Difference between revisions of "Team:Rice/Wet Lab"

 
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    <div class = "fixed_flyer" id = "sec1" style="position:relative;z-index:8;">
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    <div class = "h1">Overview</div>
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        <div class = "h1" style="color:white">Overview</div>
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  <div class="pagediv">
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    <div class="pagediv">
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      <br><br>
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  Photoacoustic imaging is a technique in which contrast agents absorb photon energy and emit signals that can be analyzed by ultrasound. Currently, photoacoustics is used to image blood vessels because heme is a natural contrast agent found in blood. Photoacoustic imaging also provides a non-invasive alternative to current diagnostic tools used to detect internal tissue inflammation. In previous literature, hypoxia and nitric oxide have both been discovered to molecularly indicate gut inflammation, and iRFP670, 713, anacy and cyan have been found to emit wavelengths that are different from heme and can penetrate tissue with near-infrared wavelengths. Therefore, our goal is to report inflammation and cancer in the gut through photoacoustic imaging of engineered E. coli that express bacterial pigment violacein, as well as near-infrared fluorescent proteins iRFP670 and iRFP713.
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      <div class = "para"> Photoacoustic imaging is a technique in which contrast agents absorb photon energy and emit signals that can be analyzed by ultrasound. Currently, photoacoustics is used to image blood vessels because heme is a natural contrast agent found in blood. Photoacoustic imaging also provides a non-invasive alternative to current diagnostic tools used to detect internal tissue inflammation. In previous literature, hypoxia and nitric oxide have both been discovered to molecularly indicate gut inflammation, and iRFP670, 713, anacy and cyan have been found to emit wavelengths that are different from heme and can penetrate tissue with near-infrared wavelengths. Therefore, our goal is to report inflammation and cancer in the gut through photoacoustic imaging of engineered E. coli that express bacterial pigment violacein, as well as near-infrared fluorescent proteins iRFP670 and iRFP713.</div>
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    <div class = "fixed_flyer" id = "sec2" style="position:relative;z-index:9;">
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        <div class = "h1" style="color:white">Arabinose Induced iRFP 670 and 713 Fluorescence</div>
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pBAD is a very well-characterized expression system in E. coli. pBAD normally works by arabinose induction: araC, a constitutively produced transcription regulator, changes form in the presence of arabinose sugar, allowing for the activation of promoter pBAD. Therefore, we formed genetic circuits consisting of the pBAD expression system and iRFP670 and 713 to test the inducibility of our iRFPs. <br><br>
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<img src= "https://static.igem.org/mediawiki/2016/d/df/Arabinose-Induced_Assay.jpg" width = “470px”>
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<div class = “para”>
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Figure 1a. Construct of arabinose-induced iRFP or violacein. In the presence of arabinose, araC activates pBad. <br><br>
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<img src= "https://static.igem.org/mediawiki/2016/1/1d/RiceArabinoseConstruct.jpg" width = "470px">
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<div class = “para”>
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Figure 1b. Fluorescence of iRFP 670 significantly correlates with arabinose levels.
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<img src = "https://static.igem.org/mediawiki/2016/e/e5/Arabinosefinal713.jpeg" width = "470 px">
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Figure 1c. Fluorescence of iRFP 713 significantly correlates with arabinose levels.
  
