />
<h3 class="bg-yellow">Headspace GC</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
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<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with 0 or 0.1 mM of salicylic acid and left to grow for 7 hours.<br />
#Cultures were chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filterstelized (0.22µm).<br />
#Salt was added to 5 ml of this filtersterilized supernatant<br/><br/><br />
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<b>Gass chromatography:</b><br />
Samples were analyzed with a calibrated Autosystem XL gas chromatograph with a headspace sampler (HS40; Perkin-Elmer, Wellesley, Mass.) and equipped with a CP-Wax 52 CB column (length, 50 m; internal diameter, 0.32 mm; layer thickness, 1.2 μm; Chrompack; Varian, Palo Alto, Calif.). Samples were heated for 16 min at 72°C in the headspace autosampler. The injection block and flame ionization detector (FID) temperatures were kept constant at 180 and 250°C, respectively; helium was used as the carrier gas. The oven temperature was 75°C held for 6 min and then increased to 110°C at 25°C min−1 and held at 100°C for 3.5 min. Results were analyzed with Perkin-Elmer Turbochrom Navigator software.<br />
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<h3 class="bg-yellow">GC-MS analysis</h3><br />
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<br/><b>Aim</b>: To detect the production of MeS by our <i>E. coli</i>.<br />
<br/><br/><br />
<b>Sample preparation</b>:<br/><br />
#At day 0: a preculture was grown at 37°C overnight (BL21 or BBa_K1060003)<br />
#The preculture was used to inoculate 500µl into 50ml of fresh LB medium supplemented with or without 0.1 mM salicylic acid and left to grow for overnight. Samples were induced with 0.2 mM IPTG 6 hours post inoculation.<br />
#Cultures were then chilled on ice and put at 4°C<br />
#Bacterial cells were removed by centrifugation (10’, 4000g, 4°C) and then filter sterilized (0.22µm).<br />
#2 ml of this filtersterilized supernatant was then extracted with 1 ml of hexane<br />
#Extractions were done in glass tubes with rigorous vortexing for 10 minutes<br />
#The upper phase was transferred to a new glass vial<br />
#The extraction was repeated twice (total of 3 extractions with 1 ml of hexane)<br />
#The resulting ± 3 ml of extract were then reduced by evaporation under a nitrogen flow and redissolved in 50 µl of hexane.<br />
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<b>Gass chromatography:</b><br />
GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
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<h3 class="bg-yellow">Protein extraction</h3><br />
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<br/><b>Aim</b>: To extract proteins from our <i>E. coli</i> for SDS-PAGE analysis.<br />
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<b>Sample preparation</b>:<br/><br />
E.coli cells transformed with the indicated plasmids were grown either to mid-exponential phase (OD600nm ~ 1.0 for endpoint assays) or to the indicated optical densities.<br/><br />
Samples were taken and spun down (3min, 4000 rpm, 4°C).<br/><br />
Growth medium was removed and cell pellets frozen (-80 °C)<br/><br />
Cell pellets were thawed on ice and resupended in equal volumes extraction buffer.<br/><br />
Suspensions were incubated on ice for 10min and subsequently sonicated (3X 10” pulses) with ice cooling in between.<br/><br />
Suspensions were spun down (10min, 14000 rpm, 4°C).<br/><br />
Aliquots were mixed with 5X sample buffer and boiled (5min 95°C).<br/><br />
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: 25mM Tris pH 8.0; 0.1%9v/v) NP40; 5 mM EDTA; 50 mM NaCl; protease inhibitor cocktail (Benzamidine; PMSF; Leupeptin).<br />
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<br/><br/></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:50:43Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>You are here!</p><br />
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
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We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
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We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
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<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
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<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
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<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
<br/> <br/><br />
Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
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<h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3><br />
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We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
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<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
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<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
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<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
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<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
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<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/><br />
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<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/><br />
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<p align = "justify">This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/><br />
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<p align = "justify">FBA analysis predicted LB medium conditions to be beneficial for both <i>E.coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover, a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a <b>trade off<b/> between the two. This linear correlation is present not only for LB medium conditions but also for minimal medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions. <b>These trade-offs are not only present <I>in silico</I> but we also found them <I>in vivo</I></b> in the choice between methyl salicylate production and growth rate. Finally, also our GC-MS analysis fits with this modelling, in sofar that when salicylate was added to the medium, we not only observed a reduced growth rate but also a distinct MeS production peak.<br/><br />
These results not only show correlation between wetlab and modelling data but also suggest that cellular level modelling can result in colony wide effects (as observed in the delayed growth). <br />
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<h3 class="bg-oscillator">References</h3><br />
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Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:38:42Z<p>Veerledewever: </p>
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
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<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"><br />
We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
<br />
We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
</p><br />
</p><br />
</div><br />
</div><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
<br />
<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
<br/> <br/><br />
Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
<br />
<br />
<br />
</p><br />
</div><br />
</div><br />
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<div id="header" class="row-fluid"><br />
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<h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"><br />
We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
<br />
<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
</p><br />
<br />
<p align = "justify"><br />
<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
</p><br />
<br />
<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
<br />
<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
<br />
<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/><br />
<br />
<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/><br />
<br />
<p align = "justify">This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/><br />
</div><br />
</div><br />
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<h3 class="bg-oscillator">Conclusion</h3><br />
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<p align = "justify">FBA analysis predicted LB medium conditions to be beneficial for both <i>E.Coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover, a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present not only for LB medium conditions but also for minimal medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions. These trade-offs are not only present <I>in silico</I> but we also found them <I>in vivo</I> in the choice between methyl salicylate production and growth rate. Finally, also our GC-MS analysis fits with this modelling, in sofar that when salicylate was added to the medium, we not only observed a reduced growth rate but also a distinct MeS production peak. <br />
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</div><br />
</div><br />
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<h3 class="bg-oscillator">References</h3><br />
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<p align = "justify"><br />
Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
<br />
</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:29:02Z<p>Veerledewever: </p>
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<a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA"><br />
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<h3>Flux Balance Analysis</h3> </a><br />
<p>You are here!</p><br />
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"><br />
We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
<br />
We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
</p><br />
</p><br />
</div><br />
</div><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
<br />
<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
<br/> <br/><br />
Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
<br />
<br />
<br />
</p><br />
</div><br />
</div><br />
<br />
<br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"><br />
We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
<br />
<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
</p><br />
<br />
<p align = "justify"><br />
<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
</p><br />
<br />
<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
<br />
<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
<br />
<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/><br />
<br />
<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/><br />
<br />
<p align = "justify">This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/><br />
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<h3 class="bg-oscillator">Conclusion</h3><br />
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<p align = "justify">FBA analysis predicted LB medium conditions to be beneficial for both <i>E.Coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover, a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present not only for LB medium conditions but also for minimal medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions. These trade-offs are not only present <I>in silico</I> but we also found them <I>in vivo</I>. <br />
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<h3 class="bg-oscillator">References</h3><br />
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<p align = "justify"><br />
Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
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</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:18:37Z<p>Veerledewever: </p>
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<a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA"><br />
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<h3>Flux Balance Analysis</h3> </a><br />
<p>You are here!</p><br />
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
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We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
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We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
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<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
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<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
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<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
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Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
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<h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3><br />
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We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
</p><br />
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<p align = "justify"><br />
<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
</p><br />
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<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
<br />
<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
<br />
<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/><br />
<br />
<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/><br />
<br />
<p align = "justify">This figure shows how higher concentrations of added salicylate (0.1 mM) result in a longer lag phase. Higher levels of salicylate may lead to higher levels of S-adenosylmethionine (SAM) consumption, a co-substrate of the methyltransferase reaction producing methylsalicylate from salicylate. These higher consumption levels can in turn be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to salicylate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/><br />
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<h3 class="bg-oscillator">Conclusion</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify">LB medium conditions are predicted by FBA analysis to be beneficial for both <i>E.Coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present for the minimal and LB medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions.<br />
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<h3 class="bg-oscillator">References</h3><br />
</div><br />
</div><br />
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<p align = "justify"><br />
Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
<br />
</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:14:43Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>You are here!</p><br />
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
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<p align = "justify"><br />
We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
<br />
We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
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<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
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<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
<br />
<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
<br/> <br/><br />
Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
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</p><br />
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<h3 class="bg-oscillator">Results: what Did COBRA Do For us?</h3><br />
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<p align = "justify"><br />
We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
<br />
<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
</p><br />
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<p align = "justify"><br />
<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
</p><br />
<br />
<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
<br />
<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
<br />
<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This trade-off is reminiscent of our wet-lab growth curve results for the MeS brick.</p><br/><br />
<br />
<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br/><br />
<br />
<p align = "justify">This figure shows how higher concentrations of added salicylate result in a longer lag phase. If we assume that the production of SAM (S-adenosylmethionine), a common co-substrate of the methyltransferase reaction from salicylate to methylsalicylate, is important for cell metabolism, providing higher levels of salicylate would increase SAM consumption. However, high levels of salicylate may lead to higher levels of SAM. These in turn can be associated with higher homocysteine levels, a side-product that stays behind when the methylgroup has been transferred to the substrate. Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.</p><br/><br />
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<h3 class="bg-oscillator">Conclusion</h3><br />
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<p align = "justify">LB medium conditions are predicted by FBA analysis to be beneficial for both <i>E.Coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present for the minimal and LB medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions.<br />
<br />
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<h3 class="bg-oscillator">References</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
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<p align = "justify"><br />
Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
<br />
</p><br />
</div><br />
</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-29T00:08:21Z<p>Veerledewever: </p>
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<h3>Kinetic Parameters</h3><br />
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<p>BanAphids MeS production?</p><br />
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<h3 class="bg-oscillator">Flux Balance Analysis on Methyl Salicylate</h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
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<p align = "justify"><br />
We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
<br />
We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
</p><br />
</p><br />
</div><br />
</div><br />
<br />
<div id="header" class="row-fluid"><br />
<div class="span12"><br />
<h3 class="bg-oscillator">Matlab: COBRA Toolbox<h3><br />
</div><br />
</div><br />
<div class="row-fluid"><br />
<div class="span12 white"><br />
