Dynamics and variability in the pleiotropic effects of adaptation in laboratory budding yeast populations
Abstract
Evolutionary adaptation to a constant environment is driven by the accumulation of mutations which can have a range of unrealized pleiotropic effects in other environments. These pleiotropic consequences of adaptation can influence the emergence of specialists or generalists, and are critical for evolution in temporally or spatially fluctuating environments. While many experiments have examined the pleiotropic effects of adaptation at a snapshot in time, very few have observed the dynamics by which these effects emerge and evolve. Here, we propagated hundreds of diploid and haploid laboratory budding yeast populations in each of three environments, and then assayed their fitness in multiple environments over 1000 generations of evolution. We find that replicate populations evolved in the same condition share common patterns of pleiotropic effects across other environments, which emerge within the first several hundred generations of evolution. However, we also find dynamic and environment-specific variability within these trends: variability in pleiotropic effects tends to increase over time, with the extent of variability depending on the evolution environment. These results suggest shifting and overlapping contributions of chance and contingency to the pleiotropic effects of adaptation, which could influence evolutionary trajectories in complex environments that fluctuate across space and time.
Data availability
Raw amplicon sequencing reads have been deposited in the NCBI BioProject database with accession number PRJNA739738. Source data files are listed in appropriate figure legends. Analysis code is available at https://github.com/amphilli/pleiotropy-dynamics.
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Raw sequence readsNCBI BioProject, PRJNA739738.
Article and author information
Author details
Funding
National Defense Science and Engineering Graduate
- Christopher W Bakerlee
National Institutes of Health (GM007598)
- Christopher W Bakerlee
Howard Hughes Medical Institute (Hanna H. Gray Postdoctoral Fellowship)
- Angela M Phillips
National Science Foundation (PHY-1914916)
- Michael M Desai
National Institutes of Health (GM104239)
- Michael M Desai
Harvard University (FAS Division of Science Research Computing Group Cannon cluster)
- Michael M Desai
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Bakerlee 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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