Difference between revisions of "Team:Wageningen UR/Model/IntegrationCpx"

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                         <div class="menu-head">
 
                         <div class="menu-head">
                             <h4>QS model integration</h4>
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                             <h4>Cpx system</h4>
 
                         </div>
 
                         </div>
 
                         <ul class="sidebar-nav">
 
                         <ul class="sidebar-nav">
                             <li>
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                             <li class="menu-item">
                                 <a href="#Biobrick">Existing biobrick</a>
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                                 <a href="#Initial_modeling">Initial modeling</a>
 
                             </li>
 
                             </li>
 
                             <li>
 
                             <li>
                                 <a href="#Labinitial">Lab results</a>
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                                 <a href="#Lab_test">Wet-lab testing</a>
 
                             </li>
 
                             </li>
 
                             <li>
 
                             <li>
                                 <a href="#Model">Modeling insights</a>
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                                 <a href="#dataFit">Data fitting</a>
 
                             </li>
 
                             </li>
 
                             <li>
 
                             <li>
                                 <a href="#aiiA">Lab implementation</a>
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                                 <a href="#Venus">Wet-lab implementation</a>
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                            </li>
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<li>
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                                <a href="#Conclusions">Conclusions</a>
 
                             </li>
 
                             </li>
 
                         </ul>
 
                         </ul>
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                             <li><a href="https://2017.igem.org/Team:Wageningen_UR">Home</a></li>
 
                             <li><a href="https://2017.igem.org/Team:Wageningen_UR">Home</a></li>
 
                             <li><a href="https://2017.igem.org/Team:Wageningen_UR/Model">Model</a></li>
 
                             <li><a href="https://2017.igem.org/Team:Wageningen_UR/Model">Model</a></li>
                             <li>QS model integration</li>
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                             <li>Cpx model integration</li>
 
                         </ul>
 
                         </ul>
 
                     </div>
 
                     </div>
 
                     <!--breadcrumb-wrapper -->
 
                     <!--breadcrumb-wrapper -->
  
                     <section id="QS-intro">
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                     <section id="Trans_intro">
                        <br>
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                         <div class="Title">
 
                         <div class="Title">
                             <h1>Cell signaling</h1> </div>
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                             <h1>Signal Transduction</h1> </div>
                         <p>We wanted to incorporate quorum sensing (QS) to make Mantis more robust, increase sensitivity, and amplify responses such that Mantis can sense very low antigen concentrations. To achieve this, we use a QS system that allows cells to signal the presence of antigen to the entire cell population. After detecting the antigen, a cell will start producing LuxI, the enzyme able to generate the signaling molecule AHL. AHL will diffuse out of the cell into the external medium. When it enters a neighboring cell, AHL can bind with LuxR to form an active transcription factor (TF). The TF activates production of the lysis protein COLE7, causing the cell to lyse. There are two subpopulations of cells; one containing TEV protease and the other containing a quenched form of GFP. The GFP domain in this protein is fused to a REACh domain, preventing fluorescence. Upon release, TEV protease cuts the GFP free from the REACh domain, leading to a fluorescence signal. An overview is visualized in Figure 1. More details are available on the <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Quorum_Sensing">wet-lab results</a> page.
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                        </p>
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                         <div class="Textbox Results-Desc">
                        <div class="figure-fullwidth">
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                            <!--Introduction-->
                            <div class="figure-center-imagebox">
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                                <img class="figure-center-img" src="https://static.igem.org/mediawiki/2017/6/61/T--Wageningen_UR--Results_Quorum_Sensing_Figure_1.png" />
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                            <div class="col-lg-6 banner-column" style="float: right;">
                            </div>
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                                <div class="figure-fullwidth">
                            <div class="figure-center-caption">
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                                    <div class="figure-center-imagebox.Banner-box">
                                 <b>Figure 1: </b> The components of the Mantis QS and lytic system. Antigen will activate QS (left). QS will initiate cell lysis (middle). Cell lysis will result in a fluorescent signal (right).
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                                        <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/0/0d/T--Wageningen_UR--MI.png" />
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                                    </div>
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                                 </div>
 
