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Theory predicts that well-adapted populations may evolve mechanisms to counteract the inevitable influx of deleterious mutations. While mutational robustness can be directly selected in the laboratory, evidence for its spontaneous evolution during general adaptation is mixed. Moreover, whether robustness evolves to include pleiotropic effects remains largely unexplored. Here, we studied the effects of point mutations in the RNA polymerase ofEscherichia coliover a 15,000-generation adaptive trajectory. Fitness effects of both beneficial and deleterious mutations were attenuated in fitter backgrounds. In contrast, pleiotropic effects became more severe and widespread with greater adaptation. These results show that trade-offs between robustness and fragility can evolve in regulatory networks, regardless of whether driven by adaptive or nonadaptive processes. More broadly, they illustrate how adaptation can generate hidden variability, with unpredictable evolutionary consequences in new environments.
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Relaxed selection can speed the evolution of complex adaptations
Abstract Natural selection drives adaptive evolution and removes deleterious mutations; these effects are countervailing when a complex adaptation requires mutations that are initially deleterious when they arise, but beneficial in combination. While many models of this dynamic consider how genetic drift or other influences can aid valley crossing by weakening selection, we lack a general, analytical treatment of when relaxed selection might speed this type of adaptation. Here we use simulation and analysis to show that relaxed selection is generally favorable for valley-crossing when adaptive pathways require more than a single deleterious step. We also demonstrate that spatial heterogeneity in selection pressures could, by relaxing selection, allow populations to cross valleys much more rapidly than expected. These results relate to several applications of evolutionary theory to complex systems ranging from host-pathogen evolution to search algorithms in computer science.
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- Award ID(s):
- 2147101
- PAR ID:
- 10574480
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.1101/2024.07.09.602773
