Team:Peking/Project/BioSensors

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            <h1 id="SensorsListTitle"><a href="https://2013.igem.org/Team:Peking/Project/BioSensors">Biosensors</a></h1>
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                <h1 id="SensorsListTitle"><a href="https://2013.igem.org/Team:Peking/Project/BioSensors">Biosensors</a></h1>
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                    <li class="SensorsListItem" style="font-size:18px; height:40px; width:180px; position:relative; left:-10px; top:25px;"><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/MulticomponentAnalysis">Multi-component Analysis</a></li>
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           <h1 id="BiosensorOverviewTitle">Biosensor</h1>
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           <h1 id="BiosensorOverviewTitle">Biosensor Introduction</h1>
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           <p id="BiosensorOverviewContent">A biosensor typically consists of a detector and a reporter. Input environmental signal activates the detector and the detector will subsequently stimulate the expression of the reporter, creating an output. The detection range of a particular biosensor is usually limited to a few specific signals, making the output of biosensors highly informative. The responding procedure of a biosensor usually involves no more than transcription and translation, both of which are automatically controlled by bacterial cell itself, so it is convenient to use compared with chemical method that rely heavily on complicated measuring devices. Additionally, as functional elements of natural biological systems, biosensors are subject to various tuning methods such as directed evolution, point mutation or fine adjustment of expression levels of the detectors and reporters composing them.
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           <p id="BiosensorOverviewContent">One advantage of biosensors is that the detection profile of a particular biosensor is usually limited to a few specific signals, thus making biosensor's output highly informative. Another advantage is the biosensor response method: it usually involves no more than transcription and translation, both of which are automatically operated by the living cells themselves; so it is more convenient to use, compared with conventional chemical methods that rely heavily on complicated measuring devices. Additionally, originating from functional elements of natural biological systems, biosensors are subject to various tuning methods such as directed evolution, point mutagenesis or other genetic manipulations.
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Bacteria living in aromatics-rich environment naturally have aromatic sensors. In Pseudomonas putida, there are XylR detecting toluene, XylS detecting benzoate, and DmpR detecting phenol. In Escherichia coli, there are HcaR detecting phenyl propionic acid, MhpR detecting 3-hydroxyl cinnamic acid and PaaX detecting phenyl acetic acid. These natural sensors function as transcriptional factors regulating expression of downstream genes that degrade aromatic compounds as alternative carbon source. we collected information concerning aromatic sensors from previous papers and focused on constructing biosensors with low basal signal, high induction ration and wide detection range to detect aromatic pollutants in environment.  
+
A biosensor typically consists of a detector and a reporter; the input signal activates the detector, through which the reporter is stimulated to emit the output signal. In our project, the biosensor input signals are aromatic compounds and the output is the expression of a reporter gene; the aromatics are supposed to be sensed by transcriptional regulators from the bacteria living in aromatics-rich environment.
 +
</br></br>
 +
Through the bioinformatic mining, we obtained <b>17</b> aromatics-sensing transcriptional regulators (see <a href="https://2013.igem.org/Team:Peking/Project/SensorMining">Sensor Mining</a>). Noting the fact that their expected aromatics-sensing profiles overlap a lot, we finally determined <b>8</b> transcriptional regulators whose profiles could be combined to cover the overall spectrum of aromatic compound species (<b>Table 1</b>).
</br></br>
</br></br>
-
Concerning the complex consistence in environmental water samples, the orthogonality of inducers should be confirmed. Then it's possible to use several sensors to test the multi-component sample.
 