                             </div>
 
                             </div>
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                            <p>
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                                We use <i>E. coli</i>&#39;s native Cpx signal transduction system to internalize the signal created by antigen binding. As explained <a href="https://2017.igem.org/Team:Wageningen_UR/Results/SpecificVisualization"> here</a> this system depends on several protein-protein interactions. We tested which one of these interactions was most suitable to link to Bimolecular Fluorescence Complementation (BiFC), both in the <a href="https://2017.igem.org/Team:Wageningen_UR/Results/SpecificVisualization"> wet-lab</a> and the <a href="https://2017.igem.org/Team:Wageningen_UR/Model/Cpx_Kinetics"> dry-lab</a>. Here we show how we used and combined the output of both projects!
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                            </p>
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                            <p>
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                                We went through several iterations between the model and the lab work which are explained in more detail below. First, we modeled three possible systems to visualize antigen binding with the Cpx system, and found out which parameters we had to optimize in the lab to get the fastest and most reliable signaling. We took these recommendations to the lab and found which candidate system is most suitable for antigen visualization, and under which conditions this system works optimally.
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                            </p>
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                            <p>
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                                We used the data gathered in the lab to fit with our mathematical model, and used this to give commendations about in what other ways our signaling system can be optimized experimentally. We found several parameters that could improve the speed of our signaling, and we integrated our system with the
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                                <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Fluorescent"> "Fluorescent Protein"</a> project to test this hypothesis in the lab.
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                            </p>
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                         </div>
 
                         </div>
 
                     </section>
 
                     </section>
  
                     <section id="Biobrick">
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                     <section id="Initial_modeling">
 
                         <br>
 
                         <br>
                         <div class="Title">
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                         <h2>Phase 1: Initial system modeling</h2>
                            <h2>Quorum sensing</h2> </div>
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                         <p>
 
                         <p>
                             We selected an existing QS biobrick containing a LuxI in a positive feedback motive as the starting point for our project. This biobrick, <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a>, also contains GFP under the same promoter as LuxI. The amount of fluorescence is therefore a reporter for the degree of LuxI production. The more the cells engage in QS, the more fluorescence they produce. Previous experiments have shown that <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> produces fluorescence even at low cell densities. This suggests that leaky expression of LuxI and high basal levels of LuxR will result in a significant amount of GFP production. The goal is to construct a variation on this part that no longer has spontaneous activation, but can be induced by an outside mechanism, for example by <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Cpx">Cpx</a>.
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                             First, we aimed to find out which protein-protein interactions in the Cpx pathway were most suited to visualize Cpx activation, by connecting the interacting proteins to a BiFC reporter gene (Figure 1). Three candidate systems were analyzed, which were based on either CpxR-CpxR dimerization (Figure 1A), CpxA-CpxR phosphotransfer l(Figure 1B) or CpxA-CpxR phosphotransfer combined with specific TEV-cleavage (Figure 1C). While the constructs for the wet-lab were created, initial <i>in silico</i> tests were performed. We decided that <b>protein concentrations</b> and <b>signal activation</b> through antigens were the most important variables to test. Using our models, we simulated the effect of CpxR protein levels and antigen levels on production of a fast and intense signal. These effects could also be later tested in the wet-lab.
 
                         </p>
 
                         </p>
                    </section>
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                        <div class="figure-fullwidth">
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                            <div class="figure-center-imagebox.Banner-box">
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                                <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/e/e1/T--Wageningen_UR--CpxSystemsfade.png" />
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                                <div class="figure-center-caption">
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                                    <b>Figure 1:</b> A) eYFPn and eYFPc are fused (seperately) to CpxR. This way BiFC is used to visualize the CpxR dimerization step. B) eYFPc is fused to CpxA, and eYFPn is fused to CpxR. This way, BiFC is used to visualized the phosphorylation step of the Cpx pathway. C) TEV protease is fused to CpxR, and eYFPn and eYFPc are fused to CpxA (seperately). Upon Cpx pathway activation, the eYFP-termini are cleaved off of CpxA. Leucine zippers are fused to the eYFP-termini to enable the reassembly.
  
                    <section id="Labinitial">
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                                </div>
                        <br>
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                            </div>
                        <div class="Title">
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                        </div>
                            <h2>Initial testing</h2> </div>
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                        <p>
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<p>
                             To confirm the properties of <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> reported in the repository, we first <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Quorum_Sensing#QSM">analyzed the fluorescence</a> produced by cells containing this biobrick. As a negative control, cells containing pLuxR-GFP, as well as cells containing pLuxR-GFP and pLuxL-LuxR (i.e. <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> without LuxI) were also analyzed (Figure 2). The <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> biobrick functions properly only as a reporter for QS. As it always engages in QS, it cannot be used in systems that require QS in specific cases. Our goal is to fix the behavior of <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> so that the QS mechanism can be applied in a useful manner.
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                             We quickly found out that, in theory, a system based on CpxR-CpxR dimerization (Figure 1A) was the most promising to construct in the lab as it shows strong and specific fluorescent signals <i>in silico</i>. This is likely due to one activated CpxA molecule amplifying its signal by phosphorylating several response regulator CpxR proteins. <mark>Some mathematical explanation?</mark>
 
                         </p>
 
                         </p>
 
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                        <div class="figure-center-fullwidth">
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<div class="figure-center-imagebox.Banner-box">
                             <div class="figure-center-imagebox">
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                             <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/b/b0/T--Wageningen_UR--InitModelCpxRdimerization.png" />
                                <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/8/85/T--Wageningen_UR--Model-ReporterQS.png" />
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                            </div>
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                             <div class="figure-center-caption">
 
                             <div class="figure-center-caption">
                                 <b>Figure 2: </b>Relative fluorescence produced by cells containing <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> (Reporter Quorum Sensing), pLuxR-GFP, and pLuxR-GFP & pLuxL-LuxR (Negative control 2).
+
                                 <b>Figure 2:</b> Relative fluorescent signal intensity (orange) and signaling speed (green) are plotted against the initial concentration of CpxR. Data is modeled for the CpxR-CpxR dimerization system (Figure 1A).
  
