Environment determines evolutionary trajectory in a constrained phenotypic space
Abstract
Constraints on phenotypic variation limit the capacity of organisms to adapt to the multiple selection pressures encountered in natural environments. To better understand evolutionary dynamics in this context, we select Escherichia coli for faster migration through a porous environment, a process which depends on both motility and growth. We find that a trade-off between swimming speed and growth rate constrains the evolution of faster migration. Evolving faster migration in rich medium results in slow growth and fast swimming, while evolution in minimal medium results in fast growth and slow swimming. In each condition parallel genomic evolution drives adaptation through different mutations. We show that the trade-off is mediated by antagonistic pleiotropy through mutations that affect negative regulation. A model of the evolutionary process shows that the genetic capacity of an organism to vary traits can qualitatively depend on its environment, which in turn alters its evolutionary trajectory.
Data availability
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Sequencing data for motility selection experimentsPublicly available at Illinois Data Bank.
Article and author information
Author details
Funding
National Science Foundation (PHY 0822613)
- David T Fraebel
- Harry Mickalide
- Diane Schnitkey
- Jason Merritt
- Thomas E Kuhlman
- Seppe Kuehn
National Science Foundation (PHY 1430124)
- David T Fraebel
- Harry Mickalide
- Diane Schnitkey
- Jason Merritt
- Thomas E Kuhlman
- Seppe Kuehn
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Wenying Shou, Fred Hutchinson Cancer Research Center, United States
Version history
- Received: December 24, 2016
- Accepted: March 25, 2017
- Accepted Manuscript published: March 27, 2017 (version 1)
- Version of Record published: May 23, 2017 (version 2)
Copyright
© 2017, Fraebel et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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