<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
<br />
<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
<br/> <br/><br />
Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
<br />
<br />
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We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
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<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
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<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br/><br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time. </p><br />
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<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/><br/><br />
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<p align = "justify">As can be seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. <br />
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This trade-off is reminiscent of our <a href="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg">growth curve results</a> for the MeS brick. <br />
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<img src="https://static.igem.org/mediawiki/2013/d/d5/BBa_K1060003.jpg"/><br />
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is consistent when taking into account the results from the wetlab since we observed lower growth curves for higher concentrations of added salicylate. If we assume that the production of SAM (S-adenosylmethionine), a common co-substrate of the methyltransferase reaction from salicylate to methylsalicylate, is important for cell metabolism, providing higher levels of salicylate would increase SAM consumption. However, high levels of salicylate may lead to higher levels of SAM which are in turn associated with higher homocysteine levels (a side-product that stays behind when the methylgroup has been transferred to the substrate). Increased homocysteine levels are toxic for <i>E. coli</i> strains (Tuite <i>et al.</i> which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.<br />
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<p align = "justify">LB medium conditions are predicted by FBA analysis to be beneficial for both <i>E.Coli</i> growth and the flux towards chorismate, an important precursor for methylsalicylate. Moreover a linear correlation between maximal predicted MeS flux and maximal growth rate exists, showing a trade off between the two. This linear correlation is present for the minimal and LB medium conditions and is qualitatively similar, showing a steeper correlation for LB medium conditions.<br />
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Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
Tuite, N. L.,Fraser, K. R.,O'Byrne, C. P.(2005)."Homocysteine toxicity in Escherichia coli is caused by a perturbation of branched-chain amino acid biosynthesis" Journal of bacteriology 187(13):4362-4371.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Modelling/Colony_LevelTeam:KU Leuven/Project/Modelling/Colony Level2013-10-28T23:47:39Z<p>Veerledewever: </p>
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<p align = "justify">To obtain an optimal effect of the released pheromones on aphids and the ladybugs, we designed an oscillating transcription factor network. This <b>oscillator</b> has two important features:</p><br />
<ol><li>The BanAphids use the oscillator to <b>regulate</b> their <b>production</b> of EBF and MeS.</li><br />
<li>The oscillator is used to <b>communicate</b> between cells, enforcing the oscillating rhythm onto the whole colony.</li><br />
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All BanAphids in the same colony, equipped with this oscillator, produce the same pheromone at the exact same time. This optimises the effect of the pheromones, resulting in a <b>more efficient aphid protection</b>!<br />
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<p align="justify">In this part we describe the design of an oscillator that could be useful in biological networks. We created a system that creates synchronized oscillations without depending heavily on the components used. We explain several necessities to obtain a synchronized oscillator, and how we managed to incorporate those within our network. </p><br />
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<p align="justify">For those who are not afraid of having a more mathematical view on our oscillator, we invite you to read our modelling article. Depending on the perspective this text is seen as 'very extensive' or 'very short and preliminary', but please decide for yourself.</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Team/AttributionsTeam:KU Leuven/Team/Attributions2013-10-28T23:42:39Z<p>Veerledewever: </p>
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<p>Working with other teams.</p><br />
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<p>Meet our most generous sponsors!</p><br />
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<p align="justify">We were able to participate in the iGEM competition thanks to <a href="http://bio.kuleuven.be/pf/functional_biology/functional-biology/">Prof. Joris Winderickx</a> and the <a href="http://www.kuleuven.be/bioscenter/">KU Leuven BioSCENTer</a>, the Center for Bio-Science, Bio-Engineering and Bio-Technology.<br/> BioSCENTer’s mission is to integrate expertise on biology and biomedicine; to stimulate interaction between experts and to foster cross-fertilization of disciplines. <br />
BioSCENTer wants to create visibility in research, teaching and valorisation by appropriate branding and scientific outreach. Sounds like a perfect match with iGEM to us ?! We are very grateful for the opportunity and hope BioSCENTer will keep up the iGEM vibe at the KU Leuven!</br></br></br></br><br />
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We did our molecular biology experiments in the Laboratory of Molecular and Synthetic Biology under supervision of Johan Robben and his lab members. We would like to thank all of them for the help and advice they've given us during the summer. They strongly believed in letting us do our own experiments! In the end, we are thankful for all the hands-on experience we gained this summer! <br/><br />
Thanks also to the <a href="http://www.biw.kuleuven.be/m2s/cmpg/">Centre of Microbial and Plant Genetics</a> and especially Elke Van Assche, David De Coster and Tine Verhoeven for their help and patience during our qPCR adventure.</p><br/><br />
<p align="justify">Another big thank you to the other lab on the second floor, the group of <a href="http://chem.kuleuven.be/en/research/bmsb/gmgroup/">Prof. Giovanni Maglia</a>, working on Nanopore devices, for their help and support in finding our way through a hall with identical doors with media, buffers, ... hiding behind them! <br/><br/><br/><br/><br/><br/><br />
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<p align="justify"> Finally, thanks to our <b>administrative supporters</b>! First and foremost to <a href="http://www.kuleuven.be/wieiswie/en/person/00001926">Cathy Hendrickx</a>, who helped us take care of our finances and made sure we were all booked on the right flights with the proper paperwork!</br><br />
Thanks also to <a href="http://www.kuleuven.be/wieiswie/en/person/00047610">Anouk Desmet</a>, an invaluable part of the iGEM selection committee, a rockstar in ISP organisation and our connection to the Medical Faculty.</br></br></br>Also <a href="http://http://www.kuleuven.be/wieiswie/nl/person/00062351">Liesbeth Van Meerbeek</a> deserves a big hurrah for continuously adapting the iGEM BioSCENTer website to our wishes, usually last moment and under time pressure .... </br></br></br></br></br></br></br><br />
Last but not least, we thank <a href="http://www.kuleuven.be/wieiswie/en/person/00001190">Mimi Deprez</a>. She facilitated access to the fancy poster printer (multiple times ...), is an important part of the iGEM selection committee and a steady and calm presence in the face of press deadlines and meetings. </br></br><br />
We couldn't have done it without them, so THANKS !!! </p><br/><br />
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<img src="https://static.igem.org/mediawiki/2013/e/ec/Jorisattributions.jpg" alt="Joris Winderickx"/><br />
<p> Joris Winderickx</p><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/Laboratory_of_molensynbio.JPG" alt="labo members"/><br />
<p> The laboratory of Molecular and Synthetic Biology.</p><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/Group_pic_Maglia.jpg" alt="Maglia lab members"/><br />
<p> The laboratory of Nanopore Devices.</p><br />
<img src="https://static.igem.org/mediawiki/2013/b/be/Cathy_adminsupport.jpg" alt="Cathy Hendrickx"/><br />
<p> Cathy Hendrickx</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/7f/Liesbeth_adminsupport.JPG" alt="Liesbeth Van Meerbeek"/><br />
<p> Liesbeth Van Meerbeek</p><br />
<img src="https://static.igem.org/mediawiki/2013/8/8c/Mimi_adminsupport.png" alt="mimi photo"/><br />
<p> Mimi Deprez</p><br />
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<p align="justify">We are thankful to professor <a href="http://www.kuleuven.be/wieiswie/nl/person/u0003363">Luc Anckaert</a> from the KU Leuven for supervising the ethical part of our project and also his contribution to the article on Hans Jonas in <a href="http://www.tertio.be/sitepages/index.php?page=abonneren_keuze">Tertio</a>. We would like to thank the Institute of Philosophy of the KU Leuven for their hospitality and the collaboration between this department and the KU Leuven iGEM team.</br><br />
Thanks to dr. <a href="http://www.kuleuven.be/wieiswie/nl/person/u0060200">Jan Baeten</a> and professor <a href="http://www.kuleuven.be/wieiswie/en/person/00012869">Dirk De Vos</a> from the Centre for Surface chemistry and catalysis for their help with the GC-MS and to <a href="http://www.kuleuven.be/wieiswie/en/person/00059705">Joaquin Christiaens</a> and professor <a href="http://www.kuleuven.be/wieiswie/en/person/00031931">Kevin Verstrepen</a> from the Centre of Microbial and Plant Genetics for their help with the headspace GC-FID.<br/><br />
We would like to thank professor <a href="http://www.kuleuven.be/optec/people/74-Prof-Kristel-Bernaerts">Kristel Bernaerts</a> from the department of Chemical Engineering for her kind advice towards the modelling team.<br/><br />
We are thankful to professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0015385">Johan Suykens</a> from the department of Electrical Engineering (ESAT), professor <a href="http://www.kuleuven.be/wieiswie/nl/person/00005764">Jan Degrève</a> from the department of Chemical Engineering and professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0006989">Dirk Roose</a> from the department of Computer Science for their useful advice on our oscillator model.</br><br />
We would like to thank <a href="http://www.kuleuven.be/wieiswie/nl/person/u0073220">Tim Tambuyzer</a> for his time to explain us the possibilities of motion tracking.</br><br />
We are thankful to dr. <a href="http://www.kuleuven.be/wieiswie/nl/person/u0038989">Joke Allemeersch</a> from Nucleomics Core for the information regarding the nCounter technology.</br><br />
We are grateful to professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0033548">Abram Aertsen</a> from the Centre of Microbiological and Food Technology for his expertise on implementation of networks in vivo.</br><br />
We would like to thank Dries Van Eyck for the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#3Dmodels" target="_blank">3D modelling pictures of DAHP synthase</a>.<br />
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<p align="justify">We were invited by the companies <a href="http://www.biobest.be/home/3">Biobest</a> and <a href="http://www.pcfruit.be/Homepage/22724/pcfruit">pcfruit</a> to perform experiments with aphids, their predators and plants. We would like to thank them very much that they've opened their laboratories for us, helped us perform the experiments and shared their knowledge.</p><br />
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First of all a big thanks to the <a href="https://2013.igem.org/Team:Calgary>present and past Calgary iGEM teams</a> for their brainstorming over a few Belgian beers and tips and tricks throughout the summer. We wish them luck for their regional jamboree !</br><br />
We are grateful to professor <a href="http://www.filosofie.science.ru.nl/">Hub Zwart</a>, professor of Philosophy at the faculty of science of the Radboud University Nijmegen, The Netherlands, for his contribution to one of the three articles, published in <a href="http://www.tertio.be/sitepages/index.php?page=abonneren_keuze">Tertio</a>, on ethical aspects of synthetic biology. Moreover, we thank him for his <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/Psychoanalytics">lecture and seminar</a> on September 26th 2013.<br/><br />
We are grateful to professor <a href="http://www.tilburguniversity.edu/webwijs/show/?uid=g.h.t.blans">Bert Blans</a>, emeritus professor at the School of Catholic Theology of the Tilburg University, The Netherlands for his contribution to the article on Hans Jonas in Tertio.<br/><br />
Thank you to Flore's dad for the artwork on our flyer and key-chain gadget - it will travel the world through iGEM.<br/><br />
The wiki team would like to thank Jan Luts for his help in designing the wiki page. If you also want an awesome site like this one, you can contact him via his <a href="http://www.linkedin.com/in/janluts">LinkedIn account</a>.</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Team/AttributionsTeam:KU Leuven/Team/Attributions2013-10-28T23:40:51Z<p>Veerledewever: </p>
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<p>They made the BanAphids.</p><br />
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<p>Working with other teams.</p><br />
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<h3>Sponsors</h3> </a><br />
<p>Meet our most generous sponsors!</p><br />
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<p align="justify">We were able to participate in the iGEM competition thanks to <a href="http://bio.kuleuven.be/pf/functional_biology/functional-biology/">Prof. Joris Winderickx</a> and the <a href="http://www.kuleuven.be/bioscenter/">KU Leuven BioSCENTer</a>, the Center for Bio-Science, Bio-Engineering and Bio-Technology.<br/> BioSCENTer’s mission is to integrate expertise on biology and biomedicine; to stimulate interaction between experts and to foster cross-fertilization of disciplines. <br />
BioSCENTer wants to create visibility in research, teaching and valorisation by appropriate branding and scientific outreach. Sounds like a perfect match with iGEM to us ?! We are very grateful for the opportunity and hope BioSCENTer will keep up the iGEM vibe at the KU Leuven!</br></br></br></br><br />
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We did our molecular biology experiments in the Laboratory of Molecular and Synthetic Biology under supervision of Johan Robben and his lab members. We would like to thank all of them for the help and advice they've given us during the summer. They strongly believed in letting us do our own experiments! In the end, we are thankful for all the hands-on experience we gained this summer! <br/><br />
Thanks also to the <a href="http://www.biw.kuleuven.be/m2s/cmpg/">Centre of Microbial and Plant Genetics</a> and especially Elke Van Assche, David De Coster and Tine Verhoeven for their help and patience during our qPCR adventure.</p><br/><br />
<p align="justify">Another big thank you to the other lab on the second floor, the group of <a href="http://chem.kuleuven.be/en/research/bmsb/gmgroup/">Prof. Giovanni Maglia</a>, working on Nanopore devices, for their help and support in finding our way through a hall with identical doors with media, buffers, ... hiding behind them! <br/><br/><br/><br/><br/><br/><br />
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<p align="justify"> Finally, thanks to our <b>administrative supporters</b>! First and foremost to <a href="http://www.kuleuven.be/wieiswie/en/person/00001926">Cathy Hendrickx</a>, who helped us take care of our finances and made sure we were all booked on the right flights with the proper paperwork!</br><br />
Thanks also to <a href="http://www.kuleuven.be/wieiswie/en/person/00047610">Anouk Desmet</a>, an invaluable part of the iGEM selection committee, a rockstar in ISP organisation and our connection to the Medical Faculty.</br></br></br>Also <a href="http://http://www.kuleuven.be/wieiswie/nl/person/00062351">Liesbeth Van Meerbeek</a> deserves a big hurrah for continuously adapting the iGEM BioSCENTer website to our wishes, usually last moment and under time pressure .... </br></br></br></br></br></br></br><br />
Last but not least, we thank <a href="http://www.kuleuven.be/wieiswie/en/person/00001190">Mimi Deprez</a>. She facilitated access to the fancy poster printer (multiple times ...), is an important part of the iGEM selection committee and a steady and calm presence in the face of press deadlines and meetings. </br></br><br />
We couldn't have done it without them, so THANKS !!! </p><br/><br />
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<img src="https://static.igem.org/mediawiki/2013/e/ec/Jorisattributions.jpg" alt="Joris Winderickx"/><br />
<p> Joris Winderickx</p><br />
<img src="https://static.igem.org/mediawiki/2013/3/32/Laboratory_of_molensynbio.JPG" alt="labo members"/><br />
<p> The laboratory of Molecular and Synthetic Biology.</p><br />
<img src="https://static.igem.org/mediawiki/2013/6/69/Group_pic_Maglia.jpg" alt="Maglia lab members"/><br />
<p> The laboratory of Nanopore Devices.</p><br />
<img src="https://static.igem.org/mediawiki/2013/b/be/Cathy_adminsupport.jpg" alt="Cathy Hendrickx"/><br />
<p> Cathy Hendrickx</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/7f/Liesbeth_adminsupport.JPG" alt="Liesbeth Van Meerbeek"/><br />
<p> Liesbeth Van Meerbeek</p><br />
<img src="https://static.igem.org/mediawiki/2013/8/8c/Mimi_adminsupport.png" alt="mimi photo"/><br />
<p> Mimi Deprez</p><br />
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<p align="justify">We are thankful to professor <a href="http://www.kuleuven.be/wieiswie/nl/person/u0003363">Luc Anckaert</a> from the KU Leuven for supervising the ethical part of our project and also his contribution to the article on Hans Jonas in <a href="http://www.tertio.be/sitepages/index.php?page=abonneren_keuze">Tertio</a>. We would like to thank the Institute of Philosophy of the KU Leuven for their hospitality and the collaboration between this department and the KU Leuven iGEM team.</br><br />