 
                             </div>
 
                             </div>
 
                         </div>
 
                         </div>
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 +
  
 
                         <p>
 
                         <p>
                             This indicates that cells containing <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> auto-activate and produce the active transcription factor [LuxR-AHL} dimer. Without LuxI, there is no AHL production and there is no active form of the LuxR transcription factor. Our goal now is to make an inducible version of <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a> that initially produces no GFP, but that can be activated.
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                             Results of all three systems can be seen at the <a href="https://2017.igem.org/Team:Wageningen_UR/Model/Cpx_Kinetics">Cpx Kinetics page</a>. Although the signaling kinetics of the CpxA-CpxR systems (Figure 1B,C) are dependent on the right CpxA and antigen levels in the sample, <mark>it seems that the CpxR-CpxR setup is not dependent on any protein concentration</mark>. It even shows that the maximum reached YFP concentration is limited by CpxR, which can be increased in the lab. The strongest signal will be obtained when CpxR expression and Cpx activation are maximized (Figure 2).
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                        </p>
 +
                        <p>
 +
                            We went into the lab to test these hypotheses!
 
                         </p>
 
                         </p>
  
 +
                       
 
                     </section>
 
                     </section>
  
                     <section id="Model">
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                     <section id="Lab_test">
 
                         <br>
 
                         <br>
                         <div class="Title">
+
                         <h2>Phase 2: Testing model hypotheses in the lab</h2>
                            <h2>Modeling insights</h2> </div>
+
                         <p>In parallel to the wet-lab experiments, we developed a <a href="https://2017.igem.org/Team:Wageningen_UR/Model/QS">mathematical model</a>of our quorum sensing system. In the model, certain parameters resulted in behavior identical to our wet-lab results (Video 1). The leaky expression of LuxI resulted in fluorescence despite the fact that LuxI production was never induced.
+
 +
                         <p>
 +
                            To test these hypotheses, we created constructs in which we coupled split <a href="http://parts.igem.org/Part:BBa_E0030"> eYFP</a> halves to <a href="http://parts.igem.org/Part:BBa_K2387002">CpxA</a> and <a href="http://parts.igem.org/Part:BBa_K1486000"> CpxR</a> respectively and placed them under control of the <a href="http://parts.igem.org/Part:BBa_I0500"> inducible araC/pBAD promoter</a>. We transformed <i>E. coli</i> K12 with these constructs. The Cpx system was activated with the known activator KCl in different concentrations to mimic different antigen concentrations at a time-point of 20 minutes. An extensive overview of the performed experiments can be found <a href="https://2017.igem.org/Team:Wageningen_UR/Results/SpecificVisualization#initialresults"> here</a>.
 
                         </p>
 
                         </p>
 
+
                        <p>
                         <video controls>
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                            We quickly found out that the systems based on CpxA-CpxR interaction did not generate a clear fluorescent signal, which matches the prediction of the model! Furthermore, we show that visualizing CpxR dimerization with BiFC is indeed a viable option. Because we put the CpxR-eYFP-termini construct under control of the inducible araC/pBAD promoter we were able to test hypothesis 1: <b>The strongest signal will be obtained when CpxR expression is maximized</b>. We found out that this is indeed true (Figure 3A,B and C). To find out how we obtained this results, please visit this
                             <source src="https://static.igem.org/mediawiki/2017/7/76/T--Wageningen_UR--Model-aiiAfluorescencecomp.mp4" type="video/mp4"> Your browser does not support the video tag.
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                            <a href="https://2017.igem.org/Team:Wageningen_UR/Results/SpecificVisualization"> page</a>.
                        </video>
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                         </p>
                        <div class="figure-center-caption">
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<div class="figure-center">
                            <b>Video 1: </b> Cells spontaneously active their QS system resulting in AHL production (left). This triggers the cell lysis, generating fluoresence (right).
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                             <img class="figure-center-img" src="https://static.igem.org/mediawiki/2017/e/e4/T--Wageningen_UR--CpxR_dimerization_araC.png" />
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                            <div class="figure-center-caption">
 +
                                <b>Figure 3A:</b> CpxR dimerization visualized with different L-arabinose concentrations over time.
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                            </div>
 
