http://2013.igem.org/wiki/index.php?title=Special:Contributions/Kenta&feed=atom&limit=50&target=Kenta&year=&month=2013.igem.org - User contributions [en]2024-03-29T12:22:31ZFrom 2013.igem.orgMediaWiki 1.16.5http://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-10-29T03:29:30Z<p>Kenta: </p>
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<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
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<h1 id="common-header-title">Maestro <span class="italic">E. coli</span></h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<div id="hokkaidou-contents"><br />
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<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1(E1010)-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1(BBa_E1010) expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration among used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn't be measured by just counting colonies on a plate. There are needed more intermolecular assay for measuring the enzyme activity.<br />
</p><br />
<br />
<br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bc/HokkaidoU_2013_Fig5_in_Promoter-Result_800.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<h3>Comparison of assay results</h3><br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<div class="clearfix"></div><br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Modeling"><div class="arrow-div"></div><span>Modeling</span></a><br />
</div><br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/ExamplesTeam:HokkaidoU Japan/Shuffling Kit/Examples2013-10-29T03:21:54Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Shuffling Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<h1>Demonstrations for Usecase Example</h1><br />
<p>We will show some interesting demonstrations of our kits, Promoter Selector and RBS Selector!</p><br />
<br />
<br />
<h2>Promoter Selector</h2><br />
<p>Let's select the best promoter for Kanamycin resistance by Promoter Selector.</p><br />
<p>For this demonstration we decided to use the expression of Kanamycin resistance. Changing the concentration of Kanamycin in agar plate, it is estimated that different promoter will be chosen by our Promoter Selector (fig.1).</p><br />
<br />
<p>If the concentration of Kanamycin was high, the colony with strong promoter would survive. Therefore, only one or two colors would appear and indicate the first and second biggest occupancy rate on the plate.<br />
If the concentration of Kanamycin was low, colonies with even weak promoters could be able to survive. So in this way many colors of colonies would appear (fig.2).<br />
</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/Fig1_in_example_132029new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.1 Different promoter express each colors.</span></div><br />
</div><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2d/Fig2_in_example_HokkaidoU_2013.png"><br />
<div style="padding-bottom: 0;"><span class="bold">fig.2 Difference of Kanamycin concentration.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<br />
<p>Optimum concentration of Kanamycin: in LB is 50 mg/ml <br><br />
We prepared optimum and other 3 concentration plates. <br />
</p><br />
<br />
<ul><br />
<li>Plate A: Kanamycin 125 mg per plate</li><br />
<li>Plate B: Kanamycin 250 mg per plate</li><br />
<li>Plate C: Kanamycin 500 mg per plate (optimum concentration)</li><br />
<li>Plate D: Kanamycin 1000 mg per plate</li><br />
</ul><br />
<br />
<p>Gene<br />
Vector: pSB1C3<br />
</p><br />
<p>We cloned Kanamycin resistance gene from pSB3K3, by using BsaI adding primer. Used the Promoter Selector (K1084501, K1084502, K1084503, K1084504, K1084505 ).</p><br />
<br />
<p><br />
Culture: 37 &deg;C, for 48h<br><br />
</p><br />
<br />
<h3>Results</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/2/28/HokkaidoU_2013_Km_resistance_assay_summary_data.png"><br />
<div><span class="bold">fig.4 Graph of number and rate, and table of number of colonies size over 1mm diameter.</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/ca/POK_DEMO_48h_newnew_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.3 Picture of plate B (Kanamycine 250 mg). The colonies showed several colors.</span></div><br />
</div><br />
<br />
<p>After 48h cultivation, around 300 colonies had appeared on each LBKC (Kanamycin and Chloramphenicol) plates. We prepared LacZ&alpha expression system in Promoter Selector as negative control to estimate the success of BsaI digestion. We got 0-7 blue colonies which was expressing LacZ&alpha.<br />
</p><br />
<br />
<p> Mixed colored colonies which would have been transformed by two or more Promoter Selector were also observed. The number and rate of colonies per each plate were graphed (fig.4), with rejecting these undesirable colonies.<br />
</p><br />
<br />
<br />
<p><br />
In fig.4, legend color corresponds to Promoter Selector’s part number. The sum of colony numbers is displayed above each bar, and rate is in these sections. Number in the table referrers the number of each Promoter Selector’s colony. These data are collected by n=1.<br />
</p><br />
<br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<p><br />
Big difference did not appear among each Kanamaicin concentration. <br />
<br />
In this experiment, number of colonies derived from K1084405 (containing K1084010 promoter) had the largest rate on each plate. This result suggests that the colonies which had K1084505 could cost little resource of transcription and translation for expression of Kanamycin resistance gene, and the rest of resource had been spared to cell growth. Thus, the number of colonies might be the largest. Otherwise, the DNA solution of K1084505 Promoter Selector used at ligation was simply larger than other DNA solution. Although the result is collected from only one time assay, higher concentration of Kanamycin and much number of trials than this time will be needed.<br />
</p><br />
<br />
<p><br />
From these result and the experimental fact, the existence of Km resistance gene in Promoter Selector’s BsaI cloning section is partially confirmed. Our Promoter Selector was successfully assembled, but it does not adopted to all colonies. Then, as a result of assembling, we succeeded in making colorful colonies appear on one plate.<br />
</p><br />
<h2>RBS Selector</h2><br />
<h3>4 colors</h3><br />
<p>Let’s create all combinations by two reporter genes and make various colors on one plate!</p><br />
<br />
<p><br />
The RBS Selector we made, can randomize the strength of RBSs in the operon.<br />
For a demonstration, we decided to create all combinations by two genes; mRFP1 (BBa_E1010) and LacZ&alpha; (BBa_I732006) (fig.5). LacZ&alpha; makes the colony blue. mRFP1 makes the colony red.<br />
</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/9/90/Fig4_in_example_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.5 Create all combinations by RBS of defferent stlength mRFP1 (BBa_E1010) and LacZα (BBa_I732006).</span></div><br />
</div><br />
<br />
<p>When the RBS of mRFP1 was stronger than that of LacZα, the colony would be red. When in the opposite case, the colony would be blue. And when the strengths of both RBSs were same, colony would be white or purple (fig.6).</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/Fig5_in_example_%2Boverhang_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.6 Each combinations of RBS make different colors.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<ul><br />
<li>Assembled promoter1 (BBa_K1084001), SD2 (BBa_K1084101), SD4 (BBa_K1084102).</li><br />
<li>Spread X-GAL(250 mg)on LBC plate.</li><br />
<li>Cultured at 37 &deg;C for 26h.</li><br />
</ul><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/0/08/ROK_demo_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.7 The colonies showed red, blue, white, and purple.</span></div><br />
</div><br />
<h3>Results</h3><br />
<p><br />
We got colorful colonies; red, blue, white, and purple.<br />
<br />
<br />
</p><br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<br />
<br />
<p><br />
<br />
We can say that our RBS Selector worked exactly!!<br />
The RBSs upstream of 2 genes were randomly assembled and they had different expression level. <br />
<br />
</p><br />
<br />
<br />
<h3>64 colors</h3><br />
<br />
<p><br />
Let’s create all combinations by three reporter gene and using tandem RBS, make various colors on one plate!<br />
</p><br />
<br />
<p><br />
As the tandem RBS and the RBS selector demonstration, we randomized 64 patterns of RBS-CDS combination. By adding tandem RBS, three kinds of CDSs and GGA VACTOR, One-pot Golden Gate Assembly had done for making 64 kinds of different constructs.<br />
</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/64demo2_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.8 The 64 patterns of RBS-CDS combination constructed by RBS Selector.</span></div><br />
</div><br />
<br />
<p><br />
<h4>Method</h4><br />
<ul><br />
<li>Used tandem RBS (BBa_K1084302), GGA VECTOR that BsaI and overhang was added by PCR from K1084401, eforRed (BBa_K592012), aeBlue (BBa_K864401) and amilGFP (BBa_K592010) are assembled by Golden Gate Assembly.</li><br />
<li>Spread on LBC plate.</li><br />
<li>Cultured at 37 &deg;C, 48h.</li><br />
</ul><br />
</p><br />
<br />
<h4>Results</h4><br />
<br />
<p><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/5/55/HokkaidoU_2013_64ROK_result_1.JPG"><br />
<div><span class="bold">fig.9 The result plate of 64 pattern randomizing.</span></div><br />
</div><br />
<br />
</p><br />
<br />
<p><br />
Many yellowish green (amilGFP) and red color (mRFP1) colonies are appeared, but these colonies are undesirable colonies. As a Golden Gate assembly result, green color (strong aeBlue and amilGFP expression) appeared, and the insert DNA length was confirmed that the insert DNA has same length of Golden Gate Assembly. other colonies without blue weren’t Golden Gate Assembly result. <br />
Other color colony, white, weak green, weak amilGFP expression colonies were observed. <br />
</p><br />
<br />
<h3>Conclusion</h3><br />
<br />
<br />
<p><br />
mRFP1 expression might cause by remaining of mRFP1 expression DNA used as template for GGA VECTOR producing by PCR (K1084401), thus mRFP1 expression colonies would appear.<br />
<p><br />
<br />
<p><br />
However, the blue and green colonies appeared on the plate has the insert DNA length same with ideal Golden Gate Assembling product. Our Shuffling kit partially worked.<br />
</p><br />
<br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/Primer_Designer"><div class="arrow-div"></div><span>Primer Designer</span></a><br />
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<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/Future_Work"><div class="arrow-div"></div><span>Future Work</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/ExamplesTeam:HokkaidoU Japan/Shuffling Kit/Examples2013-10-29T02:47:38Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Shuffling Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
<div class="wrapper"><br />
<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<h1>Demonstrations for Usecase Example</h1><br />
<p>We will show some interesting demonstrations of our kits, Promoter Selector and RBS Selector!</p><br />
<br />
<br />
<h2>Promoter Selector</h2><br />
<p>Let's select the best promoter for Kanamycin resistance by Promoter Selector.</p><br />
<p>For this demonstration we decided to use the expression of Kanamycin resistance. Changing the concentration of Kanamycin in agar plate, it is estimated that different promoter will be chosen by our Promoter Selector (fig.1).</p><br />
<br />
<p>If the concentration of Kanamycin was high, the colony with strong promoter would survive. Therefore, only one or two colors would appear and indicate the first and second biggest occupancy rate on the plate.<br />
If the concentration of Kanamycin was low, colonies with even weak promoters could be able to survive. So in this way many colors of colonies would appear (fig.2).<br />
</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/Fig1_in_example_132029new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.1 Different promoter express each colors.</span></div><br />
</div><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2d/Fig2_in_example_HokkaidoU_2013.png"><br />
<div style="padding-bottom: 0;"><span class="bold">fig.2 Difference of Kanamycin concentration.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<br />
<p>Optimum concentration of Kanamycin: in LB is 50 mg/ml <br><br />
We prepared optimum and other 3 concentration plates. <br />
</p><br />
<br />
<ul><br />
<li>Plate A: Kanamycin 125 mg per plate</li><br />
<li>Plate B: Kanamycin 250 mg per plate</li><br />
<li>Plate C: Kanamycin 500 mg per plate (optimum concentration)</li><br />
<li>Plate D: Kanamycin 1000 mg per plate</li><br />
</ul><br />
<br />
<p>Gene<br />
Vector: pSB1C3<br />
</p><br />
<p>We cloned Kanamycin resistance gene from pSB3K3, by using BsaI adding primer. Used the Promoter Selector (K1084501, K1084502, K1084503, K1084504, K1084505 ).</p><br />