 
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Fluorescence levels emitted by the iRFPs increase significantly when placed in wells containing increasing concentrations of arabinose. The fluorescence levels increased 80-fold between 0.1 nM to 50 mM. This correlation suggest that our selected iRFPs fluoresce sufficiently when promoters are induced by the environment.
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    <div class = "h1">Objective</div>
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  <br>
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<div class="para">
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Create a biochemical model of the violacein production based on the synthetic
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pathway and violacein production data from bacteria with different promoters for
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each of the five genes involved in the pathway.
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<br><br>
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  <div class="fixed_flyer" id = "sec3" style="position:relative;z-index:3">
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    <div class = "h1">Model Assumptions</div>
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    <br>
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<div class="para">
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<ol>
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<li>The rate of dilution of the enzymes and the intermediaries is much greater than
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its degradation (for example by ubiquitination for the proteins or by conversion
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to products not included on the pathway)</li>
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<li>There is no saturation of the enzymes and all the reactions will follow the law
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of mass action</li>
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<li>Independence of external factors such as oxygen and NADH in the reactions</li>
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<li>None of the reactions are reversible</li>
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</ol>
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We use the mass action kinetics because this type of equation
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only requires one parameter for reaction and is less susceptible to overdosing
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    <div class = "fixed_flyer" id = "sec3" style="position:relative;z-index:10;">
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        <div class = "h1" style="color:white">Nitric-oxide-Induced Fluorescence</div>
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      <br><br>
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<div class = "para" style="float:left">
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The next step was to test the nitric oxide induction of iRFP fluorescence. We used a genetic circuit consisting of a constitutive promoter that always expresses Part:BBa_K554003, which encodes for the expression of a SoxR. In the presence of nitric oxide, SoxR changes form to activate the promoter SoxS, which in turn is supposed to activate the expression of the iRFPs. Thus, for the next assay we added DETA/NO, a nitric oxide adduct in the presence of water.
 
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<img src = "https://static.igem.org/mediawiki/2016/c/cf/Nitricoxideconstruct.jpeg" width = “470 px”>
  
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<div class = "para">
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  <div class="fixed_flyer" id = "sec4" style="position:relative;z-index:4">
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    <div class = "h1">Model Building Process</div>
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  <div class="pagediv">
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<br>
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<div class="para">
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<div class="h3">1. Modeling Promoter Strength</div>
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<br>
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Because a major goal of the model is to predict the effects of the selection of
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promoters on the final production of violacein, we decided to find a way to
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characterize promoters first. To simplify the computation, we used the promoter
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strength as a single standard to characterize the promoters. Moreover, we assumed
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the degradation rate of proteins only depends on the growth rate of E.coli. Then,
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every enzyme has the same degradation rate. The bacteriophage T7 promoter has
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been widely used for protein expression and purification (Jones et al., 2013),
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so we used data of five mutant T7 promoters to create a proof-of-concept model.
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If this model was functional, we could implement the same modeling technique to
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the promoters we were working with.The five mutant T7 promoters have distinct
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promoter strength over time after induction. The experimental data from the
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  literature are shown in the figure below (Jones et al., 2013).
+
  
 +
Figure 2a: Construct of nitric oxide activation of iRFP or violacein. Nitric oxide induces transcription of the SoxR protein, which binds to the SoxS promoter to promote transcription of iRFP or violacein.
 +
</div>
 
<br><br>
 
<br><br>
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<img src="https://static.igem.org/mediawiki/2016/3/32/Promoter_Strengh_vs_Time_paper.png" style="display: block; margin: auto; width: 80%">
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<img src = "https://static.igem.org/mediawiki/2016/2/24/NO670assay.jpeg" width = “470 px”>
 
<br><br>
 
<br><br>
 +
<div class = "para">
  
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The first step of our model is to describe the rate of change of enzymes based on promoter strength. Here we assumed that the enzyme production rate is directly proportional to strength of the promoter. Therefore, we were able to use a mass-action kinetics equation of promoters to describe the enzyme concentration. The equation is shown below:
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Figure 2b. Fluorescence levels of iRFP 670 do not differ significantly upon increasing concentrations of the nitric oxide adduct DETA/NO.
 +
</div>
  
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<br><br>
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<img src = "https://static.igem.org/mediawiki/2016/9/90/NO713assay.jpeg" width = 470 px”>
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<img src="https://static.igem.org/mediawiki/2016/2/28/Protomter_Equations_new.png" style="display: block; margin: auto; width: 25%">
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<br><br>
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In this equation, Ai is the concentration of enzyme i, ki­ is the production rate of each  enzyme i, kd is the degradation rate of all enzymes, and t is time. By solving this equation, we derived the equation of enzyme concentration against time.
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<div class = "para">
  