                         </div>
 
                         </div>
 +
<div class="figure-center">
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                            <img class="figure-center-img" src="https://static.igem.org/mediawiki/2017/0/0a/T--Wageningen_UR--AReYFP-araC-.png" />
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                              <div class="figure-center-caption">
 +
                                                <b>Figure 3B:</b> CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time.
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                                            </div>
 +
                                        </div>
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<div class="figure-center">
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                                            <img class="figure-center-img" src="https://static.igem.org/mediawiki/2017/b/b3/T--Wageningen_UR--CpxAR_interactionTEV_araC.png" />
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                                            <div class="figure-center-caption">
 +
                                                <b>Figure 3C:</b>
 +
                                                CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time. Upon Cpx activation, eYFP-termini are cleaved off of CpxA by TEV protease and reassemble in the cytoplams using leucine zipper&#39;s natural affinity for each other.
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<mark>FIX ALL GRAPH COMMENTS > DATA POINTS, CAPTIONS, ALIGNMENT</mark>
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                                            </div>
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                                        </div>
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                        <p>
 +
                            We then set to test hypothesis 2: <b> The strongest signal will be obtained when Cpx activation is maximized</b>. In Figure 4 we show that this is true. We mimic antigen binding by adding a known activator of the Cpx pathway, and by increasing its concentration the fluorescence intensity also rises (Figure 4).
  
 +
                        </p>
 
                         <p>
 
                         <p>
                             The parameter space showed the important role of <a href="https://2017.igem.org/Team:Wageningen_UR/Model/QS#Local">certain parameters</a> in preventing spontaneous auto-activation.</p>
+
                             We took this data back to the dry-lab to further improve our computer model!
                        <p>From model simulations, we found that a low rate of cellular AHL degradation is associated with spontaneous auto-activation. A potential method of controlling this parameter in the wet-lab is to use the enzyme aiiA, capatable of degrading AHL. The role of using aiiA to combat leaky expression has been explored by previous iGEM teams; <a href="https://2015.igem.org/Team:ETH_Zurich/Modeling/Experiments_Model#Characterization_of_the_effect_of_AiiA">ETH.</a>
+
                        </p>
+
                        <p>We first explored the effect of adding aiiA to our <a href="https://2017.igem.org/wiki/index.php?title=Team:Wageningen_UR/Model/QS#Spatial">spatiotemoral model</a>. Reaction rates for aiiA were added to the model and parameter sets that resulted in spontaneous lysis were simulated (Video 2). This demonstrates that aiiA can prevent fluorescence from being generated in the absence of GFP while still maintaining the generation of fluorescence in the presence of antigen.
+
 
                         </p>
 
                         </p>
  
                         <video controls>
+
                         <div class="figure-center">
                             <source src="https://static.igem.org/mediawiki/2017/7/76/T--Wageningen_UR--Model-aiiAfluorescencecomp.mp4" type="video/mp4"> Your browser does not support the video tag.
+
                             <img class="figure-center-img" src="https://static.igem.org/mediawiki/2017/e/e9/T--Wageningen_UR--CpxR_dimerization_KCl.png" />
                        </video>
+
<div class="figure-center-caption">
                        <div class="figure-center-caption">
+
                             <b>Figure 4:</b> CpxR dimerization visualized with L-arabinose concentration of 0.2% and different activator concentrations over time.
                             <b>Video 2: </b>Fluorescence produced by the original system (top) versus the system with aiiA included (bottom). On the right side is shown the system induced with antigen. On the left, the system in the abscence of antigen.
+
 
 +
                            </div>
 
                         </div>
 
                         </div>
 +
                    </section>
  
                         <p>We were able to tune the kinetic parameters of aiiA so that aiiA would prevent spontaneous auto-activation caused by leaky expression while not preventing the detection of low levels of AHL produced by neighboring cells.
+
                    <!--Text  for final result-->
 +
                    <section id="dataFit">
 +
                         <br>
 +
                        <h2>Phase 3: Fitting the computer model to the experimental data</h2>
 +
                        <!--Text for Approach-->
 +
 
 +
                        <p>
 +
                            To learn about the characteristics and potential of the system built in the lab, the experimental data was compared to the model. A parameter set was found which gave a similar YFP production to the experimental data (Figure 5). By doing this, we found that the best fitting parameter set showed a relatively high fluorescence intensity, but it was fairly slow compared to other parameter sets. This means that there is still room for improvement!
 
