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

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 as well. 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 tested later during wet-lab experiments.

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 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. This is likely because one activated CpxA can amplify its signal by phosphorylating several different response regulator CpxR's

Figure 2: Relative fluorescent signal intensity (orange) and signaling speed (green) are plotted against the initial concentration of CpxR.

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 demonstrate 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.

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 cytoplasm using leucine zipper's natural affinity for each other.

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 rises as well.

We take this data back to the 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 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!

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.

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 fluorophore 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 affinity body 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 to improve the system performance is to use a fluorophore with a faster maturation time (k6).