<br />
<p><br />
Culture: 37 &deg;C, for 48h<br><br />
</p><br />
<br />
<h3>Results</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/2/28/HokkaidoU_2013_Km_resistance_assay_summary_data.png"><br />
<div><span class="bold">fig.4 Graph of number and rate, and table of number of colonies size over 1mm diameter.</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/ca/POK_DEMO_48h_newnew_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.3 Picture of plate B (Kanamycine 250 mg). The colonies showed several colors.</span></div><br />
</div><br />
<br />
<p>After 48h cultivation, around 300 colonies had appeared on each LBKC (Kanamycin and Chloramphenicol) plates. We prepared LacZa expression in Promoter Selector system as negative control to estimate the success of Golden Gate Assembly. We got 0-7 blue colonies which was expressing LacZ alpha.<br />
</p><br />
<br />
<p> Mixed colored colonies which would have been transformed by two or more Promoter Selector were also observed. The number and rate of colonies per each plate were graphed (fig.4), with rejecting these undesirable colonies.<br />
</p><br />
<br />
<br />
<p><br />
In fig.4, legend color corresponds to Promoter Selector’s part number. The sum of colony numbers is displayed above each bar, and rate is in these sections. Number in the table referrers the number of each Promoter Selector’s colony. These data are collected by n=!.<br />
</p><br />
<br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<p><br />
Big difference did not appear among each Kanamaicin concentration. <br />
<br />
In this experiment, number of colonies derived from K1084405 (containing K1084010 promoter) had the largest rate on each plate. This result suggests that the colonies which had K1084405 could cost little resource of transcription and translation for expression of Kanamycin resistance gene, and the rest of resource had been spared to cell growth. Thus, the number of colonies might be the largest. Otherwise, the DNA solution of K1084505 Promoter Selector used at ligation was simply larger than other DNA solution. Although the result is collected from only one time assay, higher concentration of Kanamycin and much number of trials than this time will be needed.<br />
</p><br />
<br />
<p><br />
From these result and the experimental fact, the existence of Km resistance gene in Promoter Selector’s BsaI cloning section is partially confirmed. Our Promoter Selector was successfully assembled, but it does not adopted to all colonies. Then, as a result of assembling, we succeeded in making colorful colonies appear on one plate.<br />
</p><br />
<h2>RBS Selector</h2><br />
<h3>4 colors</h3><br />
<p>Let’s create all combinations by two reporter genes and make various colors on one plate!</p><br />
<br />
<p><br />
The RBS Selector we made, can randomize the strength of RBSs in the operon.<br />
For a demonstration, we decided to create all combinations by two genes; mRFP1 (BBa_E1010) and LacZ&alpha; (BBa_I732006) (fig.5). LacZ&alpha; makes the colony blue. mRFP1 makes the colony red.<br />
</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/9/90/Fig4_in_example_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.5 Create all combinations by RBS of defferent stlength mRFP1 (BBa_E1010) and LacZα (BBa_I732006).</span></div><br />
</div><br />
<br />
<p>When the RBS of mRFP1 was stronger than that of LacZα, the colony would be red. When in the opposite case, the colony would be blue. And when the strengths of both RBSs were same, colony would be white or purple (fig.6).</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/Fig5_in_example_%2Boverhang_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.6 Each combinations of RBS make different colors.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<ul><br />
<li>Assembled promoter1 (BBa_K1084001), SD2 (BBa_K1084101), SD4 (BBa_K1084102).</li><br />
<li>Spread X-GAL(250 mg)on LBC plate.</li><br />
<li>Cultured at 37 &deg;C for 26h.</li><br />
</ul><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/0/08/ROK_demo_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.7 The colonies showed red, blue, white, and purple.</span></div><br />
</div><br />
<h3>Results</h3><br />
<p><br />
We got colorful colonies; red, blue, white, and purple.<br />
<br />
<br />
</p><br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<br />
<br />
<p><br />
<br />
We can say that our RBS Selector worked exactly!!<br />
The RBSs upstream of 2 genes were randomly assembled and they had different expression level. <br />
<br />
</p><br />
<br />
<br />
<h3>64 colors</h3><br />
<br />
<p><br />
Let’s create all combinations by three reporter gene and using tandem RBS, make various colors on one plate!<br />
</p><br />
<br />
<p><br />
As the tandem RBS and the RBS selector demonstration, we randomized 64 patterns of RBS-CDS combination. By adding tandem RBS, three kinds of CDSs and GGA VACTOR, One-pot Golden Gate Assembly had done for making 64 kinds of different constructs.<br />
</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/64demo2_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.8 The 64 patterns of RBS-CDS combination constructed by RBS Selector.</span></div><br />
</div><br />
<br />
<p><br />
<h4>Method</h4><br />
<ul><br />
<li>Used tandem RBS (BBa_K1084302), GGA VECTOR that BsaI and overhang was added by PCR from K1084401, eforRed (BBa_K592012), aeBlue (BBa_K864401) and amilGFP (BBa_K592010) are assembled by Golden Gate Assembly.</li><br />
<li>Spread on LBC plate.</li><br />
<li>Cultured at 37 &deg;C, 48h.</li><br />
</ul><br />
</p><br />
<br />
<h4>Results</h4><br />
<br />
<p><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/5/55/HokkaidoU_2013_64ROK_result_1.JPG"><br />
<div><span class="bold">fig.9 The result plate of 64 pattern randomizing.</span></div><br />
</div><br />
<br />
</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_64ROK_result_1_smallest.jpgFile:HokkaidoU 2013 64ROK result 1 smallest.jpg2013-10-29T02:44:01Z<p>Kenta: </p>
<hr />
<div></div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/ExamplesTeam:HokkaidoU Japan/Shuffling Kit/Examples2013-10-29T02:32:37Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Shuffling Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
<div class="wrapper"><br />
<div id="hokkaidou-contents"><br />
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<h1>Demonstrations for Usecase Example</h1><br />
<p>We will show some interesting demonstrations of our kits, Promoter Selector and RBS Selector!</p><br />
<br />
<br />
<h2>Promoter Selector</h2><br />
<p>Let's select the best promoter for Kanamycin resistance by Promoter Selector.</p><br />
<p>For this demonstration we decided to use the expression of Kanamycin resistance. Changing the concentration of Kanamycin in agar plate, it is estimated that different promoter will be chosen by our Promoter Selector (fig.1).</p><br />
<br />
<p>If the concentration of Kanamycin was high, the colony with strong promoter would survive. Therefore, only one or two colors would appear and indicate the first and second biggest occupancy rate on the plate.<br />
If the concentration of Kanamycin was low, colonies with even weak promoters could be able to survive. So in this way many colors of colonies would appear (fig.2).<br />
</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/Fig1_in_example_132029new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.1 Different promoter express each colors.</span></div><br />
</div><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2d/Fig2_in_example_HokkaidoU_2013.png"><br />
<div style="padding-bottom: 0;"><span class="bold">fig.2 Difference of Kanamycin concentration.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<br />
<p>Optimum concentration of Kanamycin: in LB is 50 mg/ml <br><br />
We prepared optimum and other 3 concentration plates. <br />
</p><br />
<br />
<ul><br />
<li>Plate A: Kanamycin 125 mg per plate</li><br />
<li>Plate B: Kanamycin 250 mg per plate</li><br />
<li>Plate C: Kanamycin 500 mg per plate (optimum concentration)</li><br />
<li>Plate D: Kanamycin 1000 mg per plate</li><br />
</ul><br />
<br />
<p>Gene<br />
Vector: pSB1C3<br />
</p><br />
<p>We cloned Kanamycin resistance gene from pSB3K3, by using BsaI adding primer. Used the Promoter Selector (K1084501, K1084502, K1084503, K1084504, K1084505 ).</p><br />
<br />
<p><br />
Culture: 37 &deg;C, for 48h<br><br />
</p><br />
<br />
<h3>Results</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/2/28/HokkaidoU_2013_Km_resistance_assay_summary_data.png"><br />
<div><span class="bold">fig.4 Graph of number and rate, and table of number of colonies size over 1mm diameter.</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/ca/POK_DEMO_48h_newnew_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.3 Picture of plate B (Kanamycine 250 mg). The colonies showed several colors.</span></div><br />
</div><br />
<br />
<p>After 48h cultivation, around 300 colonies had appeared on each LBKC (Kanamycin and Chloramphenicol) plates. We prepared LacZa expression in Promoter Selector system as negative control to estimate the success of Golden Gate Assembly. We got 0-7 blue colonies which was expressing LacZ alpha.<br />
</p><br />
<br />
<p> Mixed colored colonies which would have been transformed by two or more Promoter Selector were also observed. The number and rate of colonies per each plate were graphed (fig.4), with rejecting these undesirable colonies.<br />
</p><br />
<br />
<br />
<p><br />
In fig.4, legend color corresponds to Promoter Selector’s part number. The sum of colony numbers is displayed above each bar, and rate is in these sections. Number in the table referrers the number of each Promoter Selector’s colony. These data are collected by n=!.<br />
</p><br />
<br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<p><br />
Big difference did not appear among each Kanamaicin concentration. <br />
<br />
In this experiment, number of colonies derived from K1084405 (containing K1084010 promoter) had the largest rate on each plate. This result suggests that the colonies which had K1084405 could cost little resource of transcription and translation for expression of Kanamycin resistance gene, and the rest of resource had been spared to cell growth. Thus, the number of colonies might be the largest. Otherwise, the DNA solution of K1084505 Promoter Selector used at ligation was simply larger than other DNA solution. Although the result is collected from only one time assay, higher concentration of Kanamycin and much number of trials than this time will be needed.<br />
</p><br />
<br />
<p><br />
From these result and the experimental fact, the existence of Km resistance gene in Promoter Selector’s BsaI cloning section is partially confirmed. Our Promoter Selector was successfully assembled, but it does not adopted to all colonies. Then, as a result of assembling, we succeeded in making colorful colonies appear on one plate.<br />
</p><br />
<h2>RBS Selector</h2><br />
<h3>4 colors</h3><br />
<p>Let’s create all combinations by two reporter genes and make various colors on one plate!</p><br />
<br />
<p><br />
The RBS Selector we made, can randomize the strength of RBSs in the operon.<br />
For a demonstration, we decided to create all combinations by two genes; mRFP1 (BBa_E1010) and LacZ&alpha; (BBa_I732006) (fig.5). LacZ&alpha; makes the colony blue. mRFP1 makes the colony red.<br />
</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/9/90/Fig4_in_example_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.5 Create all combinations by RBS of defferent stlength mRFP1 (BBa_E1010) and LacZα (BBa_I732006).</span></div><br />
</div><br />
<br />
<p>When the RBS of mRFP1 was stronger than that of LacZα, the colony would be red. When in the opposite case, the colony would be blue. And when the strengths of both RBSs were same, colony would be white or purple (fig.6).</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/Fig5_in_example_%2Boverhang_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.6 Each combinations of RBS make different colors.</span></div><br />
</div><br />
<br />
<h3>Method</h3><br />
<ul><br />
<li>Assembled promoter1 (BBa_K1084001), SD2 (BBa_K1084101), SD4 (BBa_K1084102).</li><br />
<li>Spread X-GAL(250 mg)on LBC plate.</li><br />
<li>Cultured at 37 &deg;C for 26h.</li><br />
</ul><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/0/08/ROK_demo_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.7 The colonies showed red, blue, white, and purple.</span></div><br />
</div><br />
<h3>Results</h3><br />
<p><br />
We got colorful colonies; red, blue, white, and purple.<br />
<br />
<br />
</p><br />
<div class="clearfix"></div><br />
<br />
<h3>Conclusion</h3><br />
<br />
<br />
<p><br />
<br />
We can say that our RBS Selector worked exactly!!<br />
The RBSs upstream of 2 genes were randomly assembled and they had different expression level. <br />
<br />
</p><br />
<br />
<br />
<h3>64 colors</h3><br />