Thanks to dr. <a href="http://www.kuleuven.be/wieiswie/nl/person/u0060200">Jan Baeten</a> and professor <a href="http://www.kuleuven.be/wieiswie/en/person/00012869">Dirk De Vos</a> from the Centre for Surface chemistry and catalysis for their help with the GC-MS and to dr. <a href="http://www.kuleuven.be/wieiswie/en/person/00059705">Joaquin Christiaens</a> and professor <a href="http://www.kuleuven.be/wieiswie/en/person/00031931">Kevin Verstrepen</a> from the Centre of Microbial and Plant Genetics for their help with the headspace GC-FID.<br/><br />
We would like to thank professor <a href="http://www.kuleuven.be/optec/people/74-Prof-Kristel-Bernaerts">Kristel Bernaerts</a> from the department of Chemical Engineering for her kind advice towards the modelling team.<br/><br />
We are thankful to professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0015385">Johan Suykens</a> from the department of Electrical Engineering (ESAT), professor <a href="http://www.kuleuven.be/wieiswie/nl/person/00005764">Jan Degrève</a> from the department of Chemical Engineering and professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0006989">Dirk Roose</a> from the department of Computer Science for their useful advice on our oscillator model.</br><br />
We would like to thank <a href="http://www.kuleuven.be/wieiswie/nl/person/u0073220">Tim Tambuyzer</a> for his time to explain us the possibilities of motion tracking.</br><br />
We are thankful to dr. <a href="http://www.kuleuven.be/wieiswie/nl/person/u0038989">Joke Allemeersch</a> from Nucleomics Core for the information regarding the nCounter technology.</br><br />
We are grateful to professor <a href="http://www.kuleuven.be/wieiswie/en/person/u0033548">Abram Aertsen</a> from the Centre of Microbiological and Food Technology for his expertise on implementation of networks in vivo.</br><br />
We would like to thank Dries Van Eyck for the <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS#3Dmodels" target="_blank">3D modelling pictures of DAHP synthase</a>.<br />
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<p align="justify">We were invited by the companies <a href="http://www.biobest.be/home/3">Biobest</a> and <a href="http://www.pcfruit.be/Homepage/22724/pcfruit">pcfruit</a> to perform experiments with aphids, their predators and plants. We would like to thank them very much that they've opened their laboratories for us, helped us perform the experiments and shared their knowledge.</p><br />
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First of all a big thanks to the <a href="https://2013.igem.org/Team:Calgary>present and past Calgary iGEM teams</a> for their brainstorming over a few Belgian beers and tips and tricks throughout the summer. We wish them luck for their regional jamboree !</br><br />
We are grateful to professor <a href="http://www.filosofie.science.ru.nl/">Hub Zwart</a>, professor of Philosophy at the faculty of science of the Radboud University Nijmegen, The Netherlands, for his contribution to one of the three articles, published in <a href="http://www.tertio.be/sitepages/index.php?page=abonneren_keuze">Tertio</a>, on ethical aspects of synthetic biology. Moreover, we thank him for his <a href="https://2013.igem.org/Team:KU_Leuven/Human_Practices/Psychoanalytics">lecture and seminar</a> on September 26th 2013.<br/><br />
We are grateful to professor <a href="http://www.tilburguniversity.edu/webwijs/show/?uid=g.h.t.blans">Bert Blans</a>, emeritus professor at the School of Catholic Theology of the Tilburg University, The Netherlands for his contribution to the article on Hans Jonas in Tertio.<br/><br />
Thank you to Flore's dad for the artwork on our flyer and key-chain gadget - it will travel the world through iGEM.<br/><br />
The wiki team would like to thank Jan Luts for his help in designing the wiki page. If you also want an awesome site like this one, you can contact him via his <a href="http://www.linkedin.com/in/janluts">LinkedIn account</a>.</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBATeam:KU Leuven/Project/Glucosemodel/MeS/Modelling-FBA2013-10-28T23:03:27Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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We needed to check whether the introduction of our MeS brick and the production of the components influences the overall BanAphid metabolism and/or growth rate. This modelling results will be checked against wetlab data, namely the growth curves we obtained while characterising our MeS biobricks.<br />
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We also composed a Kinetic Parameter Model to estimate the average production rate of MeS. Approach and results can be found <a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling">here</a>.<br/><br />
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<p align = "justify"> A FBA calculates possibilities for the flow of metabolites through a metabolic network while maximising a set objective, in our case the growth rate of an organism or the production of a biotechnologically important metabolite.<br />
We ran the FBA for methyl salicylate using the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html" target="_blank">COBRA Toolbox</a> for MATLAB.</b><br />
<b>COBRA</b> stands for <b>Constraint-Based Reconstruction and Analysis</b> (COBRA) approach. It provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells. The <b>Flux Balance Analysis (FBA)</b> is probably the most used analysis within COBRA. <br/> <br/><br />
<b>COBRA has been successfully applied to study the possible phenotypes that arise from a genome </b>(Covert, Schilling <i>et al.</i> 2001; Orth <i>et al.</i> 2010). COBRA consists of two fundamental steps.<br/><br />
First, a GENRE (=GEnome-scale Network REconstruction) is formed, composed of the mathematical representation of all known metabolic reactions.<br/><br />
Second, the appropriate constraints are applied to form the corresponding GEMS (GEnome-scale Model <i>in Silico</i>). <br />
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<p align = "justify">Two fundamental types of constraints exist: <b>balances and bounds</b> (Price, Reed <i>et al.</i> 2004). Balance constraints are associated with conserved quantities such as energy, mass etc. Bounds limit numerical ranges of individual variables and parameters such as concentrations, fluxes or kinetic constants. <b>At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption.</b> This balance of fluxes can be represented mathematically as S . v = 0, where v is a vector of fluxes through the metabolic network and S is the stoichiometric matrix containing the stoichiometry of all reactions in the network.<br />
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Both bound and balance constraints limit the allowed functional states of reconstructed networks. Constraints can be very diverse in a biological system : physico-chemical constraints (reaction rates, enzyme turnover rates, diffusion rates etc.) , topo-biological (e.g. organisation of DNA in <i>Escherichia coli</i> by spatio-temporal patterns (Huang, Zhang <i>et al.</i> 2003)), environmental (nutrient availability, pH, temperature, osmolarity and the availability of electron acceptors and regulatory constraints).<br/>In mathematical terms, the constraints define a system of linear equations which will be solved by linear programming in FBA. This will result in a range of allowable network states, described by a solution space which, in biology, represents the phenotypic potential of an organism. All allowable network states are contained in this solution space. (Covert and Palsson 2003; Price, Papin <i>et al.</i> 2003)<br/><br />
Thus, we can predict the growth rate potential of our BanAphids, defined by the constraints we impose, e.g. the growth medium, temperature, co-factor/precursor presence etc.</p><br />
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We used an <i>E. coli</i> model from 2007 (iAF1260 by Feist, AM. <i>et al.</i>) in all the following COBRA toolbox analyses.<br/><br/><br />
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<b>As a first step we tried to predict the growth under default conditions for this model</b>. This gave us the following results (after setting the biomass as objective function): for <i>E. coli</i> under default conditions a growth rate of 0.74 hr<sup>-1</sup> is predicted. When performing the same calculations, but for LB medium conditions, a growth rate of 5.34 hr<sup>-1</sup> is predicted. This shows that <i>E. coli</i> benefit from the LB medium conditions.<br />
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<b>In a next step we wanted to add the reactions which are necessary for our model, but lacking in the iAF1260 model.</b><br/><br />
We added 'pchA', 'chor[c] -> ichor[c]' for the isochorismate synthesis reaction, 'pchB', 'ichor[c] -> sali[c] + pyr[c]' for the salicylate synthesis reaction and 'BSMT1', 'sali[c] -> methylsalicylate' for the methyl salicylate synthesis reaction.<br/> We also added the exchange reaction for methylsalicylate ('Ex_methylsalicylate').<br />
When we performed the growth calculation analysis for this modified model with the biomass set as objective function, we also observed a growth rate of 0.737 hr<i>-1</i> </b><br />
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<p align = "justify"><b>Since our bacteria will be grown on LB medium, we changed the default medium settings towards those for LB medium.</b> This means that we changed the relevant exchange reactions for the metabolites present in LB medium as seen in <i>Tawornsamretkit et al.</i>. The lower reaction bounds of the relevant reactions were set as following:<br />
model = changeRxnBounds (model,'EX_glc(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_phe_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_cys_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_ile_L(e)',-0.089,'l')<br/><br />
model = changeRxnBounds (model,'EX_ins(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_hxan(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_h2o(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_o2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_co2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nh4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_so4(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ca2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_h(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_k(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_mg2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_na1(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_fe3(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_nac(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thym(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_ade(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_thr_L(e)',-0.288,'l')<br/><br />
model = changeRxnBounds (model,'EX_val_L(e)',-0.071 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_pro_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_his_L(e)',-1.642,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ura(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_tyr_L(e)',-0.035 ,'l')<br/><br />
model = changeRxnBounds (model,'EX_trp_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ser_L(e)',-1.722,'l')<br/><br />
model = changeRxnBounds (model,'EX_arg_L(e)',-1.17,'l')<br/><br />
model = changeRxnBounds (model,'EX_asp_L(e)',-0.041,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_lys_L(e)',-0.1,'l')<br/><br />
model = changeRxnBounds (model,'EX_ala_L(e)',-0.369,'l')<br/><br />
model = changeRxnBounds (model,'EX_zn2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_cd2(e)',-1000,'l')<br/><br />
model = changeRxnBounds (model,'EX_glyc(e)',-0.014,'l')<br/><br />
model = changeRxnBounds (model,'EX_gln_L(e)',-0.445,'l')<br/><br />
model = changeRxnBounds (model,'EX_glu_L(e)',0,'l')<br/><br />
model = changeRxnBounds (model,'EX_leu_L(e)',-0.235,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_D(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_met_L(e)',-0.084,'l')<br/><br />
model = changeRxnBounds (model,'EX_tre(e)',-0.6,'l')<br/><br />
</p><br />
<p align = "justify">When we performed the growth calculation with the biomass as objective function the flux to chorismate(a precursor of methyl salicylate) was 0.274 mol/hr in <b>non-LB medium</b> conditions and the growth of <i>E. coli</i> 0.737 hr<sup>-1</sup>. When we do the same but for the <b>LB medium conditions</b> we observe the flux to chorismate as 0.80 mol/hr and a growth rate of 5.34 hr<sup>-1</sup>. This suggests that LB-medium is beneficial for <i>E. coli</i> growth and improves the flux towards chorismate. Our aroG BioBrick <a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060000">(Part:BBa_K1060000)</a> approach, aims however for a higher flux towards chorismate. This BioBrick contains mutations that can prevent the repression by Phenylalanine, that would occur otherwise and is in favour of chorismate production at the same time.<br />
<br />
.<br />
</p><br />
<br />
<p align = "justify">We were also interested to see how the maximal production of MeS is related to the maximal growth of <i>E. coli</i> under minimal conditions and in LB medium conditions. Therefore we set the objective function to MeS and set the lower bound for the biomass at different percentages of the maximal growth rate predicted with the biomass as objective function.</p><br />
<img src="https://static.igem.org/mediawiki/2013/7/77/TinasFBA.jpg"/> <br />
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<p align = "justify">As seen in the graph, there is a linear correlation between maximal flux towards MeS and maximal <i>E. coli</i> growth. This linear correlation is qualitatively similar both under minimal growth conditions as in LB medium conditions and shows that we are dealing with a trade off between bacterial growth and MeS production.<br/><br />
If a higher flux to MeS is preferred over a lower <i>E. coli</i> growth mass (yield) a value at the left side of the graph should be considered, whereas a value to the right would give a higher <i>E. coli</i> production rate and a lower flux towards MeS. This is consistent when taking into account the results from the wetlab since we observed lower growth curves for higher concentrations of added salicylate. If we assume that the production of SAM (S-adenosylmethionine), a common co-substrate of the methyltransferase reaction from salicylate to methylsalicylate, is important for cell metabolism, providing higher levels of salicylate would increase the availability of SAM. However, high levels of salicylate may lead to higher levels of SAM which are in turn associated with higher homocysteine levels (a side-product that stays behind when the methylgroup has been transferred to substrate). Increased homocysteine levels are toxic for <i>E. coli</i> strains which may explain the ceiling we observe when adding salicylate, hoping for higher MeS fluxes.<br />
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<h3 class="bg-oscillator">Conclusion</h3><br />
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<h3 class="bg-oscillator">References</h3><br />
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<p align = "justify">LB medium conditions are predicted by FBA analysis to be beneficial for both E.Coli growth and the flux towards chorismate, an important precursor for methylsalicylate.<br />
Covert, M. W. and B. O. Palsson (2003). "Constraints-based models: regulation of gene expression reduces the steady-state solution space." J Theor Biol 221(3): 309-325.<br/><br />
Covert, M. W., C. H. Schilling, I. Famili, J. S. Edwards, Goryanin, II, E. Selkov and B. O. Palsson (2001). "Metabolic modeling of microbial strains in silico." Trends Biochem Sci 26(3): 179-186. <br/><br />
Feist, AM <i>et al.</i> (2007) “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.” Mol. Syst. Biol. 3 121.<br/> <br />
Huang, J., Q. Zhang and T. Schlick (2003). "Effect of DNA superhelicity and bound proteins on mechanistic aspects of the Hin-mediated and Fis-enhanced inversion." Biophys J 85(2): 804-817.<br/><br />
Iyarest Tawornsamretkit, Rattana Thanasomboon, Jittrawan Thaiprasit, Dujduan Waraho, Supapon Cheevadhanarak, Asawin Meechai, Analysis of Metabolic Network of Synthetic Escherichia coli Producing Linalool Using Constraint-based Modeling, Procedia Computer Science, Volume 11, 2012, Pages 24-35, ISSN 1877-0509Mahadevan, R. and C. H. Schilling (2003). "The effects of alternate optimal solutions in constraint-based genome-scale metabolic models." Metab Eng 5(4): 264-276.<br/><br />
Orth, J. D., I. Thiele, and B.O. Palsson (2010). "What is Flux Balance Analysis?" Nature Biotechnology (28): 245-248.<br/> <br />
Papin, J. A., N. D. Price, J. S. Edwards and B. B. Palsson (2002). "The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy." J Theor Biol 215(1): 67-82.<br/><br />
Price, N. D., J. A. Papin, C. H. Schilling and B. O. Palsson (2003). "Genome-scale microbial in silico models: the constraints-based approach." Trends Biotechnol 21(4): 162-169.<br/><br />
Price, N. D., J. L. Reed and B. O. Palsson (2004). "Genome-scale models of microbial cells: evaluating the consequences of constraints." Nat Rev Microbiol 2(11): 886-897.<br/><br />
Schilling, C. H., M. W. Covert, I. Famili, G. M. Church, J. S. Edwards and B. O. Palsson (2002). "Genome-scale metabolic model of Helicobacter pylori 26695." J Bacteriol 184(16): 4582-4593.<br/><br />
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</p><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T22:23:23Z<p>Veerledewever: </p>
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<a href="https://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA"><br />
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
<br><br><br />
Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<h3 class="bg-green">ODE Representation</h3><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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with:<br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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<p align = "justify"><br />
At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
<br/> <br/><br />
We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<p align = "justify"><h3>mRNA flux:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/f/fc/1A1B.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<p align = "justify"><h3>Protein flux:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<p align = "justify"><h3>Methyl salicylate synthesis:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
