Part:BBa_K3463017:Experience

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Characterization BBa_K3463017

To validate the UTR designer prediction and check the B0017 relevance, we measured the fluorescence and the absorbance of the cultures of the strains containing one of the following constructs: PtacHigh-eGFP, PtacLow-eGFP, PtacHigh-eGFP-B0017 BBa_K3463018, PtacLow-eGFP-B0017 BBa_K3463017 , and a non-transformed strain as a negative control for 4,5h. The experiment was carried out 3 times.

We obtained the following result shown in figure 1.

Figure 1 Effect of UTR prediction and B0017 terminator on eGFP fluorescence expression. The Figure 5 shows that no fluorescence was detected in the culture of the control non- transformed strain. As predicted by UTR designer the strong 5’UTR sequence (high) gave higher fluorescent levels than the weak 5’UTR sequence (Low). The terminator B0017 seems to have a limited effect on the fluorescent levels. The last seems to only impact the eGFP production of the strong UTR sequence.

First, the influence of the UTR over the fluorescent level obtained was confirmed by an Anova displayed in figure 2.

Figure 2: Anova results of constructs influence over fluorescent. Constructs have significant influence over fluorescent : P-value <2E-16

And secondly, as predicted in-silico by UTR designer, the HIGH_5’UTR has a significantly stronger expression level than the LOW_5’UTR. However, during our experiment, we didn’t find any significant impact of the B0017 terminator on the fluorescence. It should be noted that the relative fluorescence level between the difference constructions seems to be slightly different from our prediction with a HIGH relative expression of 100% and a LOW relative of 50%. According to our model, the relative expression of the LOW prediction was around 75% for a HIGH fixed at 100%. A part of this difference can be explained by the prediction incertitude. But further investigation and experiments need to be done in order to fully assess the origin of this difference. This experiment opens new ways to future iGEM participants that could use a reliable tool to predict protein expression and therefore give them the ability to build complex genetic networks.

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