<br />
<p><br />
Let’s create all combinations by three reporter gene and using tandem RBS, make various colors on one plate!<br />
</p><br />
<br />
<p><br />
As the tandem RBS and the RBS selector demonstration, we randomized 64 patterns of RBS-CDS combination. By adding tandem RBS, three kinds of CDSs and GGA VACTOR, One-pot Golden Gate Assembly had done for making 64 kinds of different constructs.<br />
</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/64demo2_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.8 The 64 patterns of RBS-CDS combination constructed by RBS Selector.</span></div><br />
</div><br />
<br />
<p><br />
<h4>Method</h4><br />
<ul><br />
<li>Used tandem RBS (BBa_K1084302), GGA VECTOR that BsaI and overhang was added by PCR from K1084401, eforRed (BBa_K592012), aeBlue (BBa_K864401) and amilGFP (BBa_K592010) are assembled by Golden Gate Assembly.</li><br />
<li>Spread on LBC plate.</li><br />
<li>Cultured at 37 &deg;C, 48h.</li><br />
</ul><br />
</p><br />
<br />
<h4>Results</h4><br />
<br />
<p><br />
<div class="fig fig400"><br />
<img src="https://2013.igem.org/File:HokkaidoU_2013_64ROK_result_1_small.jpg"><br />
<div><span class="bold">fig.9 The result plate of 64 pattern randomizing.</span></div><br />
</div><br />
<br />
</p><br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/Future_Work"><div class="arrow-div"></div><span>Future Work</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_64ROK_result_1_small.jpgFile:HokkaidoU 2013 64ROK result 1 small.jpg2013-10-28T22:37:50Z<p>Kenta: </p>
<hr />
<div></div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_64ROK_result_1_1.JPGFile:HokkaidoU 2013 64ROK result 1 1.JPG2013-10-28T22:21:04Z<p>Kenta: </p>
<hr />
<div></div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_64ROK_result_1.JPGFile:HokkaidoU 2013 64ROK result 1.JPG2013-10-28T22:05:00Z<p>Kenta: </p>
<hr />
<div></div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-10-28T21:47:50Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1(E1010)-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1(BBa_E1010) expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration among used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn't be measured by just counting colonies on a plate. There are needed more intermolecular assay for measuring the enzyme activity.<br />
</p><br />
<br />
<h3>Comparison of assay results (Conclusion)</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<br />
<br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-10-28T21:02:16Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence, but before that let's look how RNA binds to a promoter with the help of (fig.1).</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div><span class="bold">fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</span></div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a &sigma; factor. &sigma; factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using &sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of &sigma; factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey <span class="italic">et al</span>. (2007) shows that -10 and -35 region is highly preserved (fig 3). There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div><span class="bold">fig. 2 Consensus sequence shown in review article in 1983 [3].</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div><span class="bold">fig. 3 Consensus sequence prepared in 2007 [4].</span></div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<p><br />
<li><br />
[1] R. a Mooney, I. Artsimovitch, and R. Landick, “Information processing by RNA polymerase: recognition of regulatory signals during RNA chain elongation.,” Journal of bacteriology, vol. 180, no. 13, pp. 3265–75, Jul. 1998.<br />
</li><br />
<li><br />
[2] M. S. B. Paget and J. D. Helmann, “The σ 70 family of sigma factors,” Genome Biology, vol. 4, no. 1, pp. 203.1–203.6, 2003.<br />
</li><br />
<li><br />
[3] D. K. Hawley, W. R. Mcclure, and I. R. L. P. Limited, “Compilation and analysis of Escherichia coli promoter DNA sequences,” <br />
</li><br />
Nucleic Acids Research, vol. 11, pp. 2237–2255, 1983.<br />
<li><br />
[4] M. De Mey, J. Maertens, G. J. Lequeux, W. K. Soetaert, and E. J. Vandamme, “Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering.,” BMC biotechnology, vol. 7, p. 34, Jan. 2007. <br />
</li><br />
<li><br />
[5]De Mey, M., Maertens, J., Lequeux, G. J., Soetaert, W. K., & Vandamme, E. J. (2007). Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering. BMC biotechnology, 7, 34. doi:10.1186/1472-6750-7-34<br />
</li><br />
</p><br />
<br />
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration among used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn't be measured by just counting colonies on a plate. There are needed more intermolecular assay for measuring the enzyme activity.<br />
</p><br />
<br />
<h3>Comparison of assay results (Conclusion)</h3><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<br />
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<br />
<br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration among used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn't be measured by just counting colonies on a plate. There are needed more intermolecular assay for measuring the enzyme activity.<br />
</p><br />
<br />
<h3>Comparison of assay results (Conclusion)</h3><br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<div id="prev-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Modeling"><div class="arrow-div"></div><span>Modeling</span></a><br />
</div><br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
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<hr />
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<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
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<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration among used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn't be measured by just counting colonies on a plate. There are needed more intermolecular assay for measuring the enzyme activity.<br />
</p><br />
<br />
<h3>Comparison of assay results (Conclusion)</h3><br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<br />
<br />
<div id="prev-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Modeling"><div class="arrow-div"></div><span>Modeling</span></a><br />
</div><br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
</div><br />
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<hr />
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<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
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<br />
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<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay because of mutation at CDS.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.<br />
</p><br />
<br />
<p><br />
As shown in the Shuffling_Kit/Examples page, Kanamycin resistance also differed by promoter. In the result, K1084010 has the best Kanamycin resistance activity. <br />
However, the result is containing some controversial problems, such like differences of concentration between used DNA solutions at ligation step, or the Kanamycin resistance measuring problem: the Kanamycin resistance gene activity wouldn’t be measured by just counting colonies on a plate. There are needed more certain assay for measuring the intermolecular enzyme activity.<br />
</p><br />
<br />
<h3>Comparison of assay results (Conclusion)</h3><br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-10-28T08:36:16Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of fig.1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div><span class="bold">fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</span></div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a &sigma; factor. &sigma; factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using &sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of &sigma; factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved (fig 3). There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div><span class="bold">fig. 2 Consensus sequence shown in review article in 1983 [3].</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div><span class="bold">fig. 3 Consensus sequence prepared in 2007 [4].</span></div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<p><br />
<li><br />
[1] R. a Mooney, I. Artsimovitch, and R. Landick, “Information processing by RNA polymerase: recognition of regulatory signals during RNA chain elongation.,” Journal of bacteriology, vol. 180, no. 13, pp. 3265–75, Jul. 1998.<br />
</li><br />
<li><br />
[2] M. S. B. Paget and J. D. Helmann, “The σ 70 family of sigma factors,” Genome Biology, vol. 4, no. 1, pp. 203.1–203.6, 2003.<br />
</li><br />
<li><br />
[3] D. K. Hawley, W. R. Mcclure, and I. R. L. P. Limited, “Compilation and analysis of Escherichia coli promoter DNA sequences,” <br />
</li><br />
Nucleic Acids Research, vol. 11, pp. 2237–2255, 1983.<br />
<li><br />
[4] M. De Mey, J. Maertens, G. J. Lequeux, W. K. Soetaert, and E. J. Vandamme, “Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering.,” BMC biotechnology, vol. 7, p. 34, Jan. 2007. <br />
</li><br />
</p><br />
<br />
<br />
<!--modeling begin--><br />
<br />
<h1>MODELING</h1><br />
<br />
<p>We tried to theoretically predict the strength distribution of 4096 promoters, which were artificially created by random mutation. We followed these 3 steps, referring the previous study<sup><a href="#cite-1">[1]</a><a href="#cite-2">[2]</a></sup>.</p> <br />
<ol><br />
<li>Calculate the binding energy of each promoter and &sigma;-factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter using the method of statistical mechanics</li><br />
<li>Utilizing the binding probability as the transcription efficiency</li><br />
</ol><br />
<br />
<h2>STEP 1: Calculation of Binding Energy</h2><br />
<p>First, we found the binding energy of RNAP and our promoters. As we mutated only -35 region, we only use this region for calculations. Here we define the binding energy $\varepsilon$ as the energy <span class="italic">released</span> by RNAP’s binding to promoter. Simply saying, the higher is the binding energy, the stronger is the binding. We referred the data in Kenney, <span class="italic">et al.</span><a href="#cite-3"><sup>[3]</a></sup> to calculate each binding energy.<br />
<br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon$ (at $0.05 k_BT$ intervals) and the vertical axis sample number.</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div><span class="bold">fig.1 Visualized data.</span> A portion enclosed with red square is randomized -35 region.</span></div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div><span class="bold">fig.2 Promoters distribution of binding energy.</span> The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<h2>STEP 2: Conversion from Binding Energy to Binding Probability</h2><br />
<br />
<br />
<p>Next, we estimated the binding probability. On this step, we used the method of statistical mechanics. So we assumed the following.</p><br />
<ul><br />
<li>The cell is a closed system</li><br />
<li>There are $P$ RNAPs bound somewhere on DNA</li><br />
<li>The number of bases is $N$ (bp) and $1$ of $N$ bases is +1 position of the promoter</li><br />
</ul><br />
<br />
<p>The principle of statistical mechanics is very easy; any state emerges with the same probability. So we counted up the number of state. A state stands for every information of all the particles in the system, so the number is enormous. $W$ represents this number. Here $W$ can be separated as the following.<br />
<br />
\[<br />
W=W_{\mathrm{unbound}}+W_{\mathrm{bound}}<br />
\]<br />
<br />
$W_{\mathrm{bound}}$ represents the number of state where the promoter is occupied and $W_{\mathrm{unbound}}$ unoccupied.</p><br />
<br />
<p>The purpose of this step is to find the ratio $W_{\mathrm{unbound}}:W_{\mathrm{bound}}$. Concerning the position of RNAP,<br />
<br />
\begin{align*}<br />
W_{\mathrm{unbound}}:W_{\mathrm{bound}}&=\frac{(N-1)!}{P!(N-P-1)!}\times W_{\mathrm{R}}(E):1 \times \frac{(N-1)!}{(P-1)!(N-P)!}\times W_{\mathrm{R}}(E+\varepsilon) \\ &=1:\frac{P}{N-P} \times \frac{W_{\mathrm{R}}(E+\varepsilon)}{W_{\mathrm{R}}(E)}<br />
\end{align*}<br />
<br />
<br />
where $W_{\mathrm{R}}$ represents the number of state in reservoir system (a system excluding the imformation of RNAP's position). $W_{\mathrm{R}}$ is a function of internal energy. Then, we converted $W_{\mathrm{R}}$ to entropy $S$ using the conversion formula: $S \equiv k_B \ln{W}$ ($k_B$ stands for Boltzmann constant, $\approx 1.38\times 10^{-23} \mathrm{J\cdot K^{-1}}$).<br />
<br />
\begin{align*}<br />
&=1:\frac{P}{N-P} \times \frac{\exp\left(\frac{S(E+\varepsilon)}{k_B}\right)}{\exp\left(\frac{S(E)}{k_B}\right)} \\ &=1:\frac{P}{N-P} \times \exp\left(\frac{S(E+\varepsilon)-S(E)}{k_B}\right) \\ &\approx 1:\frac{P}{N} \times \exp\left(\frac{\varepsilon \frac{\partial S}{\partial E}}{k_B}\right)<br />
\end{align*}<br />