</p><br />
<p align = "justify"><br />
<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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<p align = "justify"><h3>Formulary:</h3><br />
For example for BSMT1:<br />
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<table border="1"><br />
<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<img src="https://static.igem.org/mediawiki/2013/1/12/Methylsalicilate_Pathway_Diagram.jpg"/><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
<p align="justify"> <br />
First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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<br/><br />
In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
<p align = "justify"><br />
We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
<br/><br/><br />
In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known to determine the rate of translation </b>.<br />
<br/><br/><br />
The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate.<br />
<br/><br/><br />
Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however, this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each protein can thus be determined. An absolute translation initiation rate for only one gene suffices to extract absolute rates. To model this properly, we would require a translation initiation rate of one of the genes from our construct. At this moment, these values are not available from the wetlab, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was unrealistically low. We realised the RBS for PchA was part of PchB, causing the low output. After communication with dr. Salis himself we settle on a different tool from his website, especially designed for operon structures. <br />
This was indeed the appropriate tool to quantify the translation initiation of the operon pchBA : the output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. We obtained results for the lac operon (as a control) and for the genes we want to clone into <i>E. coli</i> (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>). They are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.</p><br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<p align="justify"> When MIT produced their MeS brick <a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a> in 2006, it was meant to convert salicylate to methylsalicylate and produce a wintergreen scent in the process. However, the scent could only be detected when salicylate was added to the medium, proving that the BSMT1 equation functions yet the PchA and PchB equations may not function. Initially, it was thought that the RBS problems discussed above could be the cause but we could show this is most likely not the case : we entered the MIT brick sequence in the operon specific tool and obtained satisfactory data. Thus, at least <I>in silico</I> the pchBA operon could function, suggesting the lack of salicylate is caused by a different reason. For further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. </p><br />
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<h3>Protein degradation:</h3><br />
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The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> The limiting step (associated with the most uncertainty) in the kinetic parameter model is the transcription rate. We hoped to improve this aspect by using wetlab derived mRNA levels, obtained via qPCR studies. Due to the unforeseen circumstances with the qPCR we unfortunately were not able to integrate these wet-lab data for each protein in our system. More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.<br/><br />
These amounts were meant to reduce the uncertainty in the model. Rather than running our model with unrealistic values (eg. the transcription rate formula described in section 1) and add uncertainty to the data, we opted to not use this model at this point.<br/><br />
However, we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> Providing we can circumvent the problems associated with high copy number plasmids, wetlab data will offer more realistic values than currently used, modelled transcription values. </p><br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T22:13:15Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<p align = "justify"><h3>mRNA flux:</h3><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<p align = "justify"><h3>Methyl salicylate synthesis:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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<p align = "justify"><h3>Formulary:</h3><br />
For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
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First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
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An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
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Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known to determine the rate of translation </b>.<br />
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The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate.<br />
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Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however, this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each protein can thus be determined. An absolute translation initiation rate for only one gene suffices to extract absolute rates. To model this properly, we would require a translation initiation rate of one of the genes from our construct. At this moment, these values are not available from the wetlab, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was unrealistically low. We realised the RBS for PchA was part of PchB, causing the low output. After communication with dr. Salis himself we settle on a different tool from his website, especially designed for operon structures. <br />
This was indeed the appropriate tool to quantify the translation initiation of the operon pchBA : the output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. We obtained results for the lac operon (as a control) and for the genes we want to clone into <i>E. coli</i> (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>). They are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.</p><br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<p align="justify"> When MIT produced their MeS brick <a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a> in 2006, it was meant to convert salicylate to methylsalicylate and produce a wintergreen scent in the process. However, the scent could only be detected when salicylate was added to the medium, proving that the BSMT1 equation functions yet the PchA and PchB equations may not function. Initially, it was thought that the RBS problems discussed above could be the cause but we could show this is most likely not the case : we entered the MIT brick sequence in the operon specific tool and obtained satisfactory data. Thus, at least <I>in silico</I> the pchBA operon could function, suggesting the lack of salicylate is caused by a different reason. For further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. </p><br />
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<h3>Protein degradation:</h3><br />
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The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T22:12:00Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<p align = "justify"><h3>mRNA flux:</h3><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
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First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
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An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
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Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known to determine the rate of translation </b>.<br />
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The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate.<br />
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Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however, this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each protein can thus be determined. An absolute translation initiation rate for only one gene suffices to extract absolute rates. To model this properly, we would require a translation initiation rate of one of the genes from our construct. At this moment, these values are not available from the wetlab, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was unrealistically low. We realised the RBS for PchA was part of PchB, causing the low output. After communication with dr. Salis himself we settle on a different tool from his website, especially designed for operon structures. <br />
This was indeed the appropriate tool to quantify the translation initiation of the operon pchBA : the output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. We obtained results for the lac operon (as a control) and for the genes we want to clone into <i>E. coli</i> (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>). They are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.</p><br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<p align="justify"> When MIT produced their MeS brick <a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a> in 2006, it was meant to convert salicylate to methylsalicylate and produce a wintergreen scent in the process. However, the scent could only be detected when salicylate was added to the medium, proving that the BSMT1 equation functions yet the PchA and PchB equations may not function. Initially, it was thought that the RBS problems discussed above could be the cause but we could show this is most likely not the case : we entered the MIT brick sequence in the operon specific tool and obtained satisfactory data. Thus, at least <I>in silico</I> the pchBA operon could function, suggesting the lack of salicylate is caused by a different reason. For further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. </p><br />
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<h3>Protein degradation:</h3><br />
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The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T21:53:47Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
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First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
<br/><br/><br />
In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known to determine the rate of translation </b>.<br />
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The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate.<br />
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Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however, this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each protein can thus be determined. An absolute translation initiation rate for only one gene suffices to extract absolute rates. To model this properly, we would require a translation initiation rate of one of the genes from our construct. At this moment, these values are not available from the wetlab, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was unrealistically low. We realised the RBS for PchA was part of PchB, causing the low output. After communication with dr. Salis himself we settle on a different tool from his website, especially designed for operon structures. <br />
This was indeed the appropriate tool to quantify the translation initiation of the operon pchBA : the output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. We obtained results for the lac operon (as a control) and for the genes we want to clone into <i>E. coli</i> (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>). They are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.</p><br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
<br />
<p> A malfunctioning translation step could explain the lack of wintergreen scent when using the MIT 2006 brick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>). Of this brick only the BSMT1 step was proven to function and not the PchA and the PchB step. This buried the hypothesis that the low translation rate is responsible for the lack of occurrence of salycic acid while using the brick, for further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. </p><br />
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<h3>Protein degradation:</h3><br />
<p align="justify"> <br />
The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T21:50:23Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<img src="https://static.igem.org/mediawiki/2013/1/12/Methylsalicilate_Pathway_Diagram.jpg"/><br />
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<h3 class="bg-green">Parameter Choice</h3><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
<p align="justify"> <br />
First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known to determine the rate of translation </b>.<br />
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The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate.<br />
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Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however, this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each protein can thus be determined. An absolute translation initiation rate for only one gene suffices to extract absolute rates. To model this properly, we would require a translation initiation rate of one of the genes from our construct. At this moment, these values are not available from the wetlab, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was unrealistically low. We realised the RBS for PchA was part of PchB, causing the low output. After communication with dr. Salis himself we settle on a different tool from his website, especially designed for operon structures. <br />
This was indeed the appropriate tool to quantify the translation initiation of the operon pchBA : the output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. We obtained results for the lac operon (as a control) and for the genes we want to clone into <i>E. coli</i> (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>). They are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.<br />
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A malfunctioning translation step could explain the lack of wintergreen scent when using the MIT 2006 brick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>). Of this brick only the BSMT1 step was proven to function and not the PchA and the PchB step. This buried the hypothesis that the low translation rate is responsible for the lack of occurrence of salycic acid while using the brick, for further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. </p><br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<h3>Protein degradation:</h3><br />
<p align="justify"> <br />
The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T21:24:40Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<img src="https://static.igem.org/mediawiki/2013/1/12/Methylsalicilate_Pathway_Diagram.jpg"/><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
<p align="justify"> <br />
First we determine the number of genes transcribed in our model. We start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we assume an average of 200 gene copies per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that it is difficult to predict transcription rate, particularly combined with the proper promoter dependence. It is even near impossible with without good wet-lab data.<br/><br />
A recent review by Shiue and Prather (2012) describes this problem in the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also recent discussions on stochastic gene expression suggest that reliable, quantitative predictions of mRNA production are a daunting task.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We fear that this formula is not a proper representation of transcription rate for a number of reasons :</p><br />
<ol><li>The reference claiming an average transcription speed of 70 nt/s is no longer available. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find realistic values.</li><li>This formula does not take into account promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Watson et al., Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>Gene length, aka the number of nucleotides involved, could influence transcription rate. <b>The longer the gene, the higher the chance that the polymerase starts proofreading, slowing down the transcription rate.</b> We did not find any reference in literature incorporating gene length as an important transcription rate parameter.</li></ol> <br />
<p align = "justify">Summarized, we would strongly suggest to other iGEM teams to refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case, we observed most uncertainty in the transition from transcription to mRNA production. As an alternative to the modelled mRNA production step, we tried to determine <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known in order to determine the rate of translation </b>.<br />
<br/><br/><br />
The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate. It is however necessary to mention another feature that can be of particular importance for the initiation of translation: the occurrence of a secondary structure in the ribosome binding site. This can be regarded as an outlier tough, since evolution tuned the ribosome binding sites as such that they only rarely show this behavior. When it would occur there would be a much lower rate of translation, since initiation requires the RBS to be unfolded (De Smit and van Duin, 1990).<br />
<br/><br/><br />
Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each of the proteins can thus be determined. To extract absolute rates it suffices to have an absolute translation initiation rate for only one gene. In order to model this properly we would require a translation initiation rate of one of our genes from our construct. These values are not available at this moment, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was not satisfactory. A malfunctioning translation step could explain the lack of wintergreen scent when using the MIT 2006 brick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>). Of this brick only the BSMT1 step was proven to function and not the PchA and the PchB step. After communication with dr. Salis himself we used a different tool on the website, designed for operon structures. This was indeed the appropriate tool to quantify the translation initiation of pchBA, since the RBS of pchA is in the end of the coding sequence of pchB. The output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. This buried the hypothesis that the low translation rate is responsible for the lack of occurrence of salycic acid while using the brick, for further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. The results from using the above-mentioned tool (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>) for the lac operon and for the genes we want to clone into <i>E. coli</i>, are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.<br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<h3>Protein degradation:</h3><br />
<p align="justify"> <br />
The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
<br/><br/><br />
The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T20:58:26Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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with:<br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs) to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<img src="https://static.igem.org/mediawiki/2013/f/fc/1A1B.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<p align = "justify"><h3>Methyl salicylate synthesis:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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<p align = "justify"><h3>Formulary:</h3><br />
For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<img src="https://static.igem.org/mediawiki/2013/1/12/Methylsalicilate_Pathway_Diagram.jpg"/><br />
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<h3 class="bg-green">Parameter Choice</h3><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
<p align="justify"> <br />
The first step in our model is the determination of the number of genes which can be transcribed. In our system we start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we will assume 200 copies of genes per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that prediction of transcription rate, and its promoter dependence, is very hard and even impossible to do without any good data. The review article by Shiue and Prather (2012) describes this problem the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also the recent discussions about stochastic gene expression make it as good as impossible to do quantitative predictions of mRNA production.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We believe that this formula does not represent the transcription rate on a correct way because:</p><br />
<ol><li>The reference that says that the average transcription speed is 70 nt/s does not exist anymore. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find any decent value.</li><li>In this formula there is no single association with the promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>The number of nucleotides could indeed have some influence on the rate of transcription. <b>The longer the gene, the bigger the chance that the polymerase does not properly finish the transcript.</b> But in literature we did not find any reference that uses the gene length as one of the important parameters for determining the rate of transcription.</li></ol> <br />
<p align = "justify">We hope that other iGEM teams in the future will refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case we decided that we would bypass the mRNA production step as it is responsible for a large part of the uncertainty in our prediction. In order to attain our goal without the use of transcription rates we tried to determine the <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known in order to determine the rate of translation </b>.<br />
<br/><br/><br />
The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate. It is however necessary to mention another feature that can be of particular importance for the initiation of translation: the occurrence of a secondary structure in the ribosome binding site. This can be regarded as an outlier tough, since evolution tuned the ribosome binding sites as such that they only rarely show this behavior. When it would occur there would be a much lower rate of translation, since initiation requires the RBS to be unfolded (De Smit and van Duin, 1990).<br />
<br/><br/><br />
Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each of the proteins can thus be determined. To extract absolute rates it suffices to have an absolute translation initiation rate for only one gene. In order to model this properly we would require a translation initiation rate of one of our genes from our construct. These values are not available at this moment, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was not satisfactory. A malfunctioning translation step could explain the lack of wintergreen scent when using the MIT 2006 brick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>). Of this brick only the BSMT1 step was proven to function and not the PchA and the PchB step. After communication with dr. Salis himself we used a different tool on the website, designed for operon structures. This was indeed the appropriate tool to quantify the translation initiation of pchBA, since the RBS of pchA is in the end of the coding sequence of pchB. The output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. This buried the hypothesis that the low translation rate is responsible for the lack of occurrence of salycic acid while using the brick, for further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. The results from using the above-mentioned tool (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>) for the lac operon and for the genes we want to clone into <i>E. coli</i>, are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.<br />
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<table border=1><br />
<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<h3>Protein degradation:</h3><br />
<p align="justify"> <br />
The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
<br/><br/><br />
The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align = "justify"><h3>Concluding values:</h3></p><br />
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<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/MeS/ModellingTeam:KU Leuven/Project/Glucosemodel/MeS/Modelling2013-10-28T20:57:00Z<p>Veerledewever: </p>
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<h3>Flux Balance Analysis</h3> </a><br />
<p>Effect on BanAphids metabolism?</p><br />
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<h3>Kinetic Parameters</h3><br />
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<h3 class="bg-green">Kinetic Parameter Model on Methyl Salicylate</h3><br />
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<p align = "justify"> When we introduce new genes and pathways into our bacterium, several questions arise like for example: Does it influence its metabolism or growth rate? To answer this question we performed a Flux Balance Analysis (FBA) which can be found <a href="https://2013.igem.org/wiki/index.php?title=Team:KU_Leuven/Project/Glucosemodel/MeS/Modelling-FBA">here</a>.<br />
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Another important question could be: How much methyl salycilate (MeS) will be produced in the end? This question can be answered using the Kinetic Parameter Model, described on this page. By modelling the pathway leading to MeS we can get a good estimation of the average MeS production. Apart from that we can also take a closer look at the pathway and find the rate limiting steps. We can use this information to fine tune the MeS production the way we want it.<br />
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Jump to the following topics:</p><br />
<ul><li><a href="#ODE Representation">ODE Representation</a></li> <br />
<li><a href="#Parameter Choice">Parameter Choice</a></li><br />
<li><a href="#Results">Results</a></li></ul><br />
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<p align = "justify">The methyl salicylate pathway contains the following reactions:<br />
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<img src="https://static.igem.org/mediawiki/2013/5/58/Methylsalicylate_pathway.png"/><br />
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with:<br />
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<ul><li>PchA = Pyochelin A</li><li>PchB = Pyochelin B</li><li>BSMT1 = Benzoate/Salicylate carboxyl methyltransferase</li><li>SAM = S-adenosyl-L-methionine</li><li>SAH = Salicylate methyl ester</li></ul><br />
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At first, our intention was to model the entire pathway from the implemented DNA sequence to the resulting production rate. This could be very useful to approximate the resulting production rate and to figure out the rate-limiting step. To achieve this we need a mathematical representation of all the relevant biological processes, including transcription rate, mRNA degradation rate, translation rate, protein degradation rate and enzyme kinetics.<br />
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We created a set of <b>ordinary differential equations (ODEs)to represent every step in our pathway</b>: transcription, translation and the chemical activity of the protein. <br />
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<img src="https://static.igem.org/mediawiki/2013/f/fc/1A1B.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
The proteins <b>pyochelin A (PchA) and pyochelin B (PchB) are extracted from the pchDCBA operon </b>and are the structural proteins responsible for salicylate biosynthesis. Serion <i>et al.</i> (1995) describes that the expression of the <i>pchA</i> gene appears to depend on the transcription and translation of the upstream <i>pchB</i> gene in <i>P. aeruginosa</i>. They also state <i>“Salicylate formation was demonstrated in an </i>Escherichia coli entC<i> mutant lacking isochorismate synthase when this strain expressed both </i>pchBA<i> genes, but not when it expressed </i>pchB<i> alone”</i>. This is also confirmed by Gaille, Reimman and Haas (2003): <i>“The </i>pchA<i> gene is strictly co-expressed with the upstream </i>pchB<i> gene; without </i>pchB<i> being present in cis no expression of </i>pchA<i> can be observed”</i>. Finally Serion <i>et al.</i> (1995) reports that the <i>pchB</i> stop codon overlaps the presumed <i>pchA</i> start codon. <br/> <br/><br />
Therefore we conclude that <b>transcription and translation of <i>pchA</i> and <i>pchB</i> is coupled and we decided to use only one gene (pchBAgene), and only one mRNA molecule (mpchbA) for both proteins (PchA and PchB) in our model</b>. <br />
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<p align = "justify"><h3>Protein flux:</h3><br />
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<img src="https://static.igem.org/mediawiki/2013/b/bb/2A2C.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<img src="https://static.igem.org/mediawiki/2013/7/75/3A3F.png"/><br />
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<i> See the formulary below for further information about the used terminology. </i><br />
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<u>Comments:</u> <br/><br />
<ul><li> For our modeling purposes, we take the <b>chorismate concentration as a pool</b>.</li><li>For every reaction we assume Michaelis-Menten kinetics.</li><li>The division by NA. EcoliCellVolume in the numerator is necessary to convert the amount of molecules of our enzyme to a concentration.<li>In equations [3.E] and [3.F ] Km3a represents the Km of salicylate while Km3b represents the Km of SAM.</li></ul><br />
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<p align = "justify"><h3>Formulary:</h3><br />
For example for BSMT1:<br />
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<tr><th><b>Name<b/></th><th><b>Units<b/></th><th><b>Description</b></th></tr><br />
<tr><td>BSMT1gene</td><td># genes</td><td><b>Copy number (amount)</b> of <i>bsmt1</i> gene</td></tr><br />
<tr><td>mBSMT1</td><td># mRNA</td><td><b>Amount</b> of <i>bsmt1</i> mRNA</td></tr><br />
<tr><td>BSMT1</td><td># proteins</td><td><b>Amount</b> of BSMT1 substance (protein/molecule)</td></tr><br />
<tr><td>&#947;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Transcription rate</b> of PchBA gene</td></tr><br />
<tr><td>&#945;<sub>mBSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchBA mRNA</td></tr><br />
<tr><td>&#946;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Translation rate</b> of PchA</td></tr><br />
<tr><td>&#945;<sub>BSMT1</sub></td><td>&nbsp;</td><td><b>Degradation rate</b> of PchA protein</td></tr><br />
<tr><td>kcat1</td><td>&nbsp;</td><td><b>Turnover number</b></td></tr><br />
<tr><td>NA</td><td>&nbsp;</td><td><b>Avogadro constant</b></td></tr><br />
<tr><td>EcoliCellVolume</td><td>Liter</td><td>The average <b>volume</b> of one E. coli cell</td></tr><br />
<tr><td>Km</td><td>Molarity</td><td><b>Michaelis-Menten constant</b></td></tr><br />
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<p align = "justify"><h3>Symbiology Diagram:</h3><br />
We have put this model in SimBiology, provided by MATLAB, resulting in the following diagram:</p><br />
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<img src="https://static.igem.org/mediawiki/2013/1/12/Methylsalicilate_Pathway_Diagram.jpg"/><br />
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<p align = "justify">Of course this model is useless without any good parameters. In this next section you can read about our search for decent parameters and its complications.<br />
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<p align = "justify"><h3>Copy number:</h3></p><br />
<p align="justify"> <br />
The first step in our model is the determination of the number of genes which can be transcribed. In our system we start with 2 genes (<i>pchBA</i> operon and <i>bsmt1</i>). They are not on the same plasmid but both carry a pMB1 origin of replication. This ORI has a copy number of 100 to 300 plasmids per cell. Therefore we will assume 200 copies of genes per cell.<br />
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<p align = "justify"><h3>Transcription:</h3></p><br />
<p align="justify"> <br />
An extensive literature survey revealed that prediction of transcription rate, and its promoter dependence, is very hard and even impossible to do without any good data. The review article by Shiue and Prather (2012) describes this problem the following way: “<i>due to the large sequence space and relative lack of understanding regarding polymerase-promoter interactions, the development of such predictive models remains a daunting task</i>”. Also the recent discussions about stochastic gene expression make it as good as impossible to do quantitative predictions of mRNA production.<br />
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In the past, many iGEM teams predicted their transcription rate using a formula introduced by <a href="https://2008.igem.org/Team:NTU-Singapore/Modelling/Parameter">NTU-Singapore in 2008</a>:</p><br />
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<p align = "justify"><img src="https://static.igem.org/mediawiki/2013/0/08/NTU-Signapore-Transcription-Formula.png"></p><br />
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We believe that this formula does not represent the transcription rate on a correct way because:</p><br />
<ol><li>The reference that says that the average transcription speed is 70 nt/s does not exist anymore. We tried to search for an <b>average transcription rate</b> ourselves and we can’t seem to find any decent value.</li><li>In this formula there is no single association with the promoter strength. This is remarkable, because the strength of a promoter is a measure for how many times a transcript is initiated. (Molecular Biology of the Gene, 7th edition). <b>The stronger your promoter, the more transcripts are initiated, the more the gene is transcribed in time and thus the higher transcription rate</b>.</li><li>The number of nucleotides could indeed have some influence on the rate of transcription. <b>The longer the gene, the bigger the chance that the polymerase does not properly finish the transcript.</b> But in literature we did not find any reference that uses the gene length as one of the important parameters for determining the rate of transcription.</li></ol> <br />
<p align = "justify">We hope that other iGEM teams in the future will refrain from using this formula, because it is not a realistic representation of the transcription rate.<br />
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In our case we decided that we would bypass the mRNA production step as it is responsible for a large part of the uncertainty in our prediction. In order to attain our goal without the use of transcription rates we tried to determine the <i>in vivo</i> mRNA concentrations using qPCR. This means that we will drop formulas [1.A] and [1.B]. If you want to know more on how we tackled the qPCR, please go to our <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">WETLAB part</a>. <br />
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<p align = "justify"><h3>Translation:</h3></p><br />
<p align="justify"> <br />
Initiation is usually the most important rate-determining step of the translation process (McCarthy and Gualerzi, 1990). Combined with the fact that there is a negligible chance for premature disassembly of the ribosome and mRNA, <b> only the rate of translation initiation has to be known in order to determine the rate of translation </b>.<br />
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The initiation codon, the Shine-Dalgarno sequence, the identity of the base at position -3 and the occurrence of alternative ATGs (that do not serve as an initiation codon) are features known to be important for translation initiation (Barrick <i>et al.</i>, 1994). When those are known it should be possible to make an estimation of the translation rate. It is however necessary to mention another feature that can be of particular importance for the initiation of translation: the occurrence of a secondary structure in the ribosome binding site. This can be regarded as an outlier tough, since evolution tuned the ribosome binding sites as such that they only rarely show this behavior. When it would occur there would be a much lower rate of translation, since initiation requires the RBS to be unfolded (De Smit and van Duin, 1990).<br />