                         </p>
 
                         </p>
                        <table class="table table-bordered Results-Table">
 
                            <thead>
 
                                <tr>
 
                                    <th colspan="4">Table 3: A system with negatively regulation of LuxR that self-activated spontaneously was modified by adding aiiA or increasing the degradation rate of LuxR, resulting in the desired behavior.</th>
 
                                </tr>
 
                            </thead>
 
  
                            <tbody>
+
                        <div class="figure-center">
                                <tr>
+
                            <img class="figure-center" src="https://static.igem.org/mediawiki/2017/3/32/T--Wageningen_UR--datafit.png" />
                                    <th></th>
+
                                    <th>Score</th>
+
                            <div class="figure-center-caption">
                                    <th>Animation, no antigen</th>
+
                                 <b>Figure 5:</b> Comparison of YFP concentration over time using data measured in the wet-lab and a model simulation using the best fitting parameter set.<mark>ACCOUNT FOR COMMENTS EMMA + explain the lines</mark>
                                    <th>Animation, antigen</th>
+
</div>
                                </tr>
+
                            </div>
                                <tr>
+
                                    <td>Original set</td>
+
                                    <td>99.97</td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/c/c0/T--Wageningen_UR--NegRegSpontNegShort.mp4" target="_blank">link</a></td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/7/72/T--Wageningen_UR--NegRegSpontPos.mp4" target="_blank">link</a></td>
+
                                </tr>
+
                                <tr>
+
                                    <td>Original Set & aiiA</td>
+
                                    <td>0.059</td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/0/04/T--Wageningen_UR--NegRegaiiANegShort.mp4" target="_blank">link</a></td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/f/f5/T--Wageningen_UR--NegRegaiiAPos.mp4" target="_blank">link</a></td>
+
                                 </tr>
+
                                <tr>
+
                                    <td>Original Set & increased LuxR deg.</td>
+
                                    <td>0.084</td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/4/46/T--Wageningen_UR--NegRegLuxRNegShort.mp4" target="_blank">link</a></td>
+
                                    <td><a href="https://static.igem.org/mediawiki/2017/b/b6/T--Wageningen_UR--NegRegLuxRPos.mp4" target="_blank">link</a></td>
+
  
                                 </tr>
+
                            <p>
                             </tbody>
+
                                 Of course, to improve the current signaling system we want to know which parameters should be changed in order to increase the YFP signal and the response speed. To assess this, each parameter of the best fitting set was individually varied between 0.001 and 1000, keeping the other parameters constant. The effect of this change on the maximum achievable YFP concentration and production speed was calculated (Figure 6).
 +
                            </p>
 +
<div class="figure-center">
 +
                             <img class="figure-center" src="https://static.igem.org/mediawiki/2017/3/32/T--Wageningen_UR--datafit.png" />
 +
 +
                            <div class="figure-center-caption">
 +
                                <b>Figure 6:</b>
 +
                                <mark>*Placeholder HEATMAP*</mark> A) Effect of parameter variation on the maximum fluorescent concentration. B) Effect of parameter variation on the speed at which the fluorescent signal is produced.
  
                        </table>
 
  
                        <p>These modeling results support the strategy of using aiiA to tune the sensitivity of the QS construct and indicate that, in principle, the functionality we want to engineer can be obtained using only the components we are using.</p>
+
                            </div>
 +
</div>
 +
 +
                            <p>
 +
                                This figure shows that the maximum YFP fluorescence has more possibility to be improved than the speed at which YFP increases. Both system properties could be profoundly improved by increasing the kinetic rates for antigen binding by the affibody and CpxR-eYFPc phosphorylation (find all the used parameters <a href="https://2017.igem.org/Team:Wageningen_UR/Model/Cpx_Kinetics">here</a>). However, given the iGEM time limits, it was not feasible to confirm this hypothesis in the lab. One viable option, though, to improve system performance was to use a fluorophore with a faster maturation time <mark>(k6) DESCRIPTION OF PARAMETERS</mark>.
 +
                            </p>
 +
                        </div>
 
                     </section>
 
                     </section>
  
                     <section id="aiiA">
+
                     <section id="Venus">
                         <div class="Title">
+
                         <br>
                            <h2>Lab implementation</h2> </div>
+
                        <h2>Phase 4: Implementing faster maturation</h2>
 
                         <p>
 
                         <p>
                             Inspired by the modeling result, we decided to add aiiA to <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a>. The aiiA gene had to be controlled under an inducible and tight promoter. We used the pTet promoter to regulate aiiA, causing aiiA to be produced constitutively. To turn on fluorescence, aiiA production can be halted by introducing the TetR protein, which represses production from the pTet promoter. This construct was submitted as a new biobrick <a href="http://parts.igem.org/Part:BBa_K2387070">BBa_K2387070</a>. Production of TetR is regulated by the inducible promoter pBAD. Addition of arabinose should lead to production of TetR. TetR can then bind and repress pTet, halting aiiA production. Lower levels of aiiA in the cells would mean higher levels of AHL, which should then lead to auto-activation and production of GFP. In this way, addition of arabinose should induce a fluorescent signal.
+
                             We aim to improve the response time of our visualization system. As stated, our model shows that this can be done by using a faster maturing fluorescent protein. During our <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Fluorescent#Comparing"> "Fluorescent Protein"</a> project we tested a number of fluorescent proteins, of which <a href="http://parts.igem.org/Part:BBa_K2387037">mVenus</a> showed one of the highest levels of brightness and a high percentage of fluorescence reconstitution. Furthermore mVenus is designed to have a fast and efficient maturation time [1], exactly what we need! We show <a href="https://2017.igem.org/Team:Wageningen_UR/SpecificVisualization"> here </a> how this experiment was designed.
                        </p>
+
                        <p>
+
                            The fluorescence of this new construct was measured alongside the old construct and the negative controls (Figure 3).
+
 
