Transient mutation bias increases the predictability of evolution on an empirical genotype-phenotype landscape

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Abstract

Predicting how a population will likely navigate a genotype-phenotype landscape requires consideration of selection in combination with mutation bias, which can skew the likelihood of following a particular trajectory. Strong and persistent directional selection can drive populations to ascend toward a peak. However, with a greater number of peaks and more routes to reach them, adaptation inevitably becomes less predictable. Transient mutation bias, which operates only on one mutational step, can influence landscape navigability by biasing the mutational trajectory early in the adaptive walk. This sets an evolving population upon a particular path, constraining the number of accessible routes and making certain peaks and routes more likely to be realized than others. In this work, we employ a model system to investigate whether such transient mutation bias can reliably and predictably place populations on a mutational trajectory to the strongest selective phenotype or usher populations to realize inferior phenotypic outcomes. For this we use motile mutants evolved from ancestrally non-motile variants of the microbe Pseudomonas fluorescens SBW25, of which one trajectory exhibits significant mutation bias. Using this system, we elucidate an empirical genotype-phenotype landscape, where the hill-climbing process represents increasing strength of the motility phenotype, to reveal that transient mutation bias can facilitate rapid and predictable ascension to the strongest observed phenotype in place of equivalent and inferior trajectories. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Original languageEnglish
Article number20220043
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume378
Issue number1877
Early online date3 Apr 2023
DOIs
Publication statusPublished - 22 May 2023

Data Availability Statement

Data accessibility
The raw data for this study are available on the Open Science Framework (OSF), accessible at: https://osf.io/mg879/.

The data are provided in the electronic supplementary material.

Keywords

  • epistasis
  • experimental evolution
  • genotype-phenotype landscape
  • historical contingency
  • mutation bias
  • mutational hotspot

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences

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