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<br><br>
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Figure 2c. Fluorescence levels of iRFP 713 do not differ significantly upon increasing concentrations of the nitric oxide adduct DETA/NO.
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<img src="https://static.igem.org/mediawiki/2016/5/54/Promoter_ODE_new.png" style="display: block; margin: auto; width: 25%">
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<br><br>
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Since we assumed that the promoter strength is proportional to the promoter concentration, we would use the equation to fit our data using least squares method (Fig. 1).
 
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<br><br>
 
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<img src="https://static.igem.org/mediawiki/2016/thumb/3/3a/Fitted_Lines_of_Promoter_Strength_vs_Time.png/800px-Fitted_Lines_of_Promoter_Strength_vs_Time.png" style="display: block; margin: auto; width: 80%">
 
 
<br><br>
 
<br><br>
  
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<b>Figure 1.</b> Linear regressions fitted to normalized fluorescence vs time. The circles represent data from Jones et al., 2013. The solid lines are our regression lines. The colors indicate with which promoters the circles and lines correspond.
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These graphs show no significant difference of fluorescence/OD600 between DETA/NO concentrations.
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<br><br>
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In general, the regression lines are able to capture the change of strength of each enzyme over time. In this way, the parameters are determined. The table below lists the parameter values.
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<br><br>
 
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<img src="https://static.igem.org/mediawiki/2016/7/7e/Promoter_Strength_Fit_Parameters.png" style="display: block; margin: auto; width: 60%">
 
 
<br><br>
 
<br><br>
  
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<b>Table 1.</b> Parameters realted to promoter strength and degradation of molecules. In the table, ki­ (i = 1,2,3,4,5) are the production rate coefficients of promoter I (i = 1,2,3,4,5), and kd is the degradation rate coefficient of all promoters.
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**For materials and methods for nitric oxide assay, see July lab notebook
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    <div class = "fixed_flyer" id = "sec4" style="position:relative;z-index:10;">
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        <div class = "h1" style="color:white">Hypoxia-Induced Fluorescence</div>
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    </div>
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    <div class="pagediv">
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      <br><br>
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      <div class = "para"><br>
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In addition to the sinduction of iRFP fluorescence by nitric oxide, we also tested the induction of iRFP fluorescence with a hypoxia promoter. We expected iRFP fluorescence to increase with increased hypoxic conditions (less oxygen) when using NarK promoter and fdhf promoters, both characterized as hypoxia-inducible.  
  
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<br><br>
 
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<div class='h3'>2. Modeling the Steady-state Violacein Yield</div>
 
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<br>
 
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After we finished the regression model of each promoter, we created a second model to describe the violacein biosynthetic pathway. The pathway (Fig. 2) involves five enzyme-catalyzed reactions and one non-enzymatic reaction (Lee et al, 2013).
 
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<br><br>
 
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<img src= "https://static.igem.org/mediawiki/2016/thumb/2/2f/Violacein_Biosynthetic_Pathway.png/737px-Violacein_Biosynthetic_Pathway.png" style="display: block; margin: auto; width: 80%">
 
 
<br><br>
 
<br><br>
  
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<b>Figure 2.</b> Violacein synthetic pathway. The purple arrows highlight the five enzymatic and one non-enzymatic steps of violacein production from two molecules of tryptophan. The five enzymes are indicated by bolding (VioA, VioB, etc.).
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Transcription of the fdhf promoter is regulated by an RNA polymerase with sigma factor 54 whose binding is dictated by presence of an additional activator complex consisting of FhlA and formate. Only when the FhlA-formate complex is present will the sigma-54 polymerase initiate transcription. This process is induced by formate, but is also heavily repressed by presence of oxygen, giving it characterization as a hypoxia sensor.  
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      </div>
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<img src="https://static.igem.org/mediawiki/2016/f/f3/Hypoxianarkconstruct.jpeg" width=“470px”>
  
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<br><br>
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<div class = "para">
  
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The model was developed as three major parts. A pseudocode of this model is provided here.
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Figure 3a. Construct for hypoxia induced transcription of iRFP or violacein using the narK promoter.
  