                         </p>
 
                         </p>
  
                         <div class="figure-center-fullwidth">
+
                         <div class="figure-fullwidth">
                             <div class="figure-center-imagebox">
+
                             <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/b/b0/T--Wageningen_UR--CpxR_dimerization_eYFP_Venus.png" />
                                <img class="figure-center-img bnl_banner" src="https://static.igem.org/mediawiki/2017/0/0e/T--Wageningen_UR--Model-InducibleQS.png" />
+
                            </div>
+
 
                             <div class="figure-center-caption">
 
                             <div class="figure-center-caption">
                                 <b>Figure 3: </b>The fluorescence produced by cells containing the Reporter QS (<a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a>), Inducible QS (Reporter QS with repressible aiiA <a href="http://parts.igem.org/Part:BBa_K2387070">BBa_K2387070</a>. Arabinose was added to the 'Induced' cells
+
                                 <b>Figure 7:</b>
                                Negative control 1 encodes only for GFP, negative control 2 for both GFP and LuxR.
+
                                CpxR dimerization visualized using eYFP (left) and mVenus (right), with L-arabinose concentration = 0.2% and different activator concentrations over time
 +
 
 
                             </div>
 
                             </div>
                        </div>
 
 
                    </section>
 
  
                    <section id="QS-conclusion">
+
                             <p>
                        <div class="Title">
+
                                The results show that usage of mVenus over eYFP as a reporter protein increases the strength of the produced fluorescent signal more then five times. Unfortunately, the background signal also increases a lot, which means we lose a lot of sensitivity. This means that mVenus is not a suitable candidate to visualize antigen binding.  
                             <h2>Conclusion</h2> </div>
+
<p>
+
                            For Mantis to amplify responses to antigen, we need a system that can amplify fluorescence, but only in response to detection of antigen. We determined that QS could be a good strategy to achieve this. Our goal was to engineer a construct where QS could be induced, for example by a two-component system or by addition of arabinose. We started off with a QS system that always produces fluorescence. The goal was to reduce the sensitivity to AHL. Through the mathematical model of the QS module of Mantis we discovered that aiiA can be used to tune the sensitivity of the QS system. Inspired by our model, a biobrick <a href="http://parts.igem.org/Part:BBa_K2387070">BBa_K2387070</a> that allows for inducible repression of aiiA production was engineered. By combining our biobrick with an existing QS reporter biobrick <a href="http://parts.igem.org/Part:BBa_K1913005">BBa_K1913005</a>, we managed to increase fluorescence production after addition of arabinose. Our module was therefore able to detect arabinose and turn on QS. Thus, GFP was produced when required.
+
 
                             </p>
 
                             </p>
 
                             <p>
 
                             <p>
                                Our biobrick allows the engineering of whole-cell biosensors that incorporate QS as a means of signal amplification or to synchronize the reporter system across an entire population. For this biobrick to work with a detection module, that module has to trigger TetR production upon detection. In doing so, QS will be triggered, resulting in a fluorescent signal.
+
                                At this moment, we recommend using <a href="http://parts.igem.org/Part:BBa_K2387047">sfGFP</a> as a reporter for antigen binding. We found out sfGFP is thermostable and has efficient maturation at high temperatures, while still being one of the fastest and brightest reporters we tested.  
 +
 
 
                             </p>
 
                             </p>
 +
                        </div>
 +
 +
                    </section>
 +
 +
<section id="conclusions">
 +
                    <section class="Conclusions">
 +
                        <div class="Title">
 +
                            <h2>Conclusions </h2> </div>
 +
 +
 +
 +
<p>
 +
During this project we modeled and experimentally tested several methods to find the optimal visualization method to use in Mantis. Three strategies were tested to find the optimal visualization method of antigen binding using the Cpx pathway. In the end, it was clear that fusion of eYFP-termini to CpxR and measuring its dimerization shows clear fluorescence within two hours after activation, even in low concentrations! This shows that we found a potentially usable method of rapidly and specifically measuring antigens in blood.
 +
                        </p>
 +
                        <p>
 +
                            During this project, we constantly implemented results from the computer modeling into the experimental design, and vice versa. Doing so, we were able to predict correct induction and activation levels. This yielded experimental results which could be implemented in the model and gave us indications on how to perfect Mantis.
 +
                        </p>
 +
                        <p>
 +
                            Furthermore, we had time to integrate this combined modeling and experimental project with another wet lab projects, where we analyze several <a href="https://2017.igem.org/Team:Wageningen_UR/Results/Fluorescent">split reporter proteins</a> to potentially use in Mantis.
 +
                        </p>
 
