bioRxivEvolutionary Biology
Predicting mutational routes to new adaptive phenotypes
Abstract
Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive \"wrinkly spreader\" (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.\n\nImpact statementA combination of genetics, experimental evolution and mathematical modelling defines information necessary to predict the outcome of short-term adaptive evolution.
- organism: E. coli
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas protegens
J. T. Pentz, P. A. Lind·bioRxiv·Mar 4, 2021
- Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas syringae and Pseudomonas savastanoi
J. T. Pentz, A. Biswas, … P. A. Lind·bioRxiv·Feb 10, 2024
- Predictability shifts from local to global rules during bacterial adaptation
Alejandro Couce, M. Magnan, … O. Tenaillon·bioRxiv·May 17, 2022
- Mutational hotspots lead to robust but suboptimal adaptive outcomes in certain environments
L. M. Flanagan, J. S. Horton, T. B. Taylor·bioRxiv·Jun 7, 2023
- Distribution of mutation rates challenges evolutionary predictability
T. A. Sun, P. A. Lind·bioRxiv·Dec 12, 2022
- Mutation bias is a key predictor of adaptation rate
Shazia Parveen, Aakanksha Madhwal, … Deepa Agashe·bioRxiv·Sep 22, 2025