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<br>
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</div>
  
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<b>Define ODE System</b>
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<img src="https://static.igem.org/mediawiki/2016/1/1c/Hypoxiafdhfconstruct.jpeg" width=“470px”>
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<ol>
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<li>Calculate the production and degradation rate of each molecule in the pathway from the concentration of reagents and parameters.</li>
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<li>Obtain the rate of change of each molecule based on the production and degradation rates.</li>
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</ol>
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<b>Solve the System of Nonlinear Equations at Steady State</b>
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<ol>
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<li>Solve the system of nonlinear equations at steady state starting at an initial guess X0.</li>
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<li>Use the result as a new initial guess; repeat the numerical method to solve the system of equations again.</li>
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<li>Calculate the relative error of each chemical in the new result.</li>
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<li>If the maximum error is smaller than 0.0001%, output violacein concentration at steady state as the final result.</li>
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</ol>
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<b>Optimize Parameters to Fit Experimental Data</b>
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<ol>
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<li>Set the initial guess of the parameters.</li>
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<li>Load the data from literature, which include the choice of promoter for each gene and the corresponding violacein yield determined experimentally.</li>
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<li>For each promoter selection scenario, pass the promoter types and the temporary parameters to the steady-state model.</li>
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<li>Obtain the violacein yield predicted by the steady-state model for each promoter selection scenario.</li>
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<li>Compute the residual sum of squares (RSS) of between the predicted violacein yields and the violacein yields given by experiment.</li>
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<li>Determine the optimal parameters by minimizing the RSS (least square method).</li>
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</ol>
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<br>
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<div class = "para">
  
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Using the principles of mass action kinetics, we derived the system of ODE equations in the model. The equations involves 17 parameters (Table 2). Five parameters (kA, kB, kC, kD and kE) are related to the production rates of the five enzymes, which depend only the strength of the promoter type. Another parameter, kd, is the degradation coefficient of all molecules due to the growth of E.coli. The value of this parameter is fixed  and shown in Table1. In addition to these known parameters, the equations include 11 undetermined parameters related to the reaction rates at specific steps in the violacein synthetic pathway. As described in the pseudocode, we used least square regression to determine the optimal values of these parameters.
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Figure 3b. Construct for hypoxia induced transcription of iRFP or violacein using the fdhf promoter. The fdhA gene is constitutively promoted by the constitutive promoter. In the presence of formate, the protein that is produced from the fdhA gene (formate dehydrogenase) induces transcription of the desired iRFP/violacein.
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<br><br>
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<br> <br>
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Each one of the11 differential equations describes the rate of change of specific molecule in the system. The equations consider the production, consumption, and degradation rates of the molecules. Degradation of molecules is described by first order decay. Therefore, the rate of degradation of a molecule depends on a degradation constant and the degradation coefficient. The degradation coefficient is identical for all molecules since it only depends on E.coli growth rate.
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<br><br>
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<div class="h3"> Differential Equations in the Model</div>
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However, we observed the opposite result. A decreased fluorescence for both iRFP constructs in both promoters was measured when exposed to hypoxic conditions.
  
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<img src="https://static.igem.org/mediawiki/2016/c/cf/Enzyme_Production_Rate.png" style="display: block; margin: auto; width: 80%">
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<img src="https://static.igem.org/mediawiki/2016/thumb/7/7f/Chemical_Production_Rate_1.png/1200px-Chemical_Production_Rate_1.png" style="display: block; margin: auto; width: 100%">
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<img src = "https://static.igem.org/mediawiki/2016/9/99/Nark670.png" width="470px">
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<img src="https://static.igem.org/mediawiki/2016/thumb/f/fd/Chemical_Production_Rate_2.png/1199px-Chemical_Production_Rate_2.png" style="display: block; margin: auto; width: 100%">
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  <div class = "h1">Results</div>
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Figure 3c. Fluorescence levels of iRFP 670 upon induction by a NarK promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.
 