<br />
Entropy $S$ and energy $E$ is connected as temperature $T$ as the following.<br />
<br />
\[<br />
\frac{\partial S}{\partial E} \equiv \frac{1}{T}<br />
\]<br />
<br />
So,<br />
<br />
\[<br />
W_{\mathrm{unbound}}:W_{\mathrm{bound}} \approx 1:\frac{P}{N} \times \exp\left(\frac{\varepsilon}{k_BT}\right)<br />
\]<br />
<br />
<br />
This is a final form of this calculation. Approximately the binding energy of -35 region is exponentially proportional to the binding probability.</p><br />
<br />
<h2>STEP 3: Conclusion</h2><br />
<p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div><span class="bold">fig.3 Promoter distribution of transcription efficiency.</span> The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<ol class="citation-list"><br />
<li id="cite-1">Rob Phillips, Jane Kondev and Julie Theriot. (2008). <span class="italic">Physical Biology of the Cell.</span> Garland Science.</li><br />
<li id="cite-2">Brewster, <span class="italic">et al.</span> (2012). Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. <span class="italic">PLoS computational biology.</span></li><br />
<li id="cite-3">Kenney, <span class="italic">et al.</span> (2010). Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. <span class="italic">Proceeding of the National Academy of Sciences of the United States of America.</span></li><br />
</ol><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-10-28T08:29:59Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 Randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed. Our consensus promoter is K1084001.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with fluorescence imaging machine.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: pTetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency (for theoretical explanation, see "Method page"). This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-10-28T08:14:07Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of fig.1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div><span class="bold">fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</span></div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a &sigma; factor. &sigma; factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using &sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of &sigma; factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved (fig 3). There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div><span class="bold">fig. 2 Consensus sequence shown in review article in 1983 [3].</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div><span class="bold">fig. 3 Consensus sequence prepared in 2007 [4].</span></div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<p><br />
<li><br />
[1] R. a Mooney, I. Artsimovitch, and R. Landick, “Information processing by RNA polymerase: recognition of regulatory signals during RNA chain elongation.,” Journal of bacteriology, vol. 180, no. 13, pp. 3265–75, Jul. 1998.<br />
</li><br />
<li><br />
[2] M. S. B. Paget and J. D. Helmann, “The σ 70 family of sigma factors,” Genome Biology, vol. 4, no. 1, pp. 203.1–203.6, 2003.<br />
</li><br />
<li><br />
[3] D. K. Hawley, W. R. Mcclure, and I. R. L. P. Limited, “Compilation and analysis of Escherichia coli promoter DNA sequences,” <br />
</li><br />
Nucleic Acids Research, vol. 11, pp. 2237–2255, 1983.<br />
</p><br />
<br />
<br />
<!--modeling begin--><br />
<br />
<h1>MODELING</h1><br />
<br />
<p>We tried to theoretically predict the strength distribution of 4096 promoters, which were artificially created by random mutation. We followed these 3 steps, referring the previous study<sup><a href="#cite-1">[1]</a><a href="#cite-2">[2]</a></sup>.</p> <br />
<ol><br />
<li>Calculate the binding energy of each promoter and &sigma;-factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter using the method of statistical mechanics</li><br />
<li>Utilizing the binding probability as the transcription efficiency</li><br />
</ol><br />
<br />
<h2>STEP 1: Calculation of Binding Energy</h2><br />
<p>First, we found the binding energy of RNAP and our promoters. As we mutated only -35 region, we only use this region for calculations. Here we define the binding energy $\varepsilon$ as the energy <span class="italic">released</span> by RNAP’s binding to promoter. Simply saying, the higher is the binding energy, the stronger is the binding. We referred the data in Kenney, <span class="italic">et al.</span><a href="#cite-3"><sup>[3]</a></sup> to calculate each binding energy.<br />
<br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon$ (at $0.05 k_BT$ intervals) and the vertical axis sample number.</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div><span class="bold">fig.1 Visualized data.</span> A portion enclosed with red square is randomized -35 region.</span></div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div><span class="bold">fig.2 Promoters distribution of binding energy.</span> The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<h2>STEP 2: Conversion from Binding Energy to Binding Probability</h2><br />
<br />
<br />
<p>Next, we estimated the binding probability. On this step, we used the method of statistical mechanics. So we assumed the following.</p><br />
<ul><br />
<li>The cell is a closed system</li><br />
<li>There are $P$ RNAPs bound somewhere on DNA</li><br />
<li>The number of bases is $N$ (bp) and $1$ of $N$ bases is +1 position of the promoter</li><br />
</ul><br />
<br />
<p>The principle of statistical mechanics is very easy; any state emerges with the same probability. So we counted up the number of state. A state stands for every information of all the particles in the system, so the number is enormous. $W$ represents this number. Here $W$ can be separated as the following.<br />
<br />
\[<br />
W=W_{\mathrm{unbound}}+W_{\mathrm{bound}}<br />
\]<br />
<br />
$W_{\mathrm{bound}}$ represents the number of state where the promoter is occupied and $W_{\mathrm{unbound}}$ unoccupied.</p><br />
<br />
<p>The purpose of this step is to find the ratio $W_{\mathrm{unbound}}:W_{\mathrm{bound}}$. Concerning the position of RNAP,<br />
<br />
\begin{align*}<br />
W_{\mathrm{unbound}}:W_{\mathrm{bound}}&=\frac{(N-1)!}{P!(N-P-1)!}\times W_{\mathrm{R}}(E):1 \times \frac{(N-1)!}{(P-1)!(N-P)!}\times W_{\mathrm{R}}(E+\varepsilon) \\ &=1:\frac{P}{N-P} \times \frac{W_{\mathrm{R}}(E+\varepsilon)}{W_{\mathrm{R}}(E)}<br />
\end{align*}<br />
<br />
<br />
where $W_{\mathrm{R}}$ represents the number of state in reservoir system (a system excluding the imformation of RNAP's position). $W_{\mathrm{R}}$ is a function of internal energy. Then, we converted $W_{\mathrm{R}}$ to entropy $S$ using the conversion formula: $S \equiv k_B \ln{W}$ ($k_B$ stands for Boltzmann constant, $\approx 1.38\times 10^{-23} \mathrm{J\cdot K^{-1}}$).<br />
<br />
\begin{align*}<br />
&=1:\frac{P}{N-P} \times \frac{\exp\left(\frac{S(E+\varepsilon)}{k_B}\right)}{\exp\left(\frac{S(E)}{k_B}\right)} \\ &=1:\frac{P}{N-P} \times \exp\left(\frac{S(E+\varepsilon)-S(E)}{k_B}\right) \\ &\approx 1:\frac{P}{N} \times \exp\left(\frac{\varepsilon \frac{\partial S}{\partial E}}{k_B}\right)<br />
\end{align*}<br />
<br />
Entropy $S$ and energy $E$ is connected as temperature $T$ as the following.<br />
<br />
\[<br />
\frac{\partial S}{\partial E} \equiv \frac{1}{T}<br />
\]<br />
<br />
So,<br />
<br />
\[<br />
W_{\mathrm{unbound}}:W_{\mathrm{bound}} \approx 1:\frac{P}{N} \times \exp\left(\frac{\varepsilon}{k_BT}\right)<br />
\]<br />
<br />
<br />
This is a final form of this calculation. Approximately the binding energy of -35 region is exponentially proportional to the binding probability.</p><br />
<br />
<h2>STEP 3: Conclusion</h2><br />
<p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div><span class="bold">fig.3 Promoter distribution of transcription efficiency.</span> The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<ol class="citation-list"><br />
<li id="cite-1">Rob Phillips, Jane Kondev and Julie Theriot. (2008). <span class="italic">Physical Biology of the Cell.</span> Garland Science.</li><br />
<li id="cite-2">Brewster, <span class="italic">et al.</span> (2012). Tuning promoter strength through RNA polymerase binding site design in Escherichia coli. <span class="italic">PLoS computational biology.</span></li><br />
<li id="cite-3">Kenney, <span class="italic">et al.</span> (2010). Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. <span class="italic">Proceeding of the National Academy of Sciences of the United States of America.</span></li><br />
</ol><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-10-28T08:02:31Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/6/6b/HokkaidoU_2013_Parts-f1_2.png"><br />
<div><span class="bold">fig. 1 randomized promoter sequences.</span></div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed.</p><br />
</p><br />
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<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div><span class="bold">fig. 2 mRFP1 assay result.</span></div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with FIM.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: TetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency. This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div><span class="bold">fig. 3 Theoretical transcription efficiency distribution.</span></div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div><span class="bold">fig. 4 &beta;-Galactosidase assay result.</span></div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div><span class="bold">fig. 5 Comparison of assay results and modeling data.</span></div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as Promoter Selector construct.</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_Parts-f1_2.pngFile:HokkaidoU 2013 Parts-f1 2.png2013-10-28T08:00:45Z<p>Kenta: </p>
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<div></div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/ExamplesTeam:HokkaidoU Japan/Shuffling Kit/Examples2013-10-28T07:49:55Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Optimization Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<h1>Demonstrations for Usecase Example</h1><br />
<p>We will show some interesting demonstrations of our kits, Promoter Selector and RBS Selector!</p><br />
<br />
<br />
<h2>Promoter Selector</h2><br />
<p>Let's select the best promoter for Kanamycin resistance by Promoter Selector.</p><br />
<p>For a demonstration we decided to optimize the expression of Kanamycin resistance. Changing the concentration of Kanamycin in agar plate, it is estimated that different promoter will be chosen by our Promoter Selector (fig.1).</p><br />
<br />
<p>If the concentration of Kanamycin was high, the colony with strong promoter will survive. Therefore, only one or two colors indicate the first and second biggest occupancy rate on the plate.<br />
If the concentration of Kanamycin was low, colonies with weak promoters will be able to survive. This way many colors of colonies would appear (fig.2).<br />
</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/7/79/Fig1_in_example_131027_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.1 Different promoter express each colors.</span></div><br />
</div><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2d/Fig2_in_example_HokkaidoU_2013.png"><br />
<div style="padding-bottom: 0;"><span class="bold">fig.2 Difference of Kanamycin concentration.</span></div><br />
</div><br />
<br />
<h4>Method</h4><br />
<br />
<p>Optimum concentration of Kanamycin: in LB is 50 mg/ml<br />
We prepared 3 different concentration plates. <br />
</p><br />
<br />
<ul><br />
<li>Plate A: Kanamycin 125 mg per plate</li><br />
<li>Plate B: Kanamycin 250 mg per plate</li><br />
<li>Plate C: Kanamycin 500 mg per plate (optimum concentration)</li><br />
<li>Plate D: Kanamycin 1000 mg per plate</li><br />
</ul><br />
<br />
<p>Gene<br />
Vector: pSB1C3<br />
</p><br />
<p>We cloned Kanamycin resistant gene from pSB3K3, by using BsaI adding primer. Used the Promoter Selector (K1084501, K1084502, K1084503, K1084504, K1084505 ).</p><br />
<br />
<p><br />
Culture: 37 &deg;C, for 48h<br />
</p><br />
<br />
<h4>Results</h4><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/2/28/HokkaidoU_2013_Km_resistance_assay_summary_data.png"><br />
<div><span class="bold">fig.4 Graph of number and rate, and table of number of colonies size over 1mm diameter.</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/ca/POK_DEMO_48h_newnew_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.3 Picture of plate B (Kanamycine 250 mg). The colonies showed several colors.</span></div><br />
</div><br />
<br />
<p>After 48h cultivation, around 300 colonies had appeared on each LBKC (Kanamycin and Chloramphenicol) plates. We prepared LacZa expression in Promoter Selector system as negative control to estimate the success of Golden Gate Assembly, and only 7 to 0 colonies are expressed LacZa. Mixed colored colonies which would have been transformed by two or more Promoter Selector were also observed. The number and rate of colonies per each plate were graphed (fig.4), with rejecting these undesirable colonies.<br />
