Dr. Alex Nguyen Ba, Assistant Professor, developed a Cre/Lox CRISPR gene-drive system thus expanding gRNA array, to pop open the hood on 'global epistasis' (Alex's tweet)
Alex explained that:
"Organisms adapt by accumulating mutations that increase their fitness. A useful metaphor for this process is that of populations climbing a mountainous landscape. In this metaphor, specific paths taken to the various possible peaks are due to successive mutations, and irregularities in the terrain can be thought to be due to the specific combinations of mutations acquired along the way. A wealth of microbial evolution experiments suggest however that the uphill curvature can be, surprisingly, reproducibly concave, giving rise to ‘diminishing returns’ as fitness increases. Thus, in apparent contradiction with decades of work on biological networks, the systematic and consistent trend suggest that the specific nature of past mutations and interactions between them could be less critical in constraining adaptive trajectories than anticipated.
To unify both view of population adaptation, we developed a CRISPR-Cas9 gene drive system to comprehensively construct a first-of-its-kind synthetic fitness landscape in vivo. Doing so would allow us to probe the mechanism of ‘diminishing returns’, by separating the contributing effect of combinations of mutations to fitness as opposed to a ‘global’ and systematic effect that eclipses the details of the accumulating mutations.
By measuring the complete fitness landscape in several environments, we find that the systematic ‘global’ trend can be entirely decomposed into the underlying, simple, combination of mutations. We provide the first experimental proof that, statistically, these global trends emerge due to the widespread nature of simple interactions within large biological networks."
He is always on the lookout for talented students interested in exploring questions about evolution in well-controlled systems and quantitative genetics in all areas of life. If interested, visit: https://annb-lab.github.io/opportunities/
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