-
 
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</p>
</p>
 +
 +
<table border="1" id="BiosensorsTable">
 +
<tr>
 +
  <th>Sensor</th>
 +
  <th>Expected Aromatics-sensing Profile</th>
 +
  <th>Source</th>
 +
</tr>
 +
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/XylS">XylS</a></td>
 +
  <td> BzO; 2-MeBzO; 3-MeBzO; 2,3-DMeBzO; 3,4-DMeBzO</td>
 +
  <td><I>Pseudomonas putida</I></td>
 +
</tr>
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/XylR">XylR</a></td>
 +
  <td>TOL; <i>m</i>-Xyl; 3-ClTOL</td>
 +
  <td><I>Pseudomonas putida</I></td>
 +
</tr>
 +
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HbpR">HbpR</a></td>
 +
  <td> 2-HBP; 2,2'-DHBP</td>
 +
  <td><I>Pseudomonas azelaica</I></td>
 +
</tr>
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HcaR">HcaR</a></td>
 +
  <td>PPA; 3-HPPA; 3,4-DHPPA</td>
 +
  <td><I>Escherichia coli</I></td>
 +
</tr>
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HpaR">HpaR</a></td>
 +
  <td>  3-HPAA; 4-HPAA; 3,4-DHPAA </td>
 +
  <td><I>Escherichia coli</I></td>
 +
</tr>
 +
 +
 +
<tr>
 +
  <td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HpaR#ContentHpaR4">PaaX</a></td>
 +
  <td> PAASCoA </td>
 +
  <td><I>Escherichia coli</I></td>
 +
</tr>
 +
 +
<tr>
 +
  <td ><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/DmpR">DmpR</a></td>
 +
  <td> Phl; 2-MePhl; 3-MePhl; 4-MePhl; 2-ClPhl</td>
 +
  <td><I>Pseudomonas putida</I></td>
 +
</tr>
 +
 +
<tr>
 +
<td><a href="https://2013.igem.org/Team:Peking/Project/BioSensors/NahR">NahR</a></td>
 +
<td> SaA; ASPR; 3-ClSaA; 4-ClSaA; 5-ClSaA;  </td>
 +
<td><I>Pseudomonas putida</I></td>
 +
</tr>
 +
 +
 +
</table>
 +
 +
 +
     </div>
     </div>
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     <div>
     <div>
        