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Our model is able to compute the average violacein yields for all the strains tested experimentally, but can not capture the difference of violacein yield with different promoters strengths. The comparison between the violacein yields determined by experiments and those predicted by our model is shown in Figure 3. The optimal parameters determined by the model are listed in Table 2.
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<img src = "https://static.igem.org/mediawiki/2016/c/c7/Fdhf670.png" width="470px">
  
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<br><br>
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<div class ="para">
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<img src="https://static.igem.org/mediawiki/2016/thumb/0/01/Violacein_Yields_Model_Prediction_vs_Data.png/800px-Violacein_Yields_Model_Prediction_vs_Data.png" style="display: block; margin: auto; width: 100%">
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Figure 3d. Fluorescence levels of iRFP 670 upon induction by a fdhf promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.
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<br><br>
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</div>
  
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<b>Figure 3.</b> VIolacein yield with different promoter combinations. This graph compares the violacein found for various promoter combinations determined by Jones et al., 2013 (shown in blue) with the violacein concentrations that our model predicted for the same promoter combinations. The root-mean-square error (RMSE) is 52.04.
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<img src = "https://static.igem.org/mediawiki/2016/4/45/Fdhf713.png" width="470px">
  
 +
<div class ="para">
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Figure 3e. Fluorescence levels of iRFP 713 upon induction by a fdhf promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.
 
<br><br>
 
<br><br>
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<img src="https://static.igem.org/mediawiki/2016/b/bb/Full_parameter_table.png" style="display: block; margin: auto; width: 100%">
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**For materials and methods for hypoxia assay, see July and October lab notebook
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<br><br>
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<b>Table 2.</b> Notations of parameters.
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        <div class = "h1" style="color:white">Conclusion</div>
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      <div class = "para"><br>
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Violacein is a fluorescent protein for in vivo photoacoustic imaging in the near-infrared range and shows anti-tumoral activity. Violacein has high potential for future work in bacterial tumor targeting. We have succeeded in constructing violacein. Refer to “Future Directions.”
  
 
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    <div class = "h1">Discussion</div>
 
