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Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditio...

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Autores principales: Taylor, Mark A., Wilczek, Amity M., Roe, Judith L., Welch, Stephen M., Runcie, Daniel E., Cooper, Martha D., Schmitt, Johanna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731683/
https://www.ncbi.nlm.nih.gov/pubmed/31420516
http://dx.doi.org/10.1073/pnas.1902731116
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author Taylor, Mark A.
Wilczek, Amity M.
Roe, Judith L.
Welch, Stephen M.
Runcie, Daniel E.
Cooper, Martha D.
Schmitt, Johanna
author_facet Taylor, Mark A.
Wilczek, Amity M.
Roe, Judith L.
Welch, Stephen M.
Runcie, Daniel E.
Cooper, Martha D.
Schmitt, Johanna
author_sort Taylor, Mark A.
collection PubMed
description Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments.
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spelling pubmed-67316832019-09-18 Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana Taylor, Mark A. Wilczek, Amity M. Roe, Judith L. Welch, Stephen M. Runcie, Daniel E. Cooper, Martha D. Schmitt, Johanna Proc Natl Acad Sci U S A Biological Sciences Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments. National Academy of Sciences 2019-09-03 2019-08-16 /pmc/articles/PMC6731683/ /pubmed/31420516 http://dx.doi.org/10.1073/pnas.1902731116 Text en Copyright © 2019 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Taylor, Mark A.
Wilczek, Amity M.
Roe, Judith L.
Welch, Stephen M.
Runcie, Daniel E.
Cooper, Martha D.
Schmitt, Johanna
Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title_full Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title_fullStr Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title_full_unstemmed Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title_short Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana
title_sort large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in arabidopsis thaliana
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6731683/
https://www.ncbi.nlm.nih.gov/pubmed/31420516
http://dx.doi.org/10.1073/pnas.1902731116
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