                     </section>
 
                     </section>
  
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                     <section class="references">
 
                         <div class="Textbox Citations">
 
                         <div class="Textbox Citations">
                             <h2>
+
                             <h3>
 
References
 
References
</h2>
+
</h3>
 +
 
 +
                            <ol>
 +
                                <li>Nagai, T., Ibata, K., Park, E. S., Kubota, M., & Mikoshiba, K. (2001). A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications. <i>Nature Biotechnology</i>, 20, 1585–1588.</li>
 +
 +
                            </ol>
  
                           
 
 
                         </div>
 
                         </div>
 +
 
                     </section>
 
                     </section>
 +
</section>
  
 
                 </div>
 
                 </div>

Revision as of 19:30, 31 October 2017

Signal Transduction

We use E. coli's native Cpx signal transduction system to internalize the signal created by antigen binding. As explained here this system depends on several protein-protein interactions. We tested which one of these interactions was most suitable to link to Bimolecular Fluorescence Complementation (BiFC), both in the wet-lab and the dry-lab. Here we show how we used and combined the output of both projects!

We went through several iterations between the model and the lab work which are explained in more detail below. First, we modeled three possible systems to visualize antigen binding with the Cpx system, and found out which parameters we had to optimize in the lab to get the fastest and most reliable signaling. We took these recommendations to the lab and found which candidate system is most suitable for antigen visualization, and under which conditions this system works optimally.

We used the data gathered in the lab to fit with our mathematical model, and used this to give commendations about in what other ways our signaling system can be optimized experimentally. We found several parameters that could improve the speed of our signaling, and we integrated our system with the "Fluorescent Protein" project to test this hypothesis in the lab.


Phase 1: Initial system modeling

First, we aimed to find out which protein-protein interactions in the Cpx pathway were most suited to visualize Cpx activation, by connecting the interacting proteins to a BiFC reporter gene (Figure 1). Three candidate systems were analyzed, which were based on either CpxR-CpxR dimerization (Figure 1A), CpxA-CpxR phosphotransfer l(Figure 1B) or CpxA-CpxR phosphotransfer combined with specific TEV-cleavage (Figure 1C). While the constructs for the wet-lab were created, initial in silico tests were performed. We decided that protein concentrations and signal activation through antigens were the most important variables to test. Using our models, we simulated the effect of CpxR protein levels and antigen levels on production of a fast and intense signal. These effects could also be later tested in the wet-lab.

Figure 1: A) eYFPn and eYFPc are fused (seperately) to CpxR. This way BiFC is used to visualize the CpxR dimerization step. B) eYFPc is fused to CpxA, and eYFPn is fused to CpxR. This way, BiFC is used to visualized the phosphorylation step of the Cpx pathway. C) TEV protease is fused to CpxR, and eYFPn and eYFPc are fused to CpxA (seperately). Upon Cpx pathway activation, the eYFP-termini are cleaved off of CpxA. Leucine zippers are fused to the eYFP-termini to enable the reassembly.

We quickly found out that, in theory, a system based on CpxR-CpxR dimerization (Figure 1A) was the most promising to construct in the lab as it shows strong and specific fluorescent signals in silico. This is likely due to one activated CpxA molecule amplifying its signal by phosphorylating several response regulator CpxR proteins. Some mathematical explanation?

Figure 2: Relative fluorescent signal intensity (orange) and signaling speed (green) are plotted against the initial concentration of CpxR. Data is modeled for the CpxR-CpxR dimerization system (Figure 1A).

Results of all three systems can be seen at the Cpx Kinetics page. Although the signaling kinetics of the CpxA-CpxR systems (Figure 1B,C) are dependent on the right CpxA and antigen levels in the sample, it seems that the CpxR-CpxR setup is not dependent on any protein concentration. It even shows that the maximum reached YFP concentration is limited by CpxR, which can be increased in the lab. The strongest signal will be obtained when CpxR expression and Cpx activation are maximized (Figure 2).

We went into the lab to test these hypotheses!


Phase 2: Testing model hypotheses in the lab

To test these hypotheses, we created constructs in which we coupled split eYFP halves to CpxA and CpxR respectively and placed them under control of the inducible araC/pBAD promoter. We transformed E. coli K12 with these constructs. The Cpx system was activated with the known activator KCl in different concentrations to mimic different antigen concentrations at a time-point of 20 minutes. An extensive overview of the performed experiments can be found here.