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<img src ="https://static.igem.org/mediawiki/2016/4/4b/Violaceinplate.png" width="470">
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  <br>
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<div class = "para">
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  <div class="para">
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Figure 4a. Plate of successfully produced violacein colonies.
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The current model is not able to show the expected dependence of violacein yield on promoter strength. After reevaluating our assumptions, we identified some potential flaws of the model that might cause the unexpected results.
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<div>
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One of the assumptions from our model is that the rate of production of L-tryptophan is constant and independent of the promoter strength. Jones el al. suggest that the L-tryptophan production rate may be affected by the metabolic burden of the production of the recombinant enzymes (VioA, VioB, etc.). This phenomenon may be caused by the depletion of essential metabolic resource, such as amino acids, mRNA and ATP. Therefore, the L-tryptophan production rate might need to be dependent on enzymes production rates.
+
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<br><br>
+
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Another effect that we didn’t consider is the saturation of the enzymes. To improve our model, we could include these effects by employing Michaelis-Menten Kinetics equations in our next step. Nevertheless, we have been cautious about including this in our model, since increasing the number of parameters, without increasing the number of data points usually causes the overfitting of the model.
+
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<br><br>
+
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Finally, since the violacein pathway has not been fully characterized, it is possible that we ignored some reactions in the complete pathway. Moreover, there may be feedback loops that regulate the pathway. We will need to investigate these possible components and incorporate them into our model if they prove to be present in the pathway.
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      <div class = "para"><br>
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    Here we present a method to fit a model of violacein production in E.coli to experimental data of violacein yield with different promoters using nonlinear regression. Although  it fails to calculate the dependence on promoter strength, our model is able predict the average violacein concentration. We expect that small changes on the model, such as including a L-tryptophan production dependence of the metabolic burden, would allow us to successfully predict the violacein production in response to the variation of promoter strength. Once the predictive model is complete, we will be able to find the strains that lead to optimal violacein yield computationally.
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<ol>
−
    </div>
+
<li>Anderson, J. (2006). Environmentally controlled invasion of cancer cells by engineered bacteria. <i>Science Direct</i> <br><a href="http://dx.doi.org/10.1016/j.jmb.2005.10.076">http://dx.doi.org/10.1016/j.jmb.2005.10.076
 +
</a></li>
 +
<li>Archer, E. J. <i>et al.</i> Engineered E. coli that detect and respond to gut inflammation through NO sensing.<i>ACS Synthetic Biology, 10</i>(1), 451-457.
 +
<li>Engler, C. (2009). Golden gate shuffling: A one-pot DNA shuffling method based on type 2s restriction enzymes. <i>PLOS One.</i> <br> <a href="http://dx.doi.org/10.1371/journal.pone.0005553">http://dx.doi.org/10.1371/journal.pone.0005553</a></li>
 +
<li>Guzman, L. M. (1995). Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. <i>Journal of Bacteriology,</i>, 4121-4130. Retrieved from <br> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC177145/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC177145/</a></li>
 +
<li>Jiang, Y. <i>et al.</i>Violacein as a genetically-controlled, enzymatically amplified and photobleaching-resistant chromophore for optoacoustic bacterial imaging.<i>Sci. Rep. 5,</i> 11048.
 +
<li>Nedosekin, D. (2013). Photoacoustic and photothermal detection of circulating tumor cells, bacteria and nanoparticles in cerebrospinal fluid in vivo and ex vivo. <i>Journal of Biophotonics.</i>, <br> <a href="http://dx.doi.org/10.1002/jbio.201200242
 +
">http://dx.doi.org/10.1002/jbio.201200242
 +
</a></li>
 +
<li>Ntziachristos, V. (2010). Going deeper than microscopy: The optical imaging frontier in biology. <i>Nature Methods</i>, 603-614. <br> <a href="http://dx.doi.org/doi:10.1038/nmeth.1483">http://dx.doi.org/doi:10.1038/nmeth.1483</a></li>
 +
<li>Rockwell, N. C. (2016). Identification of cyanobacteriochromes detecting far-Red light. <i>American Chemical Society</i>. <br> <a href="http://dx.doi.org/10.1021/acs.biochem.6b00299">http://dx.doi.org/10.1021/acs.biochem.6b00299</a></li>
 +
<li>Weber, J. (2016). Contrast agents for molecular photoacoustic imaging. <i>Nature Methods.</i>, <br> <a href="http://dx/doi/org/dow:10.1038/nmeth.3929
 +
">http://dx/doi/org/dow:10.1038/nmeth.3929
 +
</a></li>
 +
<li>Yao, J. (2015). Multiscale photoacoustic tomography using reversibly switchable bacterial phytochrome as a near-infrared photochromic probe.  <i>Nature Methods.</i> <br> <a href="http://dx.doi.org/doi:10.1038/nmeth.3656
 +
">http://dx.doi.org/doi:10.1038/nmeth.3656</a></li>
 +
 
 +
</ol>
 +
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     </div>
  
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  <div class="para">
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<ol>
 
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<li>Carvalho, D. D., Costa, F. T. M., Duran, N., & Haun, M. (2006). Cytotoxic activity of violacein in human colon cancer cells. <i>Toxicology in Vitro</i>, 20(8), 1514–1521. <br><a href="http://dx.doi.org/10.1016/j.tiv.2006.06.007">http://dx.doi.org/10.1016/j.tiv.2006.06.007</a></li>
 
