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

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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>
 
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://2016.igem.org/File:Fdhf713.png” width = “470px”>
 
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Figure 1a. Construct of arabinose-induced iRFP or violacein. In the presence of arabinose, araC activates pBad. <br><br>
 
Figure 1a. Construct of arabinose-induced iRFP or violacein. In the presence of arabinose, araC activates pBad. <br><br>

Revision as of 03:51, 22 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.
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.








Hypoxia-Induced Flourescence



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.


Violacein



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.

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.

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.

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.



Conclusion



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.


References



  1. Carvalho, D. D., Costa, F. T. M., Duran, N., & Haun, M. (2006). Cytotoxic activity of violacein in human colon cancer cells. Toxicology in Vitro, 20(8), 1514–1521.
    http://dx.doi.org/10.1016/j.tiv.2006.06.007
  2. 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. Scientific Reports, 5, 11301.
    http://doi.org/10.1038/srep11301
  3. 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. Nucleic Acids Research, 41(22), 10668–10678.
    http://doi.org/10.1093/nar/gkt809


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.