Team:TAU Israel/Proof Of Concept
Proof Of Concept
Empirical POC
The proof-of-concept experiment that we conducted reflects on our project as a whole, because it demonstrates that our hypothesis is valid, and the solution that we offer is based on reliable principles.
To solve the problem of genetic stability of engineered constructs, we propose interlocking a target gene to the N terminus of an essential gene in the host organism. Our theory is that mutations on the target gene will cause a misfolding to the essential protein, or a frameshift to its reading frame. This will lead to a loss of function of the essential gene and to the mutated host's mortality.
Moreover, our user-friendly software will recommend the best matching conjugated gene for this purpose, assuming that each essential gene provides varying stability levels when attached to a specific target gene. Here we prove that these assumptions, on which our project is based, are indeed true.
The raw results appear in the Results page. Following the process of raw data analysis described in our Experiments page, the fluorescent values were normalized using the iGEM protocol for normalization of fluorescence measurement. The construct’s normalized fluorescence on the last day of the experiment was compared to its normalized fluorescence on the first day.
For this purpose, we extracted an indicative measure that reflects how much fluorescence was kept in the last day of the experiment compared to the initial fluorescence. This measure ranges from 0-1, when 1 means that the fluorescence did not change during the experiment (i.e. the construct is stable), and 0.5 means that half of the initial fluorescence was kept during the evolution experiment.
As you can see in the graph, the negative control had the second-lowest normalized fluorescence at the end of the experiment, and there is a significant increase in the evolutionary stability of GFP when fused to an essential gene.
Moreover, there is a significant difference in the ability of different conjugated genes to evolutionary stabilize the GFP, and presumably any other gene.
These results demonstrate that linking a target gene to an essential gene should improve its evolutionary half-life.
We received varying final fluorescence levels, indicating that other features might be important when selecting the essential gene. This is where our software comes into play – it can find the optimal construct and exploit this variability.
It should be emphasized that these ten constructs were selected for diversity and are none-optimal. Using our software for optimization, we expect much better results.
Thus, this experiment proves the validity of our solution and demonstrates the need for our software at the same time.
Reliability
We then tried to find confounding factors that could influence our results, and undermine their credibility. The first thing that we thought of, was that the difference in the fluorescence kept over the experiment might not be correlated to which gene the GFP was fused to, but rather to the initial fluorescence level.
As shown by the 2019 Austin iGEM team, there is a direct connection between the metabolic load the protein inflicts on the host, and the evolutionary stability of the gene.
Thus, although they are all under the same promotor, all the strains started from different fluorescence levels, as they are influenced by many factors (for instance, the difference in the localization of the genes, the structure of the protein, regulation of the protein level, and many more).
This phenomenon, if exists, could undermine the reliability of our results. We can find it using the correlation between initial fluorescence and the preservation of fluorescence at the end of the experiment.
In order to test it, we calculated the Spearman correlation between the initial fluorescence and the maintained normalized fluorescence at the end of the experiment. The correlation was -0.19 with a P. value of 0.57, which means there is no significant correlation. The results are plotted below, and it can be seen that the dots are randomly scattered, thus there is no significant correlation between the tested factors. This further solidify the credibility of our results.