−
<li>Jones, J. A., Vernacchio, V. R., Lachance, D. M., Lebovich, M., Fu, L., Shirke, A. N., … Koffas, M. A. G. (2015). ePathOptimize: A Combinatorial Approach for Transcriptional Balancing of Metabolic Pathways. <i>Scientific Reports</i>, 5, 11301. <br><a href="http://doi.org/10.1038/srep11301">http://doi.org/10.1038/srep11301</a></li>
 
−
<li>Lee, M. E., Aswani, A., Han, A. S., Tomlin, C. J., & Dueber, J. E. (2013). Expression-level optimization of a multi-enzyme pathway in the absence of a high-throughput assay. <i>Nucleic Acids Research</i>, 41(22), 10668–10678. <br> <a href="http://doi.org/10.1093/nar/gkt809">http://doi.org/10.1093/nar/gkt809</a></li>
 
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Latest revision as of 22:12, 27 November 2016















Overview


Photoacoustic imaging is a technique in which contrast agents absorb photon energy and emit signals that can be analyzed by ultrasound. Currently, photoacoustics is used to image blood vessels because heme is a natural contrast agent found in blood. Photoacoustic imaging also provides a non-invasive alternative to current diagnostic tools used to detect internal tissue inflammation. In previous literature, hypoxia and nitric oxide have both been discovered to molecularly indicate gut inflammation, and iRFP670, 713, anacy and cyan have been found to emit wavelengths that are different from heme and can penetrate tissue with near-infrared wavelengths. Therefore, our goal is to report inflammation and cancer in the gut through photoacoustic imaging of engineered E. coli that express bacterial pigment violacein, as well as near-infrared fluorescent proteins iRFP670 and iRFP713.


Arabinose Induced iRFP 670 and 713 Fluorescence


pBAD is a very well-characterized expression system in E. coli. pBAD normally works by arabinose induction: araC, a constitutively produced transcription regulator, changes form in the presence of arabinose sugar, allowing for the activation of promoter pBAD. Therefore, we formed genetic circuits consisting of the pBAD expression system and iRFP670 and 713 to test the inducibility of our iRFPs.



Figure 1a. Construct of arabinose-induced iRFP or violacein. In the presence of arabinose, araC activates pBad.



Figure 1b. Fluorescence of iRFP 670 significantly correlates with arabinose levels.




Figure 1c. Fluorescence of iRFP 713 significantly correlates with arabinose levels.

Fluorescence levels emitted by the iRFPs increase significantly when placed in wells containing increasing concentrations of arabinose. The fluorescence levels increased 80-fold between 0.1 nM to 50 mM. This correlation suggest that our selected iRFPs fluoresce sufficiently when promoters are induced by the environment.


Nitric-oxide-Induced Fluorescence


The next step was to test the nitric oxide induction of iRFP fluorescence. We used a genetic circuit consisting of a constitutive promoter that always expresses Part:BBa_K554003, which encodes for the expression of a SoxR. In the presence of nitric oxide, SoxR changes form to activate the promoter SoxS, which in turn is supposed to activate the expression of the iRFPs. Thus, for the next assay we added DETA/NO, a nitric oxide adduct in the presence of water.
Figure 2a: Construct of nitric oxide activation of iRFP or violacein. Nitric oxide induces transcription of the SoxR protein, which binds to the SoxS promoter to promote transcription of iRFP or violacein.




Figure 2b. Fluorescence levels of iRFP 670 do not differ significantly upon increasing concentrations of the nitric oxide adduct DETA/NO.
Figure 2c. Fluorescence levels of iRFP 713 do not differ significantly upon increasing concentrations of the nitric oxide adduct DETA/NO.

These graphs show no significant difference of fluorescence/OD600 between DETA/NO concentrations.