</p><br />
<br />
<p><br />
In (fig.4), legend color corresponds to Promoter Selector’s part number. The sum of colony numbers is displayed above each bar, and rate is in these sections. Number in the table is the number of each Promoter Selector’s colonies. These data are collected from only one time Kanamycin resistance assay result.<br />
</p><br />
<br />
<div class="clearfix"></div><br />
<br />
<h4>Conclusion</h4><br />
<p><br />
There is no difference from lowest and highest Kanamycin concentration. In these colonies, number of colonies derived from K1084405 (containing K1084010 promoter ) has the most largest rate on each plate. This result suggests that the colonies expressed the lowest amount of Kanamycin resistance gene, and the resorce of transcription and translation could be spared to cell growth,thus the number of colonies may have been largest. Otherwise, the DNA solution of K1084505 Promoter Selector used at ligation was simply larger than other DNA solution. Although the result is collected from only one time assay, higher conscentration of Kanamycin and much number of trials than this time will be needed.<br />
</p><br />
<br />
<p><br />
From these result and the experimental fact, the existence of Km resistance gene in Promoter Selector’s BsaI cloning section is partially confirmed. Our Promoter Selector was successfully assembled, but it does not adopted to all colonies. Then, as a result of assembling, we succeeded in making colorful colonies appear on one plate.<br />
</p><br />
<h2>RBS Selector</h2><br />
<h3>4 colors</h3><br />
<p>Let’s create all combinations by two reporter genes and make various colors on one plate!</p><br />
<br />
<br />
<p><br />
The RBS Selector we made, can randomize the strength of RBSs in the operon.<br />
For a demonstration, we decided to create all combinations by two genes; mRFP1 (BBa_E1010) and LacZ&alpha; (BBa_I732006) (fig.5). LacZ&alpha; makes the colony blue. mRFP1 makes the colony red.<br />
</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/9/90/Fig4_in_example_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.5 Create all combinations by RBS of defferent stlength mRFP1 (BBa_E1010) and LacZα (BBa_I732006).</span></div><br />
</div><br />
<br />
<p>When the RBS upstream of mRFP1 was strong and the RBS upstream was weak, the colony should be red. When the RBS upstream of mRFP1 was weak, and the RBS upstream was strong, the colony should be blue. So when if the strength of RBS upstream both genes were the same, colony will be white, purple (fig.6).</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/Fig5_in_example_%2Boverhang_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.6 Each combinations of RBS make different colors.</span></div><br />
</div><br />
<br />
<h4>Method</h4><br />
<ul><br />
<li>Used promoter1 (BBa_K1084001), SD2 (BBa_K1084101), SD4 (BBa_K1084102) and assembled with.</li><br />
<li>Spread X-GAL(250 mg)on LBC plate.</li><br />
<li>Cultured for 37 &deg;C, 26h.</li><br />
</ul><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/0/08/ROK_demo_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.7 The colonies showed red, blue, white, and purple.</span></div><br />
</div><br />
<h4>Results</h4><br />
<p><br />
We got many colored colonies,red, blue, white, and purple.<br />
<br />
<br />
</p><br />
<div class="clearfix"></div><br />
<br />
<h4>Conclusion</h4><br />
<br />
<br />
<p><br />
<br />
We can say that our RBS Selector worked!!<br />
The RBSs uptsream 2 genes were randomized and they had many levels of expressions. <br />
<br />
</p><br />
<br />
<h3>64 colors</h3><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/64demo2_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.8.</span></div><br />
</div><br />
<br />
<br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Optimization/Primer_Designer"><div class="arrow-div"></div><span>Primer Designer</span></a><br />
</div><br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Optimization/Future_Work"><div class="arrow-div"></div><span>Future Work</span></a><br />
</div><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/ExamplesTeam:HokkaidoU Japan/Shuffling Kit/Examples2013-10-28T07:48:26Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Optimization Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
<div class="wrapper"><br />
<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<h1>Demonstrations for Usecase Example</h1><br />
<p>We will show some interesting demonstrations of our kits, Promoter Selector and RBS Selector!</p><br />
<br />
<br />
<h2>Promoter Selector</h2><br />
<p>Let's select the best promoter for Kanamycin resistance by Promoter Selector.</p><br />
<p>For a demonstration we decided to optimize the expression of Kanamycin resistance. Changing the concentration of Kanamycin in agar plate, it is estimated that different promoter will be chosen by our Promoter Selector (fig.1).</p><br />
<br />
<p>If the concentration of Kanamycin was high, the colony with strong promoter will survive. Therefore, only one or two colors indicate the first and second biggest occupancy rate on the plate.<br />
If the concentration of Kanamycin was low, colonies with weak promoters will be able to survive. This way many colors of colonies would appear (fig.2).<br />
</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/7/79/Fig1_in_example_131027_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.1 Different promoter express each colors.</span></div><br />
</div><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/2/2d/Fig2_in_example_HokkaidoU_2013.png"><br />
<div style="padding-bottom: 0;"><span class="bold">fig.2 Difference of Kanamycin concentration.</span></div><br />
</div><br />
<br />
<h4>Method</h4><br />
<br />
<p>Optimum concentration of Kanamycin: in LB is 50 mg/ml<br />
We prepared 3 different concentration plates. <br />
</p><br />
<br />
<ul><br />
<li>Plate A: Kanamycin 125 mg per plate</li><br />
<li>Plate B: Kanamycin 250 mg per plate</li><br />
<li>Plate C: Kanamycin 500 mg per plate (optimum concentration)</li><br />
<li>Plate D: Kanamycin 1000 mg per plate</li><br />
</ul><br />
<br />
<p>Gene<br />
Vector: pSB1C3<br />
</p><br />
<p>We cloned Kanamycin resistant gene from pSB3K3, by using BsaI adding primer. Used the Promoter Selector (K1084501, K1084502, K1084503, K1084504, K1084505 ).</p><br />
<br />
<p><br />
Culture: 37 &deg;C, for 48h<br />
</p><br />
<br />
<h4>Results</h4><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/2/28/HokkaidoU_2013_Km_resistance_assay_summary_data.png"><br />
<div><span class="bold">fig.4 Graph of number and rate, and table of number of colonies size over 1mm diameter.</span></div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/ca/POK_DEMO_48h_newnew_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.3 Picture of plate B (Kanamycine 250 mg). The colonies showed several colors.</span></div><br />
</div><br />
<br />
<p>After 48h cultivation, around 300 colonies had appeared on each LBKC (Kanamycin and Chloramphenicol) plates. We prepared LacZa expression in Promoter Selector system as negative control to estimate the success of Golden Gate Assembly, and only 7 to 0 colonies are expressed LacZa. Mixed colored colonies which would have been transformed by two or more Promoter Selector were also observed. The number and rate of colonies per each plate were graphed (fig.4), with rejecting these undesirable colonies.<br />
</p><br />
<br />
<p><br />
In (fig.4), legend color corresponds to Promoter Selector’s part number. The sum of colony numbers is displayed above each bar, and rate is in these sections. Number in the table is the number of each Promoter Selector’s colonies. These data are collected from only one time Kanamycin resistance assay result.<br />
</p><br />
<br />
<div class="clearfix"></div><br />
<br />
<h4>Conclusion</h4><br />
<p><br />
There is no difference from lowest and highest Kanamycin concentration. In these colonies, number of colonies derived from K1084405 (containing K1084010 promoter ) has the most largest rate on each plate. This result suggests that the colonies expressed the lowest amount of Kanamycin resistance gene, and the resorce of transcription and translation could be spared to cell growth,thus the number of colonies may have been largest. Otherwise, the DNA solution of K1084505 Promoter Selector used at ligation was simply larger than other DNA solution. Although the result is collected from only one time assay, higher conscentration of Kanamycin and much number of trials than this time will be needed.<br />
</p><br />
<br />
<p><br />
From these result, and the experimental fact that the existence of Km resistance gene in Promoter Selector’s BsaI cloning section is partially confirmed. Our Promoter Selector was successfully assembled, but it does not adopted to all colonies. Then, as a result of assembling, we succeeded in making colorful colonies appear on one plate.<br />
</p><br />
<h2>RBS Selector</h2><br />
<h3>4 colors</h3><br />
<p>Let’s create all combinations by two reporter genes and make various colors on one plate!</p><br />
<br />
<br />
<p><br />
The RBS Selector we made, can randomize the strength of RBSs in the operon.<br />
For a demonstration, we decided to create all combinations by two genes; mRFP1 (BBa_E1010) and LacZ&alpha; (BBa_I732006) (fig.5). LacZ&alpha; makes the colony blue. mRFP1 makes the colony red.<br />
</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/9/90/Fig4_in_example_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.5 Create all combinations by RBS of defferent stlength mRFP1 (BBa_E1010) and LacZα (BBa_I732006).</span></div><br />
</div><br />
<br />
<p>When the RBS upstream of mRFP1 was strong and the RBS upstream was weak, the colony should be red. When the RBS upstream of mRFP1 was weak, and the RBS upstream was strong, the colony should be blue. So when if the strength of RBS upstream both genes were the same, colony will be white, purple (fig.6).</p><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/Fig5_in_example_%2Boverhang_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.6 Each combinations of RBS make different colors.</span></div><br />
</div><br />
<br />
<h4>Method</h4><br />
<ul><br />
<li>Used promoter1 (BBa_K1084001), SD2 (BBa_K1084101), SD4 (BBa_K1084102) and assembled with.</li><br />
<li>Spread X-GAL(250 mg)on LBC plate.</li><br />
<li>Cultured for 37 &deg;C, 26h.</li><br />
</ul><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/0/08/ROK_demo_new_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.7 The colonies showed red, blue, white, and purple.</span></div><br />
</div><br />
<h4>Results</h4><br />
<p><br />
We got many colored colonies,red, blue, white, and purple.<br />
<br />
<br />
</p><br />
<div class="clearfix"></div><br />
<br />
<h4>Conclusion</h4><br />
<br />
<br />
<p><br />
<br />
We can say that our RBS Selector worked!!<br />
The RBSs uptsream 2 genes were randomized and they had many levels of expressions. <br />
<br />
</p><br />
<br />
<h3>64 colors</h3><br />
<br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/64demo2_HokkaidoU_2013.png"><br />
<div><span class="bold">fig.8.</span></div><br />
</div><br />
<br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Optimization/Primer_Designer"><div class="arrow-div"></div><span>Primer Designer</span></a><br />
</div><br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Optimization/Future_Work"><div class="arrow-div"></div><span>Future Work</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_Km_resistance_assay_summary_data.pngFile:HokkaidoU 2013 Km resistance assay summary data.png2013-10-28T07:28:45Z<p>Kenta: </p>
<hr />
<div></div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-09-28T03:58:13Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
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<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c1/HokkaidoU2013_promoter_Result-fig1.png"><br />
<div>Fig. 1 randomized promoter sequences.</div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div>Fig. 2 mRFP1 assay result</div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with FIM.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: TetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency. This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div>Fig. 3 Theoretical transcription efficiency distribution</div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div>Fig. 4 &beta;-Galactosidase assay result</div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div>Fig. 5 Comparison of assay results and modeling data</div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as POK construct.</p><br />
<br />
<br />
<div id="prev-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Methods"><div class="arrow-div"></div><span>Methods</span></a><br />
</div><br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-09-28T03:57:20Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
