Mapping the fitness landscape of gene expression uncovers the cause of antagonism and sign epistasis between adaptive mutations.

Abstract:

:How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels. We found strong antagonism and sign epistasis between these beneficial mutations. Mutations with the largest individual benefit interacted the most antagonistically with other mutations, a trend we also uncovered through analyses of datasets from other model systems. However, these beneficial mutations interacted multiplicatively (i.e., no epistasis) at the level of enzyme expression. By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs. Knowledge of the phenotypic landscape also illuminated that, although the fitness peak was phenotypically far from the ancestral state, it was not genetically distant. Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture. Given that adaptation in nature often results from optimizing gene expression, these conclusions can be widely applicable to other organisms and selective conditions. Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation.

journal_name

PLoS Genet

journal_title

PLoS genetics

authors

Chou HH,Delaney NF,Draghi JA,Marx CJ

doi

10.1371/journal.pgen.1004149

subject

Has Abstract

pub_date

2014-02-27 00:00:00

pages

e1004149

issue

2

eissn

1553-7390

issn

1553-7404

pii

PGENETICS-D-13-02617

journal_volume

10

pub_type

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