**For materials and methods for nitric oxide assay, see July lab notebook








Hypoxia-Induced Fluorescence



In addition to the sinduction of iRFP fluorescence by nitric oxide, we also tested the induction of iRFP fluorescence with a hypoxia promoter. We expected iRFP fluorescence to increase with increased hypoxic conditions (less oxygen) when using NarK promoter and fdhf promoters, both characterized as hypoxia-inducible.

Transcription of the fdhf promoter is regulated by an RNA polymerase with sigma factor 54 whose binding is dictated by presence of an additional activator complex consisting of FhlA and formate. Only when the FhlA-formate complex is present will the sigma-54 polymerase initiate transcription. This process is induced by formate, but is also heavily repressed by presence of oxygen, giving it characterization as a hypoxia sensor.
Figure 3a. Construct for hypoxia induced transcription of iRFP or violacein using the narK promoter.
Figure 3b. Construct for hypoxia induced transcription of iRFP or violacein using the fdhf promoter. The fdhA gene is constitutively promoted by the constitutive promoter. In the presence of formate, the protein that is produced from the fdhA gene (formate dehydrogenase) induces transcription of the desired iRFP/violacein.

However, we observed the opposite result. A decreased fluorescence for both iRFP constructs in both promoters was measured when exposed to hypoxic conditions.
Figure 3c. Fluorescence levels of iRFP 670 upon induction by a NarK promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.
Figure 3d. Fluorescence levels of iRFP 670 upon induction by a fdhf promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.
Figure 3e. Fluorescence levels of iRFP 713 upon induction by a fdhf promoter differ significantly upon oxygen vs. no oxygen (hypoxic conditions); the iRFP has significantly higher fluorescence for the cultures grown in aerobic conditions.

**For materials and methods for hypoxia assay, see July and October lab notebook


Conclusion



Violacein is a fluorescent protein for in vivo photoacoustic imaging in the near-infrared range and shows anti-tumoral activity. Violacein has high potential for future work in bacterial tumor targeting. We have succeeded in constructing violacein. Refer to “Future Directions.”
Figure 4a. Plate of successfully produced violacein colonies.


References



  1. Anderson, J. (2006). Environmentally controlled invasion of cancer cells by engineered bacteria. Science Direct
    http://dx.doi.org/10.1016/j.jmb.2005.10.076
  2. Archer, E. J. et al. Engineered E. coli that detect and respond to gut inflammation through NO sensing.ACS Synthetic Biology, 10(1), 451-457.
  3. Engler, C. (2009). Golden gate shuffling: A one-pot DNA shuffling method based on type 2s restriction enzymes. PLOS One.
    http://dx.doi.org/10.1371/journal.pone.0005553
  4. Guzman, L. M. (1995). Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. Journal of Bacteriology,, 4121-4130. Retrieved from
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC177145/
  5. Jiang, Y. et al.Violacein as a genetically-controlled, enzymatically amplified and photobleaching-resistant chromophore for optoacoustic bacterial imaging.Sci. Rep. 5, 11048.
  6. Nedosekin, D. (2013). Photoacoustic and photothermal detection of circulating tumor cells, bacteria and nanoparticles in cerebrospinal fluid in vivo and ex vivo. Journal of Biophotonics.,
    http://dx.doi.org/10.1002/jbio.201200242
  7. Ntziachristos, V. (2010). Going deeper than microscopy: The optical imaging frontier in biology. Nature Methods, 603-614.
    http://dx.doi.org/doi:10.1038/nmeth.1483
  8. Rockwell, N. C. (2016). Identification of cyanobacteriochromes detecting far-Red light. American Chemical Society.
    http://dx.doi.org/10.1021/acs.biochem.6b00299
  9. Weber, J. (2016). Contrast agents for molecular photoacoustic imaging. Nature Methods.,
    http://dx/doi/org/dow:10.1038/nmeth.3929
  10. Yao, J. (2015). Multiscale photoacoustic tomography using reversibly switchable bacterial phytochrome as a near-infrared photochromic probe. Nature Methods.
    http://dx.doi.org/doi:10.1038/nmeth.3656