        
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         <p id="BiosensorContent1">After obtaining these sensors’ coding sequence via PCR or synthesis, we constructed a biosensor circuit composed of two parts: <b>(Fig 1)</b>
+
         <p id="BiosensorContent1">Next we focused on constructing biosensors with low basal level, high induction ratio, and robust detection profiles. The coding sequences of these 8 transcriptional regulators were obtained by either chemical synthesis or PCR amplification. They were then incorporated into our biosensor circuit design (<b>Fig. 1</b>):
-
</br>
+
</br></br>
-
(1) A constitutive Pc promoter linked with sensor’s coding sequence that encodes the regulating protein;
+
-
</br>
+
-
(2) The corresponding inducible promoter located in the front of RBS-sfGFP fluorescence reporter
+
-
</br>
+
-
We tested fluorescence intensity to show induction ratio of each expression system when exposed to their inducers through ELIZA and flow cytometry. Naturally, the performances of these transcriptional factors are not well characterized and needs further tuning to be biosensors.
+
</p>
</p>
-
<p id="BiosensorContent2">Based on the data, we selectively changed Pc and RBS strength, tuning expression intensity of these transcriptional factors and sfGFP respectively, to optimize induction behavior.<b>(Fig 2)</b>
+
<p id="BiosensorContent2">It can be expected that the primary construction of a biosensor circuit might not work. For the fine-tuning, a library of constitutive promoters and a library of RBS sequences, both of different strengths,  were utilized to genetically tailor the expression of transcriptional regulators and sfGFP, respectively (<b>Fig. 2</b>).
</p>
</p>
-
<p id="BiosensorContent3">Up to now, we have constructed several well-performed types of aromatic biosensors including NahR, XylS, HbpR and DmpR. <b> (Fig 3)</b>
+
<p id="BiosensorContent3">After sparing no efforts to fine-tune the circuits, we have successfully constructed a comprehensive collection of high-performance aromatics-sening biosensors, including <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/XylS">XylS</a>, <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/XylR">XylR</a>, <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HbpR">HbpR</a>, <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/HcaR">HcaR</a>, <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/DmpR">DmpR</a>. and <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/NahR">NahR</a>. Results showed that a large variety of aromatic compounds have been taken into the detection profile of our toolkit (<b>Fig. 3</b>). See the detailed information about the performance of individual biosensors.
</p>
</p>
-
<p id="BiosensorContent4">The well-characterized aromatic biosensors consist a comprehensive aromatic detection toolkit. Various aromatic compounds are involved in our toolkit’s detection range. The performance of these biosensors propose a possibility for pathway coupling, complex sample analysis and further band pass circuit application.
+
<p id="BiosensorContent5">To allow the combination of these biosensors to analyze the aromatics profiles of practical samples, the <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/MulticomponentAnalysis">orthogonality/crosstalk</a> between inducers of different biosensors should be carefully evaluated. Within a general range of inducer concentration, no significant synergistic and antagonistic effects were observed, making our biosensors effective at profiling practical samples (<b>Fig. 4</b>).
-
<p id="BiosensorContent5">To apply the well-characterization biosensors we built in multicomponent analysis, the nonexistence of synergistic or antagonistic effects, in another word, orthogonality, among inducers should be confirmed.
+
-
</br>
+
-
We tested the orthogonality for all our fine-tuned biosensors. (please click here for further information)  The result shows that within a general inducer concentration, the orthogonality of our biosensors fits the requirements of multi-components sample detection. <b>(Fig. 4)</b>
+
</p>
</p>
-
<p id="BiosensorFigure1"><b>Fig 1. Schematic diagram of expression system</b>
+
<p id="BiosensorFigure1"><b>Figure 1. Schematic diagram for the design frame of biosensor circuits</b>
</br>
</br>
-
The transcriptional factor (TF) is ligated with Pc promoter on low-copy backbone pSB4K5. Reporter gene sfGFP is located downstream of promoter which is regulated by corresponding TF. The backbone is high-copy pUC57 simple. The dark purple arrowhead refers to Pc promoter, while the promoter regulated by corresponding TF is shown in cyan, dark green oval stands for Ribosome Binding Site (RBS), terminator is in dark red hexagon, dark blue square represents gene coding sequence.  
+
A constitutive promoter (<i>Pc</i>) constitutively expresses the transcriptional regulator protein on the low-copy backbone pSB4K5; the cognate promoter of the transcriptional regulator (<i>TF</i>) controls the expression of the reporter gene, super-fold green fluorescent protein (sfGFP, a novel and robust GFP variant designed for in vivo measurement of protein expression levels); its backbone is high-copy pUC57. Symbols used in this figure: Left orange arrow, <i>Pc</i> promoter; right orange arrow, the promoter regulated by the aromatics-sensing transcriptional regulator; green ovals, Ribosome Binding Sites (RBS); red hexagons, transcriptional terminators; dark cyan squares, gene coding sequences.  
</p>
</p>
-
<p id="BiosensorFigure2"><b>Fig 2. Tuning on Pc promoter and RBS intensity</b>
+
<p id="BiosensorFigure2"><b>Figure 2. Libraries of promoters and RBS sequences used for the fine-tuning of biosensor circuits.</b>
</br>
</br>
-
In order to obtain optimal induction performance, we constructed Pc promoter library, selecting J23106, J23105, J23114, J23117, J23109, J23113 and linked it with transcriptional factors. Besides, we constructed RBS library, adopting B0031, B0032, B0034 and put it upstream of reporter gene sfGFP. The arrowheads with blue gradient refer to different intensity of Pc promoter. The ovals with green gradient stand for distinct intensity of RBS.
+
<i>Pc</i> promoter library was exploited to fine-tune the expression level of transcriptional regulator: J23106, J23105, J23114, J23117, J23109 and J23113. The RBS library includes B0031, B0032, B0033 and B0034 to tailor the expression level of reporter gene sfGFP. Left orange arrow, <i>Pc</i> promoter; right orange arrow, the promoter regulated by the aromatics-sensing transcriptional regulator; green ovals, Ribosome Binding Sites (RBS); red hexagons, transcriptional terminators; dark cyan squares, gene coding sequences.  
</p>
</p>
-
<p id="BiosensorFigure3"><b>Fig 3 well-performed aromatic biosensors and their detective range </b>
+
<p id="BiosensorFigure3"><b>Figure 3. The aromatics spectrum showing the aromatics-sensing profiles of our individual biosensors.</b>
</br>
</br>
-
Each color in the middle ring represents the detection range of a biosensor. Structural formula with color background stands for the aromatic compounds detected by our biosensors .┝ means plug in, connecting an enzyme with existing biosensor .
+
Each color segment in the central spectrum represents the detection profile of a biosensor. Structural formula highlighted in color stand for the aromatic compounds that can be detected by our biosensors. The "plug" icon stands for <a href="https://2013.igem.org/Team:Peking/Project/Plugins">Adaptors</a>, enzymes that convert the undetectable compounds into the detectable compounds, thus to reinforce the detection capacity of some biosensors. <a href="https://static.igem.org/mediawiki/igem.org/2/24/Peking2013_Chemicals_V3%2B.pdf">Click Here</a> for the summary of the aromatics spectrum and aromatics-sensing profiles of individual biosensors.  
 +
 
