Variations and predictability of epistasis on an intragenic fitness landscape
Abstract
How epistasis hinders or facilitates movement 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. 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, 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? Our results show that epistasis is “fluid” - the nature of epistasis exhibited by a pair of mutations is strongly contingent on the genetic background. Mutations exhibit one of two binary “states”: a small fraction of mutations exhibit extremely strong patterns of global epistasis, while most do not. Despite these observations, we observe that the distribution of fitness effects (DFE) of a genotype is highly predictable based on its fitness. These results offer a new perspective on how epistasis operates within a gene, and how it can be predicted.
Significance Statement.
How a mutation changes organismal fitness is dependent on the genome in which it occurs. This phenomenon is known as epistasis and makes evolution unpredictable. Recent efforts to understand epistasis have led to the identification of statistical patterns in its manifestations. To study how epistasis operates in protein evolution, we analyze a recently reported landscape which quantifies fitness of ∼260000 sequences of anE. coligene. We show two previously unknown properties of epistasis: “fluid” (epistasis between mutations is controlled via other epistatic interactions) and “binary” (only a few mutations exhibit statistical patterns; most do not). This work sheds new light on how epistasis manifests in gene sequences. Our results have important consequences for protein & organismal evolution.
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