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Pennsylvania State University was able to quantify the different relevant features and created a tool (Salis <i>et al.</i>, 2009) (Salis, 2011) that predicts the translation rate when the mRNA sequence is known. Even within a range of five orders of magnitude the tool should not differ from the reality with a factor higher than 2.3 (Salis <i>et al.</i>, 2009). The RBS determines the translation initiation rate, however this is relative to all other translated coding sequences (Salis, 2011). Since the RBS calculator uses the same scale for every calculation, the relative translation initiation rate of each of the proteins can thus be determined. To extract absolute rates it suffices to have an absolute translation initiation rate for only one gene. In order to model this properly we would require a translation initiation rate of one of our genes from our construct. These values are not available at this moment, but values from literature should give a reasonable result. We have found that the initiation rate of translation for the <i>lacZ</i> gene in the <i>lac</i> operon is approximately 0.31 initiations per second per mRNA copy (Kennell and Riezman, 1977), which we consequently used as a standard. <br />
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A first run through the tool yielded adequate results for both PchB and BSMT1, however the output for PchA was not satisfactory. A malfunctioning translation step could explain the lack of wintergreen scent when using the MIT 2006 brick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>). Of this brick only the BSMT1 step was proven to function and not the PchA and the PchB step. After communication with dr. Salis himself we used a different tool on the website, designed for operon structures. This was indeed the appropriate tool to quantify the translation initiation of pchBA, since the RBS of pchA is in the end of the coding sequence of pchB. The output now showed a satisfactory translation rate for each of the proteins in <i>E. coli</i>. This buried the hypothesis that the low translation rate is responsible for the lack of occurrence of salycic acid while using the brick, for further elaboration on this topic we refer you to the <a href="https://2013.igem.org/Team:KU_Leuven/Project/MeSa/wetlab">methyl salicylate wetlab page</a>. The results from using the above-mentioned tool (<a href="https://salis.psu.edu/software/">https://salis.psu.edu/software/</a>) for the lac operon and for the genes we want to clone into <i>E. coli</i>, are listed in Table 1. The third column of this table shows the values of the translation initiation rate that are computed using the literature value from the lac operon.<br />
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<tr ><th><b>Gene</b></th><th><b>Translation initiation rate according to the RBS calculator (a.u.)</b></th><th><b>Translation initiation rate (initiations/(s.mRNA))</b></th></tr><br />
<tr><td><i>lacZ</i></td><td>20579,19</td><td>0,3125</td></tr><br />
<tr><td><i>bsmt1</i></td><td>5587,09</td><td>0,085</td></tr><br />
<tr><td><i>pchB</i></td><td>19288,23</td><td>0,293</td></tr><br />
<tr><td><i>pchA </i></td><td>326970,82</td><td>4,965</td></tr><br />
</table><br />
<i>Table 1. Translation rates, as computed with the Penn State University RBS calculator, using the MIT 2006 BioBrick (<a href="http://parts.igem.org/Part:BBa_J45700">BBa_J45700</a>)</i>.<br />
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<h3>Protein degradation:</h3><br />
<p align="justify"> <br />
The perceived degradation rate results not only from the breakdown of proteins, but also from the dilution due to cell growth. Every cell cycle the proteins are divided amongst the two resulting cells and the amount is thus effectively divided by two. We will look into both to conclude which effect dominates and what ranges are possible.<br />
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The breakdown part of the degradation of proteins is highly dependent on the presence of a degradation signal, called degron. These degrons could be hidden in a folded protein and could become exposed for example after a stress reaction (Dougan <i>et al.</i>, 2010). One of the most characterized and important degrons is called the N-degron, which is a destabilizing N-terminal residue. With this information, the laboratory of Varshavsky has created the N-end rule, which relates the <i>in vivo</i> half-life of a protein to the identity of its N-terminal residue (Varshavsky, 1997).<br/><br/><br />
The N-end rule is applicable to a wide range of organisms ranging from <i>E. coli</i> to plants and mammals (Dougan <i>et al.</i>, 2010). Of course we are interested in the <i>E. coli</i> N-end rule, described by Tobias <i>et al.</i> (1991) and Shrader <i>et al.</i> (1993). This N-end rule states that if the N-terminal residue is arginine, lysine, leucine, phenylalanine, tyrosine or tryptophan, the protein will have a half-life of only 2 minutes. These amino acids are called primary destabilizing residues. On the other hand, amino terminal arginine and lysine are secondary destabilizing residues in <i>E. coli</i>. These residues conjugate to primary destabilizing residues, which again results in a half-life of only 2 minutes (Tobias <i>et al.</i>, 1991). <br />
If the N-terminal residue is neither a primary nor a secondary destabilizing residue, the half-life of the proteins exceeds 10 hours. We applied this rule to the proteins of our interest, with the results displayed in Table 2. <br />
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<table class="tableizer-table"><br />
<tr class="tableizer-firstrow"><th><b>Protein</b></th><th><b>AA-sequence</b></th><th><b>Half-life</b></th></tr><br />
<tr><td>PchA</td><td>SRLAPLSQC …</td><td>>= 10 hours</td></tr><br />
<tr><td>PchB</td><td>PHPLTLLQI …</td><td>>= 10 hours</td></tr><br />
<tr><td>BSMT1</td><td>EVVEVLHM …</td><td>>= 10 hours</td></tr><br />
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<i>Table 2: The resulting half-lifes after using the N-end rule.</i><br />
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<p align="justify"> According to the N-End rule the half-life of our proteins exceeds 10 hours. If we compare this value with the generation time of a single <i>E. coli</i> cell, <b> we can conclude that these proteins live far longer than the cell itself </b>. Therefore we will take this generation time as a value for our “protein degradation”. On the <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=101790&ver=7&trm=generation%20coli"> Bionumbers website</a>, we found that a good rule of thumb for this generation time is around 3000s, which is 50 min.<br />
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<p align="justify"> <br />
<table border="1"><br />
<tr><th></th><th><b><i>pchA</b></i></th><th><b><i>pchB</b></i></th><th><b><i>bsmt1</b></i></th></tr><br />
<tr><td><b>Copy number</b></td><td>200 molecules</td><td>200 molecules</td><td>200 molecules</td></tr><br />
<tr><td><b>Transcription rate</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>mRNA degradation</b></td><td>/</td><td>/</td><td>/</td></tr><br />
<tr><td><b>Translation</b></td><td>4,965 per s</td><td>0,293 per s</td><td>0,085 per s</td></tr><br />
<tr><td><b>Protein degradation</b></td><td>50 min</td><td>50 min</td><td>50 min</td></tr><br />
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<p align = "justify"> Due to the unforeseen circumstances with the qPCR we unfortunately were not able to get to know the real amount of mRNA molecules for each protein in our system. (More about this qPCR story can be read <a href="https://2013.igem.org/Team:KU_Leuven/Project/qPCR">here</a>.) Since these amounts where the starting point of our model, we could not do any decent predictions or figure out the rate limiting step.<br/><br/><br />
Rather than running our model with unrealistic values (eg. The formula described in section 1 for the calculation of the transcription rate) which would result in inaccurate results, we opted to not use this model for any predictions. However we think that our extensive literature study has been very instructive, and hope that other iGEM teams could use this study (for example the RBS calculator) as a basis for their model. <b>We also want to emphasise the importance of the qPCR approach.</b> By using the amount of transcripts as a starting point for your model, you circumvent the most uncertain part of these kinds of models: transcription. <br />
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<h3 class="bg-green">References</h3><br />
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<p align = "justify"> Barrick, D. <i>et al.</i> (1994). Quantitative analysis of ribosome binding sites in <i>E. coli</i>. Nucleic Acids Research, 22(7):1287-1295. <br/><br />
de Smit, M. H., and van Duin J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative anaylsis. PNAS, 87(19):7668-7672.<br/><br />
Dougan, D.A., Truscott, K.N., Zeth, K. (2010). The bacterial N-end rule pathway: expect the unexpected. Molecular Biology, 76(3):545–558.<br/><br />
Gaille, C., Reimman, C., and Haas, D. (2003). Isochorismate Synthase (PchA), the First and Rate-limiting Enzyme in Salicylate Biosynthesis of <i>Pseudomonas aeruginosa</i>. The Journal of Biological Chemistry, 278: 16893-16898.<br/><br />
Kennell, D., and Riezman, H. (1997). Transcription and translation initiation frequencies of the <i>Escherichia coli</i> lac operon. J Mol Biol., 114(1):1-21.<br/><br />
McCarthy, J. E. G., and Gualerzi, C. (1990). Translational control of prokaryotic gene expression. Trends in Genetics, 6:78-85.<br/><br />
Salis, H. M., Mirsky, E. A., Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27:946-950.<br/><br />
Salis, H. M. (2011). The Ribosome Binding Site Calculator. Methods in enzymology, 498.<br/><br />
Serino, L., <i>et al.</i> (1995). Structural genes for salicylate biosynthesis from chorismate in <i>Pseudomonas Aeruginosa</i>. Molecular & General Genetics, 249(2):217-228.<br/><br />
Shiue, E., Prather, K. L. J. (2012). Synthetic biology devices as tools for metabolic engineering. Biochemical Engineering Journal, 65:82-89.<br/><br />
Shrader, T. E., Tobias, J. W., Varshavsky, A. (1993). The N-End Rule in <i>Escherichia coli</i>: Cloning and Analysis of the Leucyl, Phenylalanyl-tRNA-Protein Transferase Gene aat. Journal of Bacteriology, 175(14):4364-4374.<br/><br />
Tobias, J. W., Shrader, T. E., Rocap, G., Varshavsky, A. (1991). The N-End Rule in Bacteria. Science,254:1374-1377.<br />
Varshavsky, A. (1997). The N-end rule pathway of protein degradation. Genes to Cell, 2:13–28.<br/><br />
Books:<br/><br />
Watson, J. D., Baker, T. A., Bell, S. P., Gann, A., Levine, M., Losick, R. (2013). Molecular Biology of the Gene (7th edition). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.<br/><br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T20:41:44Z<p>Veerledewever: </p>
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<p>Design of the honeydew model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Wetlab Overview"><h4>Wetlab Overview</h4></a><br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b> <i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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<h3>Gettin' the gene</h3><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘bell pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen in the control group(see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results.<br/><br />
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<b>Figure 3</b> shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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We focussed on BBa_K1060009 in <b>Figure 4 and 5</b> where we show further characterizations : MgCl2 was added to the growth medium as it is suggested to improve solubility and functionality of EBF Synthase. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 4). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 5. The growth curves (Figure 4) showed that DH5a, transformed with BBaK1060009, had growth issues, irrespective of the presence of MgCl2. Interestingly, these cells showed an additional band around 50kDa (Figure 5) which was not observed in the BL21(DE3) transformed strains (data not shown) nor the control strain (Figure 5). Theoretical predictions suggest the EBF Synthase product should run around 66kDa, yet this still needs to be proven.<br/><br/><br />
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<b>Figure 6</b> displays a comparison between the control strain and the BL21(DE3) expression strain, the latter transformed with BBa_K1060009 and in the presence/absence of MgCl2. Here we could not observe the additional band, seen in Figure 5(*).<br/><br/><br />
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Interestingly, the DH5a strains show a reduced growth rate (Figure 4) AND an additional band (Figure 5); a correlation which would be in line with our Flux Balance Analyses results (see Modelling at the cellular level). <br />
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Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF Synthase gene product (Figure 3; 5) and possibly the increased production of a secondary protein (Figure 3). Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which could lead to increased EBF production, equally inhibitory as a too low concentration.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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<p>Figure 5: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing DH5a <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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<p>Figure 6: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing BL21(DE3) <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. No differences in the banding pattern could be observed.</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T20:36:09Z<p>Veerledewever: </p>
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<p>Design of the honeydew model</p><br />
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b> <i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘bell pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen in the control group(see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results.<br/><br />
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<b>Figure 3</b> shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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We focussed on BBa_K1060009 in <b>Figure 4 and 5</b> where we show further characterizations : MgCl2 was added to the growth medium as it is suggested to improve solubility and functionality of EBF Synthase. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 4). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 5. The growth curves (Figure 4) showed that DH5a, transformed with BBaK1060009, had growth issues, irrespective of the presence of MgCl2. Interestingly, these cells showed an additional band around 50kDa (Figure 5) which was not observed in the BL21(DE3) transformed strains (data not shown) nor the control strain (Figure 5). Theoretical predictions suggest the EBF Synthase product should run around 66kDa, yet this still needs to be proven.<br/><br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF Synthase gene product (Figure 3; 5) and possibly the increased production of a secondary protein (Figure 3). Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which could lead to increased EBF production, equally inhibitory as a too low concentration.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
<a href="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" target="_blank"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"></a><br />
<p>Figure 5: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing DH5a <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
<a href="https://static.igem.org/mediawiki/2013/b/b0/Controlproteins_EBFS.jpg" target="_blank"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b0/Controlproteins_EBFS.jpg" alt=control levels"></a><br />
<p>Figure 6: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing BL21(DE3) <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. No differences in the banding pattern could be observed.</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/File:Controlproteins_EBFS.jpgFile:Controlproteins EBFS.jpg2013-10-28T20:30:42Z<p>Veerledewever: </p>
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<div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T19:27:40Z<p>Veerledewever: </p>
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<h3>Honeydew model</h3> </a><br />
<p>Design of the honeydew model</p><br />
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<h3>E-β-Farnesene</h3><br />
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<p>You are here!</p><br />
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<h3>Methyl Salicylate</h3> </a><br />
<p>BanAphids produce MeS!</p><br />
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<h3>qPCR</h3> </a><br />
<p>Wetlab data for the MeS model</p><br />
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<h3 class="bg-green">E-β-Farnesene</h3><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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<h3 class="bg-green">General Background of the EBF synthase</h3><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen in the control group(see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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<h3 class="bg-green">EBF Synthase Expression Experiment</h3><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results.<br/><br />
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<b>Figure 3</b> shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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We focussed on BBa_K1060009 in <b>Figure 4 and 5</b> where we show further characterizations : MgCl2 was added to the growth medium as it is suggested to improve solubility and functionality of EBF Synthase. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 4). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 5. The growth curves (Figure 4) showed that DH5a, transformed with BBaK1060009, had growth issues, irrespective of the presence of MgCl2. Interestingly, these cells showed an additional band around 50kDa (Figure 5) which was not observed in the BL21(DE3) transformed strains (data not shown) nor the control strain (Figure 5). Theoretical predictions suggest the EBF Synthase product should run around 66kDa, yet this still needs to be proven.<br/><br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF Synthase gene product (Figure 3; 5) and possibly the increased production of a secondary protein (Figure 3). Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which could lead to increased EBF production, equally inhibitory as a too low concentration.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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<p>Figure 5: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T19:21:18Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results.<br/><br />
