Variations and predictability of epistasis on an intragenic fitness landscape

Author:

Baheti Sarvesh,Raj Namratha,Saini SupreetORCID

Abstract

AbstractHow epistasis hinders or facilitates movements on fitness landscapes has been a longstanding question of interest. Several high throughput experiments have demonstrated that despite its idiosyncrasy, epistatic effects exhibit global statistical patterns, namely, diminishing returns and increasing costs. Recently, Papkou et. al. constructed a fitness landscape for a 9-base region in thefolAgene, which encodes for Dihydrofolate Reductase (DHFR), inE. coli, and demonstrated that despite being highly rugged, the landscape is highly navigable. In this work, using thefolAlandscape data we ask two questions: (1) How does the nature of epistatic interactions change as a function of the genomic background? (2) How predictable is epistasis within a gene? We show that mutations exhibit one of two binary “states”: a small fraction of mutations exhibit extremely strong patterns of global epistasis, while most do not. As against this binary classification, epistasis is also very “fluid” - the nature of epistasis exhibited by a pair of mutations is strongly contingent on the genetic background. Despite this strong dependency, we observe that the DFE of a genotype is highly predictable based on its fitness. These results offer a perspective on how epistasis operates within a gene, and how it can be predicted.

Publisher

Cold Spring Harbor Laboratory

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