<div class="wrapper"><br />
<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c1/HokkaidoU2013_promoter_Result-fig1.png"><br />
<div>Fig. 1 randomized promoter sequences.</div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ&alpha; and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div>Fig. 2 mRFP1 assay result</div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with FIM.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: TetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency. This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div>Fig. 3 Theoretical transcription efficiency distribution</div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div>Fig. 4 &beta;-Galactosidase assay result</div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div>Fig. 5 Comparison of assay results and modeling data</div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as POK construct.</p><br />
<br />
<br />
<div id="prev-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Methods"><div class="arrow-div"></div><span>Methods</span></a><br />
</div><br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
</div><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-09-28T03:56:30Z<p>Kenta: </p>
<hr />
<div>{{Team:HokkaidoU_Japan/header_Maestro}}<br />
<html><br />
<div id="common-header-bottom-background"><br />
<div class="wrapper"><br />
<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
</div><br />
</div><br />
<br />
<div class="wrapper"><br />
<div id="hokkaidou-contents"><br />
<!-- end header / begin contents --><br />
<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c1/HokkaidoU2013_promoter_Result-fig1.png"><br />
<div>Fig. 1 randomized promoter sequences.</div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div>Fig. 2 mRFP1 assay result</div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with FIM.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: TetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency. This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div>Fig. 3 Theoretical transcription efficiency distribution</div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-Galactosidase) activity was measured with &beta;-Galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div>Fig. 4 &beta;-Galactosidase assay result</div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div>Fig. 5 Comparison of assay results and modeling data</div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as POK construct.</p><br />
<br />
<br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Methods"><div class="arrow-div"></div><span>Methods</span></a><br />
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<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Conclusion"><div class="arrow-div"></div><span>Conclusion</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Shuffling_Kit/Primer_DesignerTeam:HokkaidoU Japan/Shuffling Kit/Primer Designer2013-09-28T03:51:00Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Optimization Kit</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<h1>POK-ROK Primer Designer</h1><br />
<br />
<h3>1. First, you have to decide whether or not you conform to our default overhang set.</h3><br />
<div class="radio"><br />
<label><br />
<input type="radio" name="default" value="1">Yes, I conform to the default.</input><br />
</label><br />
</div><br />
<div class="radio"><br />
<label><br />
<input type="radio" name="default" value="2">No, I design original overhangs.</input><br />
</label><br />
</div><br />
<br />
<div id="conform"><br />
<h3>2. Secondly, you hove to determine how many CDS are included in your target plasmid.</h3><br />
I have<br />
<select class="form-control" name="cds-number"><br />
<option value="1">1</option><br />
<option value="2">2</option><br />
<option value="3">3</option><br />
</select><br />
CDS(s) included.<br />
<br />
<h3>3. Then, please select the region to where your fragment correspond in target plasmid.</h3><br />
<div id="plasmid-map"><br />
<div id="plasmid-0"> </div><br />
<div class="plasmid-gray" id="plasmid-1"> <span id="indicator1">1</span> </div><br />
<div class="plasmid-gray" id="plasmid-2"> <span id="indicator2">2</span> </div><br />
<div class="plasmid-gray" id="plasmid-3"> <span id="indicator3">3</span> </div><br />
<div class="plasmid-gray hide-1" id="plasmid-4"> <span id="indicator4">4</span> </div><br />
<div class="plasmid-gray hide-1" id="plasmid-5"> <span id="indicator5">5</span> </div><br />
<div class="plasmid-gray hide-2" id="plasmid-6"> <span id="indicator6">6</span> </div><br />
<div class="plasmid-gray hide-2" id="plasmid-7"> <span id="indicator7">7</span> </div><br />
<div class="plasmid-gray" id="plasmid-8"> <span id="indicator8">8</span> </div><br />
<div id="plasmid-9"> <span id="indicator9">9</span> </div><br />
</div><br />
<br />
<div class="my-form"><br />
<div class="form-group"><br />
<label for="part-beginning"><br />
Beginning of the fragment:<br />
</label><br />
<select name="part-beginning" class="form-control monospace"><br />
<option value="1">1: CGTC</option><br />
<option value="2">2: AAGG</option><br />
<option value="3">3: CTGA</option><br />
<option class="disable-1" value="4">4: TTAT</option><br />
<option class="disable-1" value="5">5: TTCG</option><br />
<option class="disable-2" value="6">6: TAGA</option><br />
<option class="disable-2" value="7">7: TCCC</option><br />
<option value="8">8: CGGT</option><br />
<option value="9">9: AGTA</option><br />
</select><br />
</div><br />
<div class="form-group"><br />
<label for="part-end"><br />
End of the fragment:<br />
</label><br />
<select name="part-end" class="form-control monospace"><br />
<option value="1">1: CGTC</option><br />
<option value="2">2: AAGG</option><br />
<option value="3">3: CTGA</option><br />
<option class="disable-1" value="4">4: TTAT</option><br />
<option class="disable-1" value="5">5: TTCG</option><br />
<option class="disable-2" value="6">6: TAGA</option><br />
<option class="disable-2" value="7">7: TCCC</option><br />
<option value="8">8: CGGT</option><br />
<option value="9">9: AGTA</option><br />
</select><br />
</div><br />
</div><br />
<br />
<br />
<h3>4. Okey, now we are ready to go. Enter your fragment's sequence, and press "Calculate"!</h3><br />
</div><br />
<br />
<div id="not-conform"><br />
<h3>2. Please enter overhangs and fragment's sequence, and press "Calculate"</h3><br />
</div><br />
<br />
<form id="primer-designer" class="my-form"><br />
<div class="form-group"><br />
<label for="overhang-f"><br />
Forward primer overhang:<br />
</label><br />
<input class="form-control monospace" type="text" name="overhang-f"><br><br />
</div><br />
<div class="form-group"><br />
<label for="overhang-r"><br />
Reverse primer overhang:<br />
</label><br />
<input class="form-control monospace" type="text" name="overhang-r"><br><br />
</div><br />
<div class="form-group"><br />
<label for="sequence"><br />
Fragment sequence:<br />
</label><br />
<textarea class="form-control monospace" type="text" name="sequence"></textarea><br><br />
</div><br />
<div class="form-group"><br />
<label></label><br />
<button class="form-control">Calculate</button><br />
</div><br />
</form><br />
<br />
<hr style="margin: 50px 0;"><br />
<br />
<h4>Forward</h4><br />
<dl><br />
<dt>sequence</dt><br />
<dd><pre id="primer-f" class="monospace"></pre></dd><br />
<dt>tm</dt><br />
<dd><pre id="tm-f"></pre></dd><br />
</dl><br />
<h4>Reverse</h4><br />
<dl><br />
<dt>sequence</dt><br />
<dd><pre id="primer-r" class="monospace"></pre></dd><br />
<dt>tm</dt><br />
<dd><pre id="tm-r"></pre></dd><br />
</dl><br />
<br />
<h3>5. Now, repeat previous step for remaining fragments included in the target plasmid.</h3><br />
<script type="text/javascript"><br />
(function(){var n,f,e,h,d,a,j,g,i,b,m,k,c,l;a=function(o){switch(o){case"AA":case"TT":return -9.1;case"AT":return -8.6;case"TA":return -6;case"CA":case"TG":return -5.8;case"GT":case"AC":return -6.5;case"CT":case"AG":return -7.8;case"GA":case"TC":return -5.6;case"CG":return -11.9;case"GC":return -11.1;case"GG":case"CC":return -11}};k=function(o){var p,q,s,r;q=0;for(p=s=0,r=o.length-2;0<=r?s<=r:s>=r;p=0<=r?++s:--s){q+=a(o.slice(p,+(p+1)+1||9000000000))}return q};j=function(o){switch(o){case"AA":case"TT":return -24;case"AT":return -23.9;case"TA":return -16.9;case"CA":case"TG":return -12.9;case"GT":case"AC":return -17.3;case"CT":case"AG":return -20.8;case"GA":case"TC":return -13.5;case"CG":return -27.8;case"GC":return -26.7;case"GG":case"CC":return -26.6}};c=function(o){var p,q,s,r;q=0;for(p=s=0,r=o.length-2;0<=r?s<=r:s>=r;p=0<=r?++s:--s){q+=j(o.slice(p,+(p+1)+1||9000000000))}return q};n=function(o){var q,p;q=k(o);p=c(o);return(1000*q/(-10.8+p+1.987*-15.89495209964411))-273.15+16.6*-1.3010299956639813};d=function(p){var r,o,q;if(n(p.slice(0,35))<60){alert("Sequence is too short.");return false}for(r=q=17;q<=35;r=++q){o=n(p.slice(0,+(r-1)+1||9000000000));if(o>60){if(p[r-1]==="G"||p[r-1]==="C"){break}}}return[p.slice(0,+(r-1)+1||9000000000),o]};h=function(o){switch(o){case"A":return"T";case"T":return"A";case"G":return"C";case"C":return"G"}};m=function(p){var o;return((function(){var t,r,s,q;s=p.split("");q=[];for(t=0,r=s.length;t<r;t++){o=s[t];q.push(h(o))}return q})()).reverse().join("")};g=function(s,o,w){var t,p,r,u,v,q;t=d(w);p=d(m(w));r="TTTGGTCTCT"+s+"T"+t[0];v=t[1];u="TTTGGTCTCA"+o+"A"+p[0];q=p[1];return[r,v,u,q]};e=function(o){if(/GGTCTC/.test(o)||/GGTCTC/.test(m(o))){alert("This sequence contains BsaI cutting site.");return false}else{if(/GAATTC/.test(o)||/GAATTC/.test(m(o))){alert("This sequence contains EcoRI cutting site.");return false}else{if(/CTGCAG/.test(o)||/CTGCAG/.test(m(o))){alert("This sequence contains PstI cutting site.");return false}else{if(/GCGGCCGC/.test(o)||/GCGGCCGC/.test(m(o))){alert("This sequence contains NotI cutting site.");return false}else{if(/ACTAGT/.test(o)||/ACTAGT/.test(m(o))){alert("This sequence contains SpeI cutting site.");return false}else{if(/TCTAGA/.test(o)||/TCTAGA/.test(m(o))){alert("This sequence contains XbaI cutting site.");return false}else{return true}}}}}}};f=function(p){var o;o=/[ATCG]+/.exec(p);if(o[0]===p){return true}else{return false}};b=function(p){var o;o=p.val().toUpperCase().split(/[\s\n\r]+/).join("");if(!f(o)){alert("You can NOT use non-AGCT characters.");return false}if(!e(o)){return false}};i=function(o){switch(o){case 1:return"CGTC";case 2:return"AAGG";case 3:return"CTGA";case 4:return"TTAT";case 5:return"TTCG";case 6:return"TAGA";case 7:return"TCCC";case 8:return"CGGT";case 9:return"AGTA"}};l=function(){var t,o,p,s,r,q;t=parseInt($('[name="part-beginning"]').val());o=parseInt($('[name="part-end"]').val());for(p=s=1;1<=t?s<t:s>t;p=1<=t?++s:--s){$("#plasmid-"+p).addClass("plasmid-gray")}for(p=r=t;t<=9?r<=9:r>=9;p=t<=9?++r:--r){$("#plasmid-"+p).removeClass("plasmid-gray")}for(p=q=o;o<=9?q<=9:q>=9;p=o<=9?++q:--q){$("#plasmid-"+p).addClass("plasmid-gray")}if(o>t){$('[name="overhang-f"]').val(i(t));return $('[name="overhang-r"]').val(i(o))}else{$('[name="overhang-f"]').val("");return $('[name="overhang-r"]').val("")}};$(function(){$('[name="overhang-f"]').focusout(function(){return b($(this))});$('[name="overhang-r"]').focusout(function(){return b($(this))});$('[name="sequence"]').focusout(function(){return b($(this))});$("#primer-designer").submit(function(r){var q,p,u,s,o,t;r.preventDefault();p=$(this);q=p.find("button");q.attr("disabled",true);u=p.find('[name="overhang-f"]').val().toUpperCase();s=p.find('[name="overhang-r"]').val().toUpperCase();t=p.find('[name="sequence"]').val().toUpperCase().split(/[\s\n\r]+/).join("");console.log(check_bsa_site(u));console.log(check_bsa_site(s));console.log(check_bsa_site(t));console.log(f(u));console.log(f(s));console.log(f(t));o=g(u,m(s),t);$("#primer-f").text(o[0]);$("#tm-f").html(""+(o[1].toFixed(2))+" &deg;C");$("#primer-r").text(o[2]);$("#tm-r").html(""+(o[3].toFixed(2))+" &deg;C");return q.attr("disabled",false)});$("#not-conform").hide();$('input[name="default"]:radio').change(function(){switch($(this).val()){case"1":$("#conform").show();return $("#not-conform").hide();case"2":$("#conform").hide();return $("#not-conform").show()}});$('[name="cds-number"]').val("3");$('[name="cds-number"]').change(function(){switch($(this).val()){case"1":$(".hide-1").hide();$(".hide-2").hide();$(".disable-1").attr("disabled","disabled");return $(".disable-2").attr("disabled","disabled");case"2":$(".hide-1").show();$(".hide-2").hide();$(".disable-1").removeAttr("disabled");return $(".disable-2").attr("disabled","disabled");case"3":$(".hide-1").show();$(".hide-2").show();$(".disable-1").removeAttr("disabled");return $(".disable-2").removeAttr("disabled")}});$('[name="part-beginning"]').change(function(){return l()});return $('[name="part-end"]').change(function(){return l()})})}).call(this);<br />
</script><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ConclusionTeam:HokkaidoU Japan/Promoter/Conclusion2013-09-28T03:40:03Z<p>Kenta: </p>
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<html><br />
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<h1 id="common-header-title">Maestro E.coli</h1><br />
