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Mapping the Fitness Landscape of Gene Expression Uncovers the Cause of Antagonism and Sign Epistasis between Adaptive Mutations

Authors: Chou, Hsin-Hung; Draghi, Jeremy A.; Delaney, Nigel Francis; Marx, Christopher J;

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.

PLoS Genetics, 10 (2)

ISSN:1553-7390

ISSN:1553-7404

Keywords

570, Population genetics, Natural selection, Evolutionary biology, Metabolic networks, QH426-470, Biochemistry, Microbiology, Microbial metabolism, Gene Expression Regulation, Enzymologic, 576, Computational biology, Bacterial evolution, Evolution, Molecular, Methylobacterium extorquens, Genetics, Microevolution, Molecular genetics, Selection, Genetic, Biology, Forms of evolution, Microbial mutation, Microbial physiology, Microbial evolution, Bacteriology, Epistasis, Genetic, Adaptation, Physiological, Metabolism, Phenotype, Metabolic pathways, Mutation, Bacterial biochemistry, Gene expression, Genetic Fitness, Systems biology, Research Article

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
73
Top 10%
Top 10%
Top 10%
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