</p>
</p>
-
<p id="BiosensorFigure4"><b>Fig. 4</b>
+
<p id="BiosensorFigure4"><b>Figure 4. Summary of the multi-component analysis to evaluate the synergistic/antagonistic effects between the inducers of 4 representative biosensors. </b>
-
Summary of the orthogonality between four sensors’ inducers. The inducers between XylS and NahR, XylS and HbpR, NahR and HbpR, XylS and DmpR, NahR and DmpR, and HbpR and DmpR are all highly orthogonal.
+
</br>
 +
No synergistic/antagonistic effects between the sensing-profiles of 4 biosensors (XylS, NahR, HbpR, and DmpR) were observed. For instance, although the sensing profiles of NahR and XylS overlap to some extent, the NahR-specific and XylS-specific inducers proved to be really orthogonal, which is consistent with our expectation. <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/MulticomponentAnalysis">Click Here</a> to see the detailed information about the  multi-component analysis.  
</p>
</p>
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         <img id="Biosensorfig2" src="https://static.igem.org/mediawiki/2013/6/63/Peking2013_Biosensorfig2.png"/>
         <img id="Biosensorfig2" src="https://static.igem.org/mediawiki/2013/6/63/Peking2013_Biosensorfig2.png"/>
         <img id="Biosensorfig3" src="https://static.igem.org/mediawiki/2013/a/a8/Peking2013_Biosensorfig3.png"/>
         <img id="Biosensorfig3" src="https://static.igem.org/mediawiki/2013/a/a8/Peking2013_Biosensorfig3.png"/>
-
         <img id="Biosensorfig4" src="https://static.igem.org/mediawiki/igem.org/5/56/Peking2013_Orthogonality.png"/>
+
         <a href="https://2013.igem.org/Team:Peking/Project/BioSensors/MulticomponentAnalysis"><img id="Biosensorfig4" src="https://static.igem.org/mediawiki/igem.org/5/56/Peking2013_Orthogonality.png"/></a>
 +
 
 +
 

Latest revision as of 18:12, 28 October 2013

Biosensor Introduction

One advantage of biosensors is that the detection profile of a particular biosensor is usually limited to a few specific signals, thus making biosensor's output highly informative. Another advantage is the biosensor response method: it usually involves no more than transcription and translation, both of which are automatically operated by the living cells themselves; so it is more convenient to use, compared with conventional chemical methods that rely heavily on complicated measuring devices. Additionally, originating from functional elements of natural biological systems, biosensors are subject to various tuning methods such as directed evolution, point mutagenesis or other genetic manipulations.

A biosensor typically consists of a detector and a reporter; the input signal activates the detector, through which the reporter is stimulated to emit the output signal. In our project, the biosensor input signals are aromatic compounds and the output is the expression of a reporter gene; the aromatics are supposed to be sensed by transcriptional regulators from the bacteria living in aromatics-rich environment.

Through the bioinformatic mining, we obtained 17 aromatics-sensing transcriptional regulators (see Sensor Mining). Noting the fact that their expected aromatics-sensing profiles overlap a lot, we finally determined 8 transcriptional regulators whose profiles could be combined to cover the overall spectrum of aromatic compound species (Table 1).

