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<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. | <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. | ||
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Revision as of 18:13, 30 October 2017
![](https://static.igem.org/mediawiki/2017/6/6e/T--Wageningen_UR--HP_MobileIcon.png)
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 in the collapsed boxes 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 strongest signaling. We took these recommendations to the lab and found wich candidate system is indeed suitable for antigen visualization, and under which conditions this system works optimally.
We used the data gathered in the lab to fit with our computer model, to find out in what other ways our system can be optimized. We found several parameters that could improve the speed of our signaling, and we combined 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 connect to a BiFC reporter gene. 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.
![](https://static.igem.org/mediawiki/2017/e/e1/T--Wageningen_UR--CpxSystemsfade.png)
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. This is likely because one activated CpxA can amplify its signal by phosphorylating several response regulator CpxR's
![](https://static.igem.org/mediawiki/2017/b/b0/T--Wageningen_UR--InitModelCpxRdimerization.png)
Results of all three setups can be seen at the modeling page. Although the CpxA-CpxR setups (Figure 1B,C) is 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 propositions 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 pBAD/araC 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 3ABC). You can check this result here.
![](https://static.igem.org/mediawiki/2017/e/e4/T--Wageningen_UR--CpxR_dimerization_araC.png)
![](https://static.igem.org/mediawiki/2017/0/0a/T--Wageningen_UR--AReYFP-araC-.png)
![](https://static.igem.org/mediawiki/2017/b/b3/T--Wageningen_UR--CpxAR_interactionTEV_araC.png)
We then set to test hypothesis 2: The strongest signal will be obtained when Cpx activation is maximized. In Figure 3 we show that this is also true. We mimic antigen binding by adding a known activator of the Cpx pathway, and by increasing its concentration the fluorescence intensity also rises.
We take this data back to the lab to further improve our computer model!
![](https://static.igem.org/mediawiki/2017/e/e9/T--Wageningen_UR--CpxR_dimerization_KCl.png)
Phase 3: Fitting the computer model to the lab 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 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!
![](https://static.igem.org/mediawiki/2017/3/32/T--Wageningen_UR--datafit.png)
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).
![](https://static.igem.org/mediawiki/2017/3/32/T--Wageningen_UR--datafit.png)
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 (check out all the used parameters here). Given the iGEM time limits, we could not manage to confirm this hypothesis in the lab. One viable option, though, to improve system performance is to use a fluorophore with a faster maturation time (k6).
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 the fastest maturation time. 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.
![](https://static.igem.org/mediawiki/2017/b/b0/T--Wageningen_UR--CpxR_dimerization_eYFP_Venus.png)
The results show that usage of mVenus over eYFP as a reporter protein increases the produced fluorescent signal some five times! Unfortunately, the background signal also increases a lot, which means we lose a lot of specificity. We hypothesize that the maturation rate of mVenus is too high, which means that many non-specific interaction become irreversible, leading to high fluorescent signals, even when no activator is present. This means that mVenus is not a suitable candidate to visualize antigen binding. Through literature research we confirmed this hypothesis [2].
During this project, more reporter proteins were tested. Due to time constraints, we were unable to model these systems. At this moment, we recommend using sfGFP as a reporter for antigen binding. We found out sfGFP is thermostable, it 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
- 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.
- Shyu, Y. J., Liu, H., Deng, X., & Hu, C.-D. (2006). Identification of new fluorescent protein fragments for bimolecular fluorescence complementation analysis under physiological conditions. BioTechniques, 40(1), 61–66.