We quickly found out that the systems based on CpxA-CpxR interaction did not generate a clear fluorescent signal, which matches the prediction of the model! Furthermore, we show that visualizing CpxR dimerization with BiFC is indeed a viable option. Because we put the CpxR-eYFP-termini construct under control of the inducible araC/pBAD promoter we were able to test hypothesis 1: The strongest signal will be obtained when CpxR expression is maximized. We found out that this is indeed true (Figure 3A,B and C). To find out how we obtained this results, please visit this page.

Figure 3A: CpxR dimerization visualized with different L-arabinose concentrations over time.
Figure 3B: CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time.
Figure 3C: CpxA-CpxR protein interaction visualized with different L-arabinose concentrations over time. Upon Cpx activation, eYFP-termini are cleaved off of CpxA by TEV protease and reassemble in the cytoplams using leucine zipper's natural affinity for each other. FIX ALL GRAPH COMMENTS > DATA POINTS, CAPTIONS, ALIGNMENT

We then set to test hypothesis 2: The strongest signal will be obtained when Cpx activation is maximized. In Figure 4 we show that this is true. We mimic antigen binding by adding a known activator of the Cpx pathway, and by increasing its concentration the fluorescence intensity also rises (Figure 4).

We took this data back to the dry-lab to further improve our computer model!

Figure 4: CpxR dimerization visualized with L-arabinose concentration of 0.2% and different activator concentrations over time.

Phase 3: Fitting the computer model to the experimental data

To learn about the characteristics and potential of the system built in the lab, the experimental data was compared to the model. A parameter set was found which gave a similar YFP production to the experimental data (Figure 5). By doing this, we found that the best fitting parameter set showed a relatively high fluorescence intensity, but it was fairly slow compared to other parameter sets. This means that there is still room for improvement!

Figure 5: Comparison of YFP concentration over time using data measured in the wet-lab and a model simulation using the best fitting parameter set.ACCOUNT FOR COMMENTS EMMA + explain the lines

Of course, to improve the current signaling system we want to know which parameters should be changed in order to increase the YFP signal and the response speed. To assess this, each parameter of the best fitting set was individually varied between 0.001 and 1000, keeping the other parameters constant. The effect of this change on the maximum achievable YFP concentration and production speed was calculated (Figure 6).

Figure 6: *Placeholder HEATMAP* A) Effect of parameter variation on the maximum fluorescent concentration. B) Effect of parameter variation on the speed at which the fluorescent signal is produced.

This figure shows that the maximum YFP fluorescence has more possibility to be improved than the speed at which YFP increases. Both system properties could be profoundly improved by increasing the kinetic rates for antigen binding by the affibody and CpxR-eYFPc phosphorylation (find all the used parameters here). However, given the iGEM time limits, it was not feasible to confirm this hypothesis in the lab. One viable option, though, to improve system performance was to use a fluorophore with a faster maturation time (k6) DESCRIPTION OF PARAMETERS.


Phase 4: Implementing faster maturation

We aim to improve the response time of our visualization system. As stated, our model shows that this can be done by using a faster maturing fluorescent protein. During our "Fluorescent Protein" project we tested a number of fluorescent proteins, of which mVenus showed one of the highest levels of brightness and a high percentage of fluorescence reconstitution. Furthermore mVenus is designed to have a fast and efficient maturation time [1], exactly what we need! We show here how this experiment was designed.

Figure 7: CpxR dimerization visualized using eYFP (left) and mVenus (right), with L-arabinose concentration = 0.2% and different activator concentrations over time

The results show that usage of mVenus over eYFP as a reporter protein increases the strength of the produced fluorescent signal more then five times. Unfortunately, the background signal also increases a lot, which means we lose a lot of sensitivity. This means that mVenus is not a suitable candidate to visualize antigen binding.

At this moment, we recommend using sfGFP as a reporter for antigen binding. We found out sfGFP is thermostable and has efficient maturation at high temperatures, while still being one of the fastest and brightest reporters we tested.

Conclusions

During this project we modeled and experimentally tested several methods to find the optimal visualization method to use in Mantis. Three strategies were tested to find the optimal visualization method of antigen binding using the Cpx pathway. In the end, it was clear that fusion of eYFP-termini to CpxR and measuring its dimerization shows clear fluorescence within two hours after activation, even in low concentrations! This shows that we found a potentially usable method of rapidly and specifically measuring antigens in blood.

During this project, we constantly implemented results from the computer modeling into the experimental design, and vice versa. Doing so, we were able to predict correct induction and activation levels. This yielded experimental results which could be implemented in the model and gave us indications on how to perfect Mantis.

Furthermore, we had time to integrate this combined modeling and experimental project with another wet lab projects, where we analyze several split reporter proteins to potentially use in Mantis.

References

  1. Nagai, T., Ibata, K., Park, E. S., Kubota, M., & Mikoshiba, K. (2001). A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications. Nature Biotechnology, 20, 1585–1588.