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<b>Figure 3<b/> shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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We focussed on BBa_K1060009 in <b>Figure 4 and 5<b/> where we show further characterizations : MgCl2 was added to the growth medium as it is suggested to improve solubility and functionality of EBF Synthase. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 4). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 5. The growth curves (Figure 4) showed that DH5a, transformed with BBaK1060009, had growth issues, irrespective of the presence of MgCl2. Interestingly, these cells showed an additional band around 50kDa (Figure 5) which was not observed in the BL21(DE3) transformed strains (data not shown) nor the control strain (Figure 5). Theoretical predictions suggest the EBF Synthase product should run around 66kDa, yet this still needs to be proven.<br />
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Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF Synthase gene product (Figure 3; 5) and possibly the increased production of a secondary protein (Figure 3). Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which could lead to increased EBF production, equally inhibitory as a too low concentration.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
<a href="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" target="_blank"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"></a><br />
<p>Figure 5: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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<h3 class="bg-green">The pathway to E-β-Farnesene</h3><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T19:15:28Z<p>Veerledewever: </p>
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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<h3 class="bg-green">General Background of the EBF synthase</h3><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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<h3>Gettin' the gene</h3><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results.<br/><br />
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Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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We focussed on BBa_K1060009 in Figure 4 and 5 where we show further characterizations : MgCl2 was added to the growth medium as it is suggested to improve solubility and functionality of EBF Synthase. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 4). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 5. <br />
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Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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<p>Figure 5: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T19:10:52Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<img src="https://static.igem.org/mediawiki/2013/e/e8/Bio_assay_moving_aphids.tif"></a> <br/><br />
<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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<h3 class="bg-green">EBF Synthase Expression Experiment</h3><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br/><br />
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Figure 4 and 5 show further characterizations : MgCl2 is suggested to improve solubility and functionality of the EBF Synthase, so we added it into the growth medium. We transformed BBa_K1060009 into DH5a and BL21(DE3), with/without MgCl2. Simultaneously we took an "empty" strain as a control, again with/without MgCl2. We grew overnight cultures of these 6 strains, inoculated 50ml each to a final Optical Density (600nm) of 0.05 and followed cellular growth over time (Figure 5). Simultaneously, we took samples for protein extraction at the OD600nm's indicated in Figure 4.<br />
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Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<a href="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" target="_blank"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"></a><br />
<p>Figure 4: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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<img src="https://static.igem.org/mediawiki/2013/c/c5/Growthcurves_oct28_EBFS.jpg" alt="growth curves"></a><br />
<p>Figure 5: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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<h3 class="bg-green">The pathway to E-β-Farnesene</h3><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T19:01:58Z<p>Veerledewever: </p>
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<h3>Honeydew model</h3> </a><br />
<p>Design of the honeydew model</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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<h3 class="bg-background">General Background of the EBF synthase</h3><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*).</p><br />
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<p>Figure 5: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:57:25Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"><br />
<p>Figure 4: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*) </p><br />
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<p>Figure 5: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:56:35Z<p>Veerledewever: </p>
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Wetlab Overview"><h4>Wetlab Overview</h4></a><br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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<h3 class="bg-background">General Background of the EBF synthase</h3><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
<center><img src="https://static.igem.org/mediawiki/2013/5/5e/Reaction.jpg" alt="reaction"/></center><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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<h3 class="bg-background">EBF Synthase Expression Experiment</h3><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"><br />
<p>Figure 4: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3)) and an EBF Synthase containing <i>E. coli</i> strain, each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie. Differences in the banding pattern are indicated with an (*) </p><br />
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<p>Figure 5: Growth curves of the EBF Synthase expressing bacterial strains. We followed 6 strains in total, as indicated in the figure legend. Strains were grown overnight and a sample of each taken and diluted to an )OD600nm of 0.05; our effective starting point. Initially, strains jointly proceeded through lag phase yet in exponential phase, differences in growth rate can be observed.<br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:52:03Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of endpoint protein extracts from the EBF Synthase biobricks. Differences between the lanes are indicated with a red arrow.</p><br />
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<p>Figure 4: Colloidal coomassie stain of protein extracts from a control strain (BL21(DE3) and an EBF Synthase containing <i>E. coli</i> strain each in the presence/absence of 5 mM MgCl2. Samples were taken at increasing optical densities (indicated on the figure), protein extracts separated on a 10% SDS-PAGE and stained with colloidal coomassie.</p><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:45:49Z<p>Veerledewever: </p>
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Wetlab Overview"><h4>Wetlab Overview</h4></a><br />
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<a href="#Our Bricks"><h4>Our Bricks</h4></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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<h3 class="bg-background">General Background of the EBF synthase</h3><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
<center><img src="https://static.igem.org/mediawiki/2013/5/5e/Reaction.jpg" alt="reaction"/></center><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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<h3>gBlocks</h3><br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figure 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figure 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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<h3 class="bg-background">GC-MS analysis of EBF</h3><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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<h3 class="bg-background">EBF Synthase Expression Experiment</h3><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. Figure 3 shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<p>Figure 3: Colloidal coommassie stain of protein extracts from the EBF Synthse biobricks.</p><br />
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<h3 class="bg-background">The pathway to E-β-Farnesene</h3><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:39:47Z<p>Veerledewever: </p>
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figuur 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figuur 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a> and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a combination construct consisting of a Lac operator and medium strength promoter, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. First, we took so-called end-point assays were strains were allowed to grow for 8hrs after inoculation. Bacterial pellets were harvested and proteins extracted.<br/><br />
Protein extracts were separated on a 10% SDS-PAGE gel. Since we have no access to a specific antibody we verified the overall protein levels by colloidal coomassie stain comparisons. Here we show the most interesting results. The figure shows some slight additional bands in lane a (around 110kDa and around 60kDa) which is the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lanes. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band.<br/><br />
Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T18:29:30Z<p>Veerledewever: </p>
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
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<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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<h3 class="bg-background">Wetlab Work Overview</h3><br />
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<h3>Gettin' the gene</h3><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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<h3>Confirmation</h3><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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<h3>gBlocks</h3><br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<h3 class="bg-background">Our Bricks</h3><br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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<h3 class="bg-background">Aphid experiments</h3><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<a href="https://static.igem.org/mediawiki/2013/7/7f/Bio_assay_top_aphids.tif" target="_blank"><br />
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<p>Figuur 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<img src="https://static.igem.org/mediawiki/2013/e/e8/Bio_assay_moving_aphids.tif"></a> <br/><br />
<p>Figuur 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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<a id="GC-MS analysis of EBF"></a><br />
<h3 class="bg-background">GC-MS analysis of EBF</h3><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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<h3 class="bg-background">EBF Synthase Expression Experiment</h3><br />
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Apart from the GC-MS analysis, we also performed protein expression studies with our EBF Synthase bricks.<br/><br />
First, we transformed our 3 different EBF synthase bricks, <a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a>)and <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a>, controlled by a medium-strength promoter; a strong promoter or a Lac operator, respectively, into BL21(DE3), an <i>E.coli</i> expression strain. Cells were grown to <br />
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, we transformed our different EBF synthase bricks in an expression strain, grew these under different temperatures, times and, if possible, IPTG induction levels. Bacterial pellets were harvested and proteins extracted.<br/><br />
Here we show the most interesting results. The figure shows some slight additional bands in lane a (around 110kDa and around 60kDa), the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lane. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band. Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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<img src="https://static.igem.org/mediawiki/2013/6/6e/Protein_oct28_EBFS.jpg" alt="comparative expression"><br />
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<h3 class="bg-background">The pathway to E-β-Farnesene</h3><br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<a href="https://static.igem.org/mediawiki/2013/2/2a/Mevalonate_pathway.jpg" target="_blank"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2a/Mevalonate_pathway.jpg" alt="Mevalonate Pathway"></a><br />
<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<h3 class="bg-background">Problems & Solutions Concerning The Pathway</h3><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledeweverhttp://2013.igem.org/Team:KU_Leuven/Project/Glucosemodel/EBFTeam:KU Leuven/Project/Glucosemodel/EBF2013-10-28T17:10:54Z<p>Veerledewever: </p>
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<p>Design of the honeydew model</p><br />
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<p>You are here!</p><br />
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<p>BanAphids produce MeS!</p><br />
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<p>Wetlab data for the MeS model</p><br />
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In this part, we will give some more information about the E-β-farnesene (EBF) part of the project. EBF is an <b>alarm pheromone</b>, released by almost all of the 4000 aphid species known thus far <b>in response to the presence of predators</b> (e.g. the ladybug) or other disturbances. In response to the produced EBF, aphids change their metabolism and turn into a winged form, allowing them to "flee the scene" and thus increase their survival rate. Apart from the short term repelling effect, <b>EBF can also cause long term effects: changes in aphid’s development, fecundity, survival when introduced to different growth stages, etc.</b> Moreover, natural aphid predators such as the ladybugs are attracted by EBF. <br/><br />
Hence, having our BanAphids produce EBF should help to <a href="https://2013.igem.org/Team:KU_Leuven/Project/Ecological/Background#E-β-farnesene">repel aphids</a> from our plant of choice. In the following sections, we will give you a <a href="#background">general background of EBF synthase</a> followed by an overview of the <a href="#model">model and the genes</a>, the <a href="#wetlab">wetlab work</a> and the <a href="#bricks">biobricks</a> we built for the EBF part. We were also able to characterise these biobricks. We showed that our biobrick has an effect on <a href="#aphid experiments">aphids</a> and used <a href="#SDSPAGE">SDS-PAGE</a> to show that it was EBF-synthase was produced. Finally we have made some suggestion how to optimise the production of EBF in the future. For this we will take you on a tour through <a href="#pathway">the pathways that result in EBF</a> and the problems that arise with this. Of course we have added possible <a href="#problemssolutions">solutions to these problems</a>.<br />
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<a href="#EBF synthase Background"><h4>EBF synthase Background</h4></a><br />
<a href="#EBF pathway"><p>Pathway to E-β-farnesene</p></a><br />
<a href="#Problems and solutions"><p>Problems and solutions</p></a><br />
<a href="#Model and the genes"><p>The model and the genes</p></a> <br />
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<a href="#Characterisation"><h4>Characterisation</h4></a><br />
<a href="#Aphid Experiments"><p>Aphid Experiments</p></a><br />
<a href="#EBF synthase expression"><p>EBF synthase expression</p></a><br />
<a href="#References"><h4>References</h4></a><br />
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We cloned and expressed the EBF synthase gene in <i>E. coli</i>. This enzyme will break down (2E,6E)-farnesyl diphosphate into (E)-β-farnesene (EBF) and diphosphate (see reaction scheme below).<br/><br />
The enzyme prefers bivalent cations as cofactors; a Mg<sup>2+</sup> concentration of 5 mM should be beneficial for EBF synthase function. The ideal pH for EBF synthase will be between 5.5 and 7.</p><br />
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The EBF construct we designed consists of a <b>constitutive promoter with a <i>lac</i> operator, the EBF synthase itself and a double terminator. We used <a href="http://parts.igem.org/Part:BBa_B0015" target="_blank">BBa_B0015</a> for the double terminator.</b> EBF is not only made by aphids but also by plants and other organisms in a form of biomimicry. We obtained two different sources of the EBF gene. One gene originates from the <b>soil bacterium</b><i>Streptomyces coelicolor</i> (Centre of Microbial and Plant Genetics of KU Leuven). We chose this plant-residing bacterium because it would be a perfect chassis for the ultimate expression of EBF in our <i>E. coligy</i> system. The other EBF gene is from <b>the plant <i>Artemisia annua</i> (sweet wormwood)</b> and was a kind gift from Professor Peter Brodelius (Kalmar University, Sweden). Here we were inspired with the plant origin. The K<sub>M</sub> for the <i>Artemisia annua</i> protein is calculated at 0.0021 mM, with a Kcat/K<sub>M</sub>=4.5 and a turnover number of 0.0095 s<sup>-1</sup>. For the <i>Streptomyces coelicolor</i> protein the K<sub>M</sub> is 0.0168 mM and the turnover number 0.019 s<sup>-1</sup>. <br/><br />
