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Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape
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 towar...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067260/ https://www.ncbi.nlm.nih.gov/pubmed/37004722 http://dx.doi.org/10.1098/rstb.2022.0043 |
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author | Horton, James S. Ali, Shani U. P. Taylor, Tiffany B. |
author_facet | Horton, James S. Ali, Shani U. P. Taylor, Tiffany B. |
author_sort | Horton, James S. |
collection | PubMed |
description | 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’. |
format | Online Article Text |
id | pubmed-10067260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-100672602023-04-03 Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape Horton, James S. Ali, Shani U. P. Taylor, Tiffany B. Philos Trans R Soc Lond B Biol Sci Articles 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’. The Royal Society 2023-05-22 2023-04-03 /pmc/articles/PMC10067260/ /pubmed/37004722 http://dx.doi.org/10.1098/rstb.2022.0043 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Horton, James S. Ali, Shani U. P. Taylor, Tiffany B. Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title | Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title_full | Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title_fullStr | Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title_full_unstemmed | Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title_short | Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
title_sort | transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067260/ https://www.ncbi.nlm.nih.gov/pubmed/37004722 http://dx.doi.org/10.1098/rstb.2022.0043 |
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