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<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<h1>Conclusion</h1><br />
<h2>Consensus promoter strength</h2><br />
<p>This time we made "consensus promoter" by combining consensus sequence information and pLac promoter sequence. Because we thought that this would be the strongest promoter. It worked as promoter biobrick. However the strength was between pLac and pTet promoters. This was verified by mRFP expression as. Generally pLac is strong and pTet is medium promoter. We came up with 3 hypotheses why "consensus promoter" was not the strongest.</p><br />
<ul><br />
<li>The consensus sequence we designed did not have the strongest &sigma; factor affinity.</li><br />
<li>The binding interaction was too excessive.</li><br />
<li>There were some other problems we didn’t know.</li><br />
</ul><br />
<p>For second reason, it is known that the strongest binding of RNAP and promoter region inhibit promoter escape. Promoter escape is the stage when RNAP leaves from promoter region.</p><br />
<br />
<h2>Problem in promoter randomization</h2><br />
<p>We found a problem in the experiment for promoter randomizing at an early stage. Some colonies which seemed not to express reporter gene had mutations in CDS. It might have occurred when we used PCR for construction of the region from promoter to double terminator. To avoid this problem, we propose two methods for promoter randomizing. When doing PCR for randomization take a shorter sequence. Another solution is de novo synthesis.</p><br />
<br />
<h2>Family expansion</h2><br />
<p>We produced promoter family but their variations can be improved even more. Here we propose ideas to expand the variation.</p><br />
<ul><br />
<li>Mutating TG motif<br />
<div>TG motif is placed at upstream of -10 region. This motif helps bind RNAP to promoter region, and can change transcription level. Previous research showed the level decrease to 20% by mutating this motif.</div><br />
</li><br />
<li>Mutating other regions related to transcription<br />
<div>Spacer region and discriminator region are known to relate to transcription efficiency. There might be other regions which contribute promoter activity. So changing sequence in these regions could show more various strengths.</div><br />
</li><br />
</ul><br />
<p>There is one point to note to make bigger variety,. The regions relating to transcription affect transcription level in different ways difficult to control.</p><br />
<br />
<h2>Transcription strength</h2><br />
<p>Transcription efficiency is not simply correlated to binding strength. Generally the strong binding leads to more transcription but too strong binding is known to inhibit transcription level. As described above, a too strong binding inhibits promoter escape. Another example is inclusion bodies. Inclusion body sometimes appears when too many proteins are produced and inactivates them. For these reasons, some researches recommend using weak promoter.</p><br />
<br />
<h2>summary</h2><br />
<p>Above all, transcription efficiency does not equal to expression level or enzyme activity. In our experiment, same promoter shows different activity in different conditions. This means that the strongest promoter is not the best promoter for getting the maximum amount of product. And the best promoter which we should use is different for every gene or condition. So we produced a kit which selects suitable promoter automatically. We call the great kit "POK"!</p><br />
<br />
<br />
<div id="prev-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Results"><div class="arrow-div"></div><span>Results</span></a><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-09-28T03:37:13Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a &sigma; factor. &sigma; factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using &sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of &sigma; factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively [Fig 2]. Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved [Fig 3]. There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. 2 Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 3 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and &sigma; factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-09-28T03:36:59Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<!-- end header / begin contents --><br />
<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription Factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a &sigma; factor. &sigma; factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using &sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of &sigma; factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively [Fig 2]. Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved [Fig 3]. There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. 2 Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 3 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and &sigma; factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<br />
<div id="next-page"><br />
<a href="https://2013.igem.org/Team:HokkaidoU_Japan/Promoter/Methods"><div class="arrow-div"></div><span>Methods</span></a><br />
</div><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-09-28T03:34:48Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
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<!-- end header / begin contents --><br />
<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription Factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a sigma factor. Sigma factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &Sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of sigma factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively [Fig 2]. Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved [Fig 3]. There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. 2 Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 3 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and sigma factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/MethodsTeam:HokkaidoU Japan/Promoter/Methods2013-09-28T03:34:02Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<h1>Method</h1><br />
<br />
<h2>Promoter family</h2><br />
<br />
<p>As our first step for constructing original promoter family, we synthesized theoretically ideal consensus sequence to bind &sigma; factor. This should ensure that promoter will form the most stable complex with &sigma; factor. We synthesized such a consensus promoter showed in the figure above, originated from consensus sequence and lac operon promoter (pLac) [Fig. 1].</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/a/a8/HokkaidoU2013_promoter_Method-fig1.png"><br />
<div>Fig. 1</div><br />
</div><br />
<br />
<br />
<p>We constructed consensus promoter by primer annealing [Fig. 2].<br />
For mutating hexamer at -35 region, a promoter randomize primer which has random hexamer (NNNNNN) at -35 region was used, but other sequence in the primer is same with consensus promoter [Fig.3]. We designed reverse promoter, promoter isolation primer, that is to isolate randomized promoter by annealing downstream of it [Fig.4].</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/8/87/HokkaidoU2013_promoter_Method-fig2.png"><br />
<div>Fig.2</div><br />
</div><br />
<br />
<div class="fig fig800 "><br />
<img src="https://static.igem.org/mediawiki/2013/5/5f/HokkaidoU2013_promoter_Method-fig3.png"><br />
<div>Fig.3</div><br />
</div><br />
<div class="fig fig800 "><br />
<img src="https://static.igem.org/mediawiki/2013/7/78/HokkaidoU2013_promoter_Method-fig4.png"><br />
<div>Fig.4</div><br />
</div><br />
<div class="clearfix"></div><br />
<h2>Assay</h2><br />
<p><br />
To measure transcription activities, we prepared two popular reporter genes and one antibiotics resistance gene, mRFP1, lacZ&alpha;, and Kanamycin resistance gene.<br />
</p><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/Promoter/ResultsTeam:HokkaidoU Japan/Promoter/Results2013-09-28T03:32:51Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<br />
<br />
<h1>Result</h1><br />
<h2>-35 region randomization</h2><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c1/HokkaidoU2013_promoter_Result-fig1.png"><br />
<div>Fig. 1 randomized promoter sequences.</div><br />
</div><br />
<p>We randomized -35 region by PCR primers with random hexamer region. The template DNA was consensus_promtoer-B0034-mRFP1-B0015 (about 1,000 bp). We assayed the constructed sequences and isolated 10 distinct promoters. We sequenced the randomized promoter sequences to confirm that only -35 regions was changed.</p><br />
</p><br />
<br />
<div class="clearfix"></div><br />
<h2>promoter assay; mRFP1, LacZ and Kanamycin resistance gene</h2><br />
<br />
<br />
<h3>mRFP1</h3><br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/7/73/HokkaidoU2013_promoter_Result-fig2.png"><br />
<div>Fig. 2 mRFP1 assay result</div><br />
</div><br />
<p>mRFP1 expressing JM109 colonies were resuspend to 2 ml LBC liquid culture.<br />
After cultivation (180 rpm shaking at 37C) for 12 hrs, we measured OD650 with micro titer plate reader. We avoided using 600 nm because mRFP1 absorbs 600 nm. mRFP1 expression was measured with FIM.<br />
All 10 of the promoters were characterized. Five promoters were used as a reference.<br />
</p><br />
<br />
<p>Reference promoters are following<p><br />
<ul><br />
<li>BBa_R0010: pLac</li><br />
<li>BBa_R0040: TetR</li><br />
<li>BBa_J23106: constitutive promoter family member (1185 arb. unit)</li><br />
<li>BBa_J23112: constitutive promoter family member ( 1 arb. unit)</li><br />
<li>Negative control: not protein expression construct</li><br />
</ul><br />
<br />
<br />
<br />
<br />
<h3>promoter selection by modeling</h3><br />
<br />
<p>We chose 5 of 10 promoters by the value of theoretical transcription efficiency. This efficiency is affected by binding energy in our assumption.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/b/b2/HokkaidoU2013_promoter_Modeling_fig5.png"><br />
<div>Fig. 3 Theoretical transcription efficiency distribution</div><br />
</div><br />
<br />
<h3>LacZ&alpha;</h3><br />
<br />
<br />
<p><br />
We selected five promoters from our original family to model. LacZ&alpha;<br />
Only these promoters were characterized using LacZ assay.<br />
LacZ (&beta;-galactosidase) activity was measured with &beta;-galactosidase assay kit. (OZ Biogenesis<br />
http://www.funakoshi.co.jp/data/datasheet/OZB/GC-10002.pdf )<br />
DH5&alpha; strain was used.<br />
</p><br />
<br />
<br />
<p><br />
These data was compared with modeling data (logarithm of transcription efficiency, t. e.).<br />
BBa_K1084010 and BBa_K1084009 couldn't be characterized by mRFP1 assay.<br />
</p><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/1d/HokkaidoU2013_promoter_Result-fig4.png"><br />
<div>Fig. 4 &beta;-galactosidase assay result</div><br />
</div><br />
<br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/e/eb/HokkaidoU2013_promoter_Result-fig5.png"><br />
<div>Fig. 5 Comparison of assay results and modeling data</div><br />
</div><br />
<br />
<h3>Kanamycin resistance gene</h3><br />
<p>Kanamycin resistance gene is expressed by these promoters as POK construct.</p><br />
<br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-09-28T03:29:06Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
<img id="common-header-img" src="https://static.igem.org/mediawiki/2013/e/ea/HokkaidoU2013_Maestro_Header.png"><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/igem.org/7/77/HokkaidoU_2013_Promoter_fig1.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription Factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a sigma factor. Sigma factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &Sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of sigma factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (Fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved (Fig 3). There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. 2 Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 3 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and sigma factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<br />
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/3/37/HokkaidoU_2013_Background-f1-new.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription Factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a sigma factor. Sigma factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &Sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of sigma factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (Fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved (Fig 3). There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. 2 Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 3 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and sigma factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
<br />
<br />
<br />
<div id="next-page"><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/PromoterTeam:HokkaidoU Japan/Promoter2013-09-28T03:12:38Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Promoter</h2><br />
