Abstract
Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.
Highlights
IntroductionLaboratory evolution experiments are opening an unprecedented window into the dynamics and genetic basis of adaptive change by de novo mutation (Crozat et al, 2010; Good et al, 2017; Huang et al, 2018; Lang et al, 2013; Levy et al, 2015; Tenaillon et al, 2012; Venkataram et al, 2016a)
If the ‘fitness-relevant modularity’ model depicted in Figure 1B is true it is possible that adaptive mutations are locally modular — that they affect very few phenotypes that matter to fitness in the evolution condition — and globally pleiotropic
The first question we address is whether these diverse mutations collectively affect a large number of phenotypes that matter to fitness, or whether these mutants are functionally similar in that they collectively alter a small set of fitness-relevant phenotypes
Summary
Laboratory evolution experiments are opening an unprecedented window into the dynamics and genetic basis of adaptive change by de novo mutation (Crozat et al, 2010; Good et al, 2017; Huang et al, 2018; Lang et al, 2013; Levy et al, 2015; Tenaillon et al, 2012; Venkataram et al, 2016a). While the identities of these adaptive mutations are often unique to a specific replicate of the evolutionary experiment, across many replicates they tend to occur in similar functional units (e.g. genes and pathways) (Crozat et al, 2010; Fumasoni and Murray, 2020; Good et al, 2017; Huang et al, 2018; Lang et al, 2013; Levy et al, 2015; Tenaillon et al, 2012; Venkataram et al, 2019, Venkataram et al, 2016a). Venkataram et al, 2016a found that, of the hundreds of unique genetic mutations that occur during adaptation to glucose-limitation, the vast majority fall into a relatively small number of genes (mostly IRA1, IRA2, GPB2, PDE2) and primarily two pathways — Ras/
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