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Network of epistatic interactions within a yeast snoRNA

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Science  13 May 2016:
Vol. 352, Issue 6287, pp. 840-844
DOI: 10.1126/science.aaf0965

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Epistasis and mutational fitness landscape

A fitness landscape of a gene defines the molecular potential of evolution. This can help us understand the current state of evolution as well as predict unrealized potential. Using deep sequencing to examine mutations in nonessential genes that affect the growth of yeast strains, two studies have generated fitness landscapes and measured the effect of epistatic interactions (see the Perspective by He and Liu). Li et al. generated a library of mutants in a transfer RNA gene, including all single and many double and multiple mutants. The RNA secondary structure was generally predictive of bases under selection. Similarly, Puchta et al. assessed a small nucleolar RNA gene for the fitness effects of individual mutations, which correlated with evolutionary conservation and structural stability. Both studies suggest that epistasis—the combined functional effect—for double substitutions is more often negative than positive.

Science, this issue pp. 837 and 840; see also p. 769

Abstract

Epistatic interactions play a fundamental role in molecular evolution, but little is known about the spatial distribution of these interactions within genes. To systematically survey a model landscape of intragenic epistasis, we quantified the fitness of ~60,000 Saccharomyces cerevisiae strains expressing randomly mutated variants of the 333-nucleotide-long U3 small nucleolar RNA (snoRNA). The fitness effects of individual mutations were correlated with evolutionary conservation and structural stability. Many mutations had small individual effects but had large effects in the context of additional mutations, which indicated negative epistasis. Clusters of negative interactions were explained by local thermodynamic threshold effects, whereas positive interactions were enriched among large-effect sites and between base-paired nucleotides. We conclude that high-throughput mapping of intragenic epistasis can identify key structural and functional features of macromolecules.

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