Sensor Expected Aromatics-sensing Profile Source
XylS BzO; 2-MeBzO; 3-MeBzO; 2,3-DMeBzO; 3,4-DMeBzO Pseudomonas putida
XylR TOL; m-Xyl; 3-ClTOL Pseudomonas putida
HbpR 2-HBP; 2,2'-DHBP Pseudomonas azelaica
HcaR PPA; 3-HPPA; 3,4-DHPPA Escherichia coli
HpaR 3-HPAA; 4-HPAA; 3,4-DHPAA Escherichia coli
PaaX PAASCoA Escherichia coli
DmpR Phl; 2-MePhl; 3-MePhl; 4-MePhl; 2-ClPhl Pseudomonas putida
NahR SaA; ASPR; 3-ClSaA; 4-ClSaA; 5-ClSaA; Pseudomonas putida

Next we focused on constructing biosensors with low basal level, high induction ratio, and robust detection profiles. The coding sequences of these 8 transcriptional regulators were obtained by either chemical synthesis or PCR amplification. They were then incorporated into our biosensor circuit design (Fig. 1):

It can be expected that the primary construction of a biosensor circuit might not work. For the fine-tuning, a library of constitutive promoters and a library of RBS sequences, both of different strengths, were utilized to genetically tailor the expression of transcriptional regulators and sfGFP, respectively (Fig. 2).

After sparing no efforts to fine-tune the circuits, we have successfully constructed a comprehensive collection of high-performance aromatics-sening biosensors, including XylS, XylR, HbpR, HcaR, DmpR. and NahR. Results showed that a large variety of aromatic compounds have been taken into the detection profile of our toolkit (Fig. 3). See the detailed information about the performance of individual biosensors.

To allow the combination of these biosensors to analyze the aromatics profiles of practical samples, the orthogonality/crosstalk between inducers of different biosensors should be carefully evaluated. Within a general range of inducer concentration, no significant synergistic and antagonistic effects were observed, making our biosensors effective at profiling practical samples (Fig. 4).

Figure 1. Schematic diagram for the design frame of biosensor circuits
A constitutive promoter (Pc) constitutively expresses the transcriptional regulator protein on the low-copy backbone pSB4K5; the cognate promoter of the transcriptional regulator (TF) controls the expression of the reporter gene, super-fold green fluorescent protein (sfGFP, a novel and robust GFP variant designed for in vivo measurement of protein expression levels); its backbone is high-copy pUC57. Symbols used in this figure: Left orange arrow, Pc promoter; right orange arrow, the promoter regulated by the aromatics-sensing transcriptional regulator; green ovals, Ribosome Binding Sites (RBS); red hexagons, transcriptional terminators; dark cyan squares, gene coding sequences.

Figure 2. Libraries of promoters and RBS sequences used for the fine-tuning of biosensor circuits.
Pc promoter library was exploited to fine-tune the expression level of transcriptional regulator: J23106, J23105, J23114, J23117, J23109 and J23113. The RBS library includes B0031, B0032, B0033 and B0034 to tailor the expression level of reporter gene sfGFP. Left orange arrow, Pc promoter; right orange arrow, the promoter regulated by the aromatics-sensing transcriptional regulator; green ovals, Ribosome Binding Sites (RBS); red hexagons, transcriptional terminators; dark cyan squares, gene coding sequences.

Figure 3. The aromatics spectrum showing the aromatics-sensing profiles of our individual biosensors.
Each color segment in the central spectrum represents the detection profile of a biosensor. Structural formula highlighted in color stand for the aromatic compounds that can be detected by our biosensors. The "plug" icon stands for Adaptors, enzymes that convert the undetectable compounds into the detectable compounds, thus to reinforce the detection capacity of some biosensors. Click Here for the summary of the aromatics spectrum and aromatics-sensing profiles of individual biosensors.

Figure 4. Summary of the multi-component analysis to evaluate the synergistic/antagonistic effects between the inducers of 4 representative biosensors.
No synergistic/antagonistic effects between the sensing-profiles of 4 biosensors (XylS, NahR, HbpR, and DmpR) were observed. For instance, although the sensing profiles of NahR and XylS overlap to some extent, the NahR-specific and XylS-specific inducers proved to be really orthogonal, which is consistent with our expectation. Click Here to see the detailed information about the multi-component analysis.