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Unfortunately, the EBF synthase from <i>Streptomyces coelicolor</i> is a bifunctional enzyme, not only processing β-farnesene but also containing albaflavenone synthase activity. For this reason, we chose to follow up on the <i>Artemisia annua</i> gene and product. <br />
For our construct, our first choice was a medium strength promoter with medium RBS (<a href="http://parts.igem.org/Part:BBa_K608006" target="_blank">BBa_K608006</a>); we nonetheless also made the construct with a strong promoter and RBS. The <i>lac</i> operator in front of the EBF synthase gene will allow us to switch the transcription of the EBF synthase gene on and off.</p><br />
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In the case of the EBF synthase gene from <i>Streptomyces coelicolor</i>, we amplified this gene with a colony PCR. The EBF synthase gene from <i>Artemisia annua</i> was received in the pET28 vector from professor Brodelius (Kalmar University, Sweden). In this gene an additional <i>EcoRI</i> restriction site was present, which would conflict with the standard iGEM cloning work. Therefore <b>we removed this site via site directed mutagenesis</b> after transferring the gene into the iGEM pSB1C3 backbone. <br />
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<h3>Cutting and pasting</h3><br />
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Once we obtained the target gene (EBF) in the standard pSB1C3 backbone, we started our cloning work. We used plasmid pSB1C3 with a promoter or terminator as chassis, cut this open and inserted the gene of interest. When ligating the insert in front of the double terminator, we cut the vector with <i>EcoRI</i> and <i>XbaI</i>, and the insert with <i>EcoRI</i> and <i>SpeI</i>. The promotor vector on the other hand is cut with <i>SpeI</i> and <i>PstI</i> restriction sites, and the insert is cut with <i>XbaI</i> and <i>PstI</i> restriction sites. This works because <i>SpeI</i> and <i>XbaI</i> are isoschizomers.<br/><br />
Ligations were performed in parallel in two different ways. In one setup we ligated for 20 minutes at 16 ℃, and in comparison, the second ligation of the same products was conducted at 16 ℃ overnight.<br/><br />
For transformation, we used both chemically competent cells and electrocompetent cells. Electroporation had a higher efficiency when compared to heat shock transformation. </p><br />
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After we observed colonies the next day, we needed to confirm the products. The first step we did was usually a colony PCR to check if the insert was in the vector, this was followed up by digestion confirmation after the plasmid extraction. <b>Only the plasmids which succeeded in both controls were send for sequencing, the final confirmation</b>.<br />
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Meanwhile, we also built the EBF construct with a <i>lac</i> operator between the promoter and gene, using the gBlock principle. We designed the gBlocks, assembled them and ligated the insert into pSB1C3 backbone. The colonies obtained also went through the three confirmation steps mentioned above before we were satisfied. <br />
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For more details on the labwork and the wetlab difficulties as well as how we overcame them, please consult <a href="https://2013.igem.org/Team:KU_Leuven/Journal/EBF/wetlab">our wetlab journal</a>.<br />
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<b>After we overcame a lot of difficulties, we finally made the following bricks at the end of the summer.</b></p><ol><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060001" target="_blank">BBa_K1060001</a> This is a coding biobrick with the EBF synthase gene from <i>Streptomyces coelicolor</i> in pSB1C3 backbone and an insert length of 1386bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060002" target="_blank">BBa_K1060002</a> This is another coding biobrick with EBF synthase gene from <i>Artemisia annua</i> in pSB1C3 backbone and an insert length of 1725bp.</li><br />
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<li><a href="http://parts.igem.org/Part:BBa_K1060008" target="_blank">BBa_K1060008</a> This is an intermediate biobrick with EBF of <i>Artemisia annua</i> in front of a double terminator. </li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060009" target="_blank">BBa_K1060009</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> in the pSB1C3 backbone and an insert length of 1924bp.</li><br />
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<li><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_K1060011" target="_blank">BBa_K1060011</a> This is a generator biobrick with a medium constitutive expression of EBF synthase from <i>Artemisia annua</i> AND a <i>lac</i> operator after the promoter and an insert length of 1965bp.</li><br />
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With our EBF synthase constructs ready, we tested them with several aphid experiments.<br/><br />
Our pilot experiment tested the medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. We placed aphids on a leaf in the middle of a huge petri dish, an EBF-producing bacterium plate on the left, a control on the right. In <b>the resulting video we observed that the general trend of aphid movement was away from the EBF-producing bacterium. These results suggest our EBF synthase producing bacteria seemed to work.</b> <br/><br />
Moreover, we also tried another set-up with our high strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</b></a>. This time we connected the leaves that were on the EBF-producing bacteria plate with those on the control plate and with the leaf in the middle where the aphids resided. This facilitates movement of the aphids to other leaves. However, there was no significant difference between the amount of aphids on the control leaves versus the <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K106011</a> leaves. The lac operator in this construct may interfere with the production of significant amounts of EBF.</p><br />
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In addition, we also examined the aphid's behavior without a leaf as a starting point. We put 30 aphids in the middle of a huge petri dish, on the left side we placed a leaf with 10µl of EBF-producing bacteria and on the right side we placed a non-treated leaf as control. Thus, we offered the aphids the chance to go searching for food. After 2 hours we counted the number of aphids on the leaves, there were 4 aphids on the leaf where the EBF was produced and 6 aphids on the control leaf, the rest of aphids just walked randomly in the big petri dish. For lack of time, we could unfortunately not repeat this experiment.<br/><br />
<b>Our pilot experiments indicated a trend in the right direction.</b> Several aspects of the setup can still be optimized in the future, for example the amount of bacteria, the strength of the promoter, the ventilation of the setup, the incubation time and the temperature, etc. The reason for this is that the concentration of EBF is essential to trigger the desired response in the aphids. Both too high and too low concentrations will lead to aphid insensitivity. <br/><br />
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To back-up our pilot experiment, we tested again our medium strength EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a>. <br/><br />
This time we used <b>3 biological repeats</b> for both a control setup (BL21 wild-type) and our EBF producing strain. From an infested <i>Capsicum annuum</i> (‘sweet pepper’ or ‘belt pepper’) plant, leaves from similar sizes were cut and placed it in the glass containers with sufficient natural ventilation. The aphids were then left for one hour before introducing our bacterial plates. Petri dishes with 10µl of a fresh overnight culture (~OD 1.6) were place under each leaf. The amount of aphids on the top side of each leaf were counted and used as a reference point (t=0). Every half hour the aphids were counted in a standardized manner. The amount of aphids moving on each leaf were also counted at each time point. We observed a trend of more aphids scattering away from the leave in the EBF setup but no statistical differences were seen (see Figure 1). <br/> <br/><br />
<b>When looking at the percentage of aphids moving at each time point, we observed a similar trend of aphids being more agitated in the EBF setup compared to the control (see Figure 2). This difference was statistacilly significant (P-value = 0.013) at 180 minutes after introducing our bacteria. </b><br />
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<p>Figuur 1: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids on the top side of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids compared to time point 0 ± standard error of mean. The average total count of aphids on the 3 leaves for EBF and control are also shown.</p><br />
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<p>Figuur 2: BBa_K1060009 (EBF) or BL21 (control) bacterial plates were placed underneath leaves (n=3) from <i>Capsicum annuum</i>. The amount of aphids moving around on the top of each leaf were counted at 0, 30, 60, 90, 150 and 180 minutes after the introduction of the bacteria. Data are represented as % of aphids moving compared to the amount of aphids on the top side of each leaf ± standard error of mean. The average total count of aphids moving on the 3 leaves for EBF and control are also shown.</p><br />
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To confirm that our <i>E. coli</i> strain containing our EBF synthase producing brick <b><a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</b></a> produced EBF, we turned to GC-MS analysis with the help of Dr. Jan Baeten (Centre for Surface Chemistry and Catalysis; Prof. Dirk De Vos). Supernatant of bacterial cultures were extacted 3 times with hexane. GC-MS analyses were carried out using a 7890A Agilent gas chromatograph coupled to a 5977A mass spectrometric detector. The GC was equiped with a HP-5MS capillary column (30m x 0.25 mm x 0.25 µm). 1 µl of each sample was injected using splitless (head pressure 9.15 psi) or pulsed splitless (head pressure 20 psi) injection at a temperature of 250 °C. The initial oven temperature of 40 °C was held for 1 min., ramped at 6 °C / min. to 124 °C, then ramped at 20 °C / min. to 320 °C and finally kept at this temperature for 5 min. The transfer line and ion source were held at 320 °C and 230 °C, respectively. Mass spectra were taken between masses m/z 30-300 with an ionization potential of 70 eV. Retention indices of standards were determined by co-injection of a C7-C30 n-alkane mixture (Supelco) and were compared with published retention indices.<br />
To test if EBF could be detected by this setup, farnesene (Sigma-Aldrich, W383902, mixture of isomers) was used as a standard. In total 16-17 peaks were observed of which the following could be identified with the help of the mass spec and retention index (RI). E-beta-farnesene RI = 1459 (lit. 1458-59), (3Z,6E)-alfa-farnesene RI = 1490 (lit. 1487) and (3E,6E)-alfa-farnesene RI = 1506 (lit. 1506-1510). All samples were measured in duplicate; once in SCAN-modus, splitless injectie of 1 µl, and once in SIM-modus (most sensitive), pulsed splitless injectie of 4 µl. No EBF could be detected for our samples, suggesting that the concentration of EBF produced in our cultures is below the threshold values.</p><br />
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As another approach to prove our constructs (<a href="http://parts.igem.org/Part:BBa_K1060009" target="_blank">BBa_K1060009</a>, <a href="http://parts.igem.org/Part:BBa_K1060011" target="_blank">BBa_K1060011</a> and <a href="http://parts.igem.org/Part:BBa_K1060014" target="_blank">BBa_K1060014</a>), we transformed our different EBF synthase bricks in an <i>E.coli</i> expression strain, grew these under different temperatures, times and, if possible, IPTG induction levels. Bacterial pellets were harvested and proteins extracted.<br/><br />
Here we show the most interesting results. The figure shows some slight additional bands in lane a (around 110kDa and around 60kDa), the protein extract from the lacI operator medium strength promoter construct. These bands are less clear in the medium and high strength promoter lane. The expected size of the EBF synthase protein is around 66kDa which could fit with the lower band. Gel extraction and Mass Spectrometry based identification will confirm if these bands represent the EBF synthase gene and possibly the increased production of a secondary protein. Interestingly, the lacI medium promoter construct did not influence aphid behaviour. Possibly the expression of EBF synthase is just too high, which would be equally inhibitory as a too low concentration. Other approaches to better identify the functionality of this construct would be via a gas chromatography analysis to directly measure the amounts of EBF produced.<br />
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β-farnesene is a terpenoid that is converted from farnesyl pyrophosphate (FPP) by the enzyme β-farnesene synthase (EC 4.2.3.47). <br/><br />
<b>FPP is the precursor of β-farnesene</b>, that is produced by the building blocks, the molecules isopentenyl pyrophosphate (IPP) and its isomer dimethylallylpyrophosphate (DMAPP).<br/><br />
These precursors of farnesyl pyrophosphate can be produced by several metabolic pathways. Most <b>prokaryotes use the non-mevalonate or DXP pathway</b>, producing IPP starting from glyceraldehyde-3-phosphate and pyruvate. <b>Eukaryotes, except for plants, exclusively use the mevalonate pathway</b>, producing IPP starting from acetyl-CoA. Plants use both pathways.</p><br />
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<p align="justify">Mevalonate pathway, showing the conversion of acetyl-CoA to general terpenoid precursor IPP and its isomer DMAPP. </p><br />
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<p align="justify">On the left you can see the non-mevalonate pathway or DXP pathway, showing the conversion of pyruvate and glyceraldehyde-3-phosphate to the terpenoid precursor IPP and its isomer DMAPP.<br/><br />
Pyr = pyruvate, G3P = glyceraldehyde-3-phosphate, DXP = 1-deoxy-D-xylulose 5-phosphate, MEP = 2-C-methylerythritol 4-phosphate, CDP-ME = 4-phosphocytidyl-2-C-methylerythritol, CDP-MEP = 4-phosphocytidyl-2-C-methyl-D-erythritol 2-phosphate, MEcPP = 2-C-methyl-D-erythritol 2,4-cyclopyrophosphate, HMB-PP = (E)-4-Hydroxy-3-methyl-but-2-enyl pyrophosphate, DXS = DXP synthase, DXR = DXP reductase, CMS = CDP-ME synthase, CMK = CDP-ME kinase, MCS = MEcPP synthase, HDS = HMB-PP synthase, HDR = HMB-PP reductase</p><br />
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<p align="justify">FPP is an important precursor, used for the biosynthesis of lots and lots of compounds. Once we insert a plasmid containing the β-farnesene synthase gene, we may obtain only a very small amount of β-farnesene, since the precursor amount wasn't increased and there simply isn’t enough FPP available to produce the amount of β-farnesene to fully use the capacity of the EBF synthase enzyme we brought in.<br/><br />
A solution may be to co-transform plasmids to engineer a mevalonate pathway in <i>E. coli</i>, thereby upregulating the production of FPP. This larger amount of FPP may then be converted to β-farnesene, creating a large enough amount of this volatile. This was demonstrated many times by J.D. Keasling in <i>S. cerervisiae</i>, while Martin <i>et al.</i>, (2003) implemented this mevalonate pathway in <i>E. coli</i>. In the article, they described their successful efforts to create a high level production of amorphadiene by introducing the mevalonate pathway in <i>E. coli</i>. However, expression of this heterologous pathway led to such an abundance of isoprenoid precursors that cells ceased to grow or mutated to overcome the toxicity. This once again shows the need for a controlled production of the elements in this pathway; too much is equally detrimental as too little.</p><br />
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<p align="justify"><b>The genes involved in the mevalonate pathway</b><br/>(Martin <i>et al.</i>)</p><br />
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<p align="justify">Since there are <b>eight genes </b>responsible for the mevalonate pathway, Martin <i>et al.</i> decided to split them up into <b>two parts</b>. A first plasmid named <b>pMevT</b>, responsible for the conversion of acetyl-CoA to mevalonate, harboring the <i>atoB</i>, <i>HMGS</i> and <i>tHMGR</i> genes into a pBAD33 vector, and a second one named <b>pMBIS</b>, harboring the <i>ERG12</i>, <i>ERG8</i>, <i>MVD1</i>, <i>idi</i> and <i>ispA</i> genes into a pBBR1MCS-3 plasmid. Coexpression of these two operons in an <i>ispC</i> deficient <i>E. coli</i> strain produced the terpenes, even in the absence of mevalonate, indicating that the mevalonate pathway works. <b>A combined expression of their recombinant mevalonate pathway and the synthetic gene product (ADS in their case) resulted in greatly improved yields</b>.</p><br />
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<p align="justify">Even though we do not need a very high production of EBF it would be definitely better to optimise the pathway, by using the plasmids pMevT and pMBIS, described above. <b>Implementing them into our BanAphids along with the synthetic β-farnesene synthase gene could result into high yields of β-farnesene. This way the amount of EBF can be easily changed via the amount of bacteria used or the concentration of the cofactor Mg<sup>2+</sup></b>.<br/>Due to the short amount of time iGEM offered we did not yet started doing this, but this is definitely something future teams might look into.</p><br />
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<p align="justify">Kajiwara S., Fraser P., Kondo K., Misawa N., Expression of an exogenous isopentenyl diphosphate isomerase gene enhances isoprenoid synthesis in Escherichia coli, Biochem J. 324, 421-426 (1997).<br/><br />
Martin V., Pitera D., Withers S., Newman J., Keasling J., Engineering a mevalonate pathway in Escherichia coli for production of terpenoids, Nature Biotechnology 21(7), 796-802 (2003). <br />
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</div></div>Veerledewever