ArticleGenetics2018
Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments.
Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 35 citations in OpenAlex.
- Article
- Epistasis decreases with increasing antibiotic pressure but not temperature.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- On the incongruence of genotype-phenotype and fitness landscapes.PLoS computational biology · 2022Article
- The adaptive landscape of a metallo-enzyme is shaped by environment-dependent epistasis.Nature communications · 2021Article
- The Macroevolutionary Consequences of Niche Construction in Microbial Metabolism.Frontiers in microbiology · 2021Article
- Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping.PLoS biology · 2020Article
- Article
- Analysis of statistical correlations between properties of adaptive walks in fitness landscapes.Royal Society open science · 2020Article
- Mathematical modeling of movement on fitness landscapes.BMC systems biology · 2019Article
- Recombination drives the evolution of mutational robustness.Current opinion in systems biology · 2019Article
- Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.