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<h1>Overview</h1><br />
<p>Proteins are expressed in mainly 2 steps. First mRNA is polymerized using DNA as a template. Then ribosome binds mRNA and translates it into protein.<br />
</p><p>Promoter is a DNA sequence initiating transcription from DNA to mRNA. If transcriptional efficiency is defined as "promoter strength", stronger promoter has ability to transcribe more mRNA. This should lead in stronger expression of proteins.<br />
</p><p>We have created several promoters by randomization of -35 sequence followed by selection. In promoters -35 region is responsible for supporting binding of RNA polymerase (RNAP). This interaction results in closed complex which is rate-limiting step. We focused on this rather transparent function to introduce variability in promoter strength.<br />
</p><br />
<br />
<h2>Overview about Transcription</h2><br />
<p>We explain the importance of promoter sequence. But before that let's look how RNA binds to a promoter with the help of figure 1.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/3/37/HokkaidoU_2013_Background-f1-new.png"><br />
<div>Fig. 1 mRNA transcription starts with promoter engagement, continues to initiation, elongation, and then it comes to termination (omitted in the figure).</div><br />
</div><br />
<br />
<p>First transcription complex must be formed. Transcription complex polymerizes mRNA in 2 steps. Initiation step starts polymerization followed by elongation step. Promoter serves crucial role on engagement and initiation. After closed complex formation DNA double helix pulled apart to form transcription bubble. During this closed complex changes into open complex. This marks the beginning of mRNA polymerization. Transcription bubble exposes deoxyribonucleotides to form new hydrogen bonds with ribonucleotides. In short DNA serves as template to make mRNA.</p><br />
<br />
<h2>Transcription Factors related to Promtoer</h2><br />
<p>RNA complex consist of 5 core enzymes and a sigma factor. Sigma factor plays crucial role in promoter recognition. It recognizes and binds to promoter region on DNA sequence and helps to assemble the core enzyme and start transcription. &Sigma; factor has several analogs, E. coli which is widely used bacteria by iGEMers is using sigma;70 for house-keeping gene expression at exponential growth. Bacterial promoter can be roughly divided into three regions; -10 region, spacer and -35 region. Bases in promoter are numbered in descending order from transcription start base which is defined as +1.</p><br />
<br />
<dl><br />
<dt>-10 region</dt><br />
<dd>The -10 region is structurally very important because it is initiates promoter melting in RNAP-promoter complex. This is essential to form open complex. Promoter consensus sequence is TATAAT at -12 to -7 position.</dd><br />
<br />
<dt>Spacer</dt><br />
<dd>Spacer is thought to increase flexibility of sigma factor binding requirements.</dd><br />
<br />
<dt>-35 region</dt><br />
<dd>-35 region is second in importance to -10. It does not energetically contribute to promoter melting. There reports on promoters without -35 region. In those case TG motif at about -16 is thought as alternative. -35 consensus sequence is TTGACA at from -36 to -31.</dd><br />
</dl><br />
<br />
<p>Promoters function to bind RNAP is a reason it is genetically well preserved. Most frequently conserved residues in the sequence make a "consensus sequence". In 1983, -35 and -10 consensus was showed to be TTGACA and TATAAT respectively (Fig 2). Horizontal axis of the figures represents the position upstream of translation ignition point. Letter at the top of the figure signifies more than over 39% occurrence of that letter at that position. Larger occurrence over 54% is represented as upper case letter. Consensus sequence published by Marjan De Mey et al. (2007) shows that -10 and -35 region is highly preserved. There other less preserved regions. The tetramer (TRTG) upstream from -10 region is called TG motif. Upstream of -35 region is UP element and downstream of -10 region is discriminator region. These sequences are thought to bind core enzymes. So these sequences are also well conserved. Each sequence is important to control promoter strength.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d6/HokkaidoU2013_Promoter_background_fig3_new_800.png"><br />
<div>Fig. Consensus sequence shown in review article in 1983 [3]</div><br />
</div><br />
<br />
<div class="fig fig400"><br />
<img src="https://static.igem.org/mediawiki/2013/e/ef/HokkaidoU2013_promoter_Background_fig4.png"><br />
<div>Fig. 4 Consensus sequence prepared in 2007 [4]</div><br />
</div><br />
<br />
<br />
<p>So we went and designed "consensus promoter". It should have strongest binding energy to RNAP. By adding mutations to -35 we sought to construct promoters with various binding energies. There are three reasons why we used -35 region.<br />
</p><p>First, -35 region is just supporting binding with &sigma; factor. It has less vital role compared to -10 region, which energetically contributes to formation of open complex. Having this in mind we changed -35 region to easily change promoter binding strength without severe errors in promoter function.<br />
</p><p>Second, RNAP and promoter binding orchestrated by &sigma; factor binding. Complex formation is thought to be rate-limited step. We thought that -35 region performs a simpler function. For this reason, mutations at -35 region can be thought as more structurally transparent.<br />
</p><p>Recently published research reported the making of promoter family by randomizing both -35 and -10 regions, changing spacer length. However it would be too much of the task for us to make some many changes. By changing hexamer sequence of -35 region there are 4096 variation. This number is a lot smaller compared to mutating every promoter position. So we can get result with a smaller library size.<br />
</p><p>With these 3 reasons we went on to construct our promoter family.<br />
</p><br />
<br />
<br />
<h2>Theoretic Prediction of Promoter Strength Distribution</h2><br />
<p>The study by Brewster et al. [5] made it possible to theoretically predict the transcription efficiency using the promoter sequence, at least to a certain extent. To predict it, we need to follow these 3 steps.</p><br />
<ol><br />
<li>Calculate the binding energy of promoter and sigma factor using the sequence</li><br />
<li>Convert the binding energy to the probability that RNAP binds promoter</li><br />
<li>Convert the binding probability to the transcription efficiency</li><br />
</ol><br />
<br />
<p>Using this theory, we tried to find the strength distribution of 4096 promoters, which were artificially created by random mutation.<br />
</p><p>As the first step, we must find the binding energy of each promoter. As we mutated only -35 region, we only use this region for calculations. The binding energy is the energy needed for two bodies to bind. This is formulated below.<br />
</p><br />
\[<br />
\varepsilon_{\mathrm{bind}} = \Delta G = G_{\mathrm{bound} } - G_{\mathrm{unbound}}<br />
\]<br />
<br />
<p>Provided that G stands for Gibbs free energy. This means that the lower is the binding energy, the higher is the binding strength. We referred the data in Kenney et al. [6] to calculate each binding energy.<br />
</p><br />
<p>The distribution of computed 4096 promoters' binding energies is shown below. The horizontal axis stands for $\varepsilon_{-35}$: the binding energy of -35 region and RNAP (at $0.05k_{B}T$ intervals) and the vertical axis sample number.</p><br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/b/bb/HokkaidoU2013_promoter_Modeling_fig1.png"><br />
<div>M-Fig. 1 Visualized data. A portion enclosed with red square is randomized -35 region.</div><br />
</div><br />
<br />
<div class="fig fig400 para"><br />
<img src="https://static.igem.org/mediawiki/2013/1/16/HokkaidoU2013_promoter_Modeling_fig2.png"><br />
<div>M-Fig. 2 The result is an approximate normal distribution.</div><br />
</div><br />
<br />
<p>Next, we found RNAP's binding probability using this binding energy. To simplify the calculation, we assumed the following.</p><br />
<ul><br />
<li>The environment is a closed system</li><br />
<li>P RNAPs bind somewhere on DNA</li><br />
<li>There are $N_{\mathrm{NS}}$ non-specific binding sites and one specific binding site (=promoter) on DNA</li><br />
<li>Define $\varepsilon_{\mathrm{NS}}$ as binding energy of RNAP and non-specific binding site</li><br />
<li>Define $\varepsilon_{\mathrm{S}}$ as binding energy of RNAP and promoter</li><br />
</ul><br />
<br />
<p>According to statistical mechanics, there is a relation between $p_i$, the probability of state $i$ and $E_i$, the energy of this state as the following.</p><br />
<br />
<br />
\[<br />
p_i \propto \exp\left(-\frac{E_i}{k_{\mathrm{B}}T}\right)<br />
\]<br />
<br />
<br />
<p>This fact gives the following calculation result.</p><br />
<br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/c/c8/HokkaidoU2013_promoter_Modeling_fig3_800.png"><br />
<div>M-Fig. 3 Quoted from [5]</div><br />
</div><br />
<br />
<p>Therefore, the binding probability is</p><br />
<br />
\begin{align*}<br />
p&=\frac{W_{\mathrm{bound}}}{W_{\mathrm{unbound}}+W_{\mathrm{bound}}} \\[6pt]<br />
&=\frac{ \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) }{1+\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) } \\[6pt]<br />
\mathrm{suppose\ that} &\frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \ll 1 \\[6pt]<br />
&\approx \frac{P}{N_{\mathrm{NS}}} \exp\left(-\frac{\varepsilon_{\mathrm{S}} - \varepsilon_{\mathrm{NS}}}{k_{\mathrm{B}}T} \right) \\[6pt]<br />
&\propto \exp\left(-\frac{\varepsilon_{-35}}{k_{\mathrm{B}}T} \right)<br />
\end{align*}<br />
<br />
<p>The binding energy of -35 region is exponentially proportional to the binding probability.<br />
</p><p>The last step is to convert the binding probability to the transcription efficiency. Let us assume these suppositions.<br />
</p><br />
<br />
<ul><br />
<li>RNAP bound to promoter promptly initiate transcription</li><br />
<li>There is no "traffic jam" of RNAPs on DNA (i. e., RNAP's transcription initiation is rate-limiting)</li><br />
</ul><br />
<br />
<p>These assumptions mean that we can directly use the value of binding probability as transcription energy in an arbitrary unit. In this way, we get following conclusive result.</p><br />
<div class="fig fig800"><br />
<img src="https://static.igem.org/mediawiki/2013/d/d3/HokkaidoU2013_promoter_Modeling_fig4.png"><br />
<div>M-Fig. 4 The horizontal axis stands for the transcription efficiency.</div><br />
</div><br />
<br />
<p>As you can see in this figure, the strengths of our promoter families vary about 1000 fold!</p><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_Background-f1-new.pngFile:HokkaidoU 2013 Background-f1-new.png2013-09-28T03:11:11Z<p>Kenta: </p>
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<div></div>Kentahttp://2013.igem.org/File:HokkaidoU_2013_Plates_nashi2.pngFile:HokkaidoU 2013 Plates nashi2.png2013-09-28T02:41:40Z<p>Kenta: </p>
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<div></div>Kentahttp://2013.igem.org/Team:HokkaidoU_Japan/OverviewTeam:HokkaidoU Japan/Overview2013-09-27T15:19:43Z<p>Kenta: </p>
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<h1 id="common-header-title">Maestro E.coli</h1><br />
<h2 id="common-header-subtitle">Overview</h2><br />
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<h1>Project Description</h1><br />
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<p><br />
Recently, more and more projects for iGEM involve precisely regulated systems or multistep catalysis. These biological systems need to have<br />
<ol><br />
<li>transparent structure</li><br />
<li>reliable function</li><br />
<li>reproducibility</li><br />
<li>and be safe</li><br />
</ol><br />
Especially parts controlling gene expression such as promoters or RBSs, it is desired that their prospective functions are explainable.<br />
</p><br />
<p>This year, we made well-selected sets of promoters and RBSs with dynamic range of strength. We mutated a -35 region of a promoter to make a family of variable promoters. These parts were strictly evaluated. </p><br />
<p>Furthermore, we make the expresion Optimization Kit which enables you to select the most suitable promoters and RBSs for your project. This can be accomplished in only one step. This Kit will help other iGEMers create more complex and effective devises.</p><br />
<p>We also did presentation at our university and two high schools about iGEM and genetics. On that occasion we get to know what the people think about gene recombination by means of questionnaires. We tried to provide them with information for deeper thought. We hope these activities will lead the society where people are more accepting genetic engineering. </p><br />
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{{Team:HokkaidoU_Japan/footer}}</div>Kenta