Extending evolutionary forecasts across bacterial species

Improving evolutionary forecasting requires progressing from studying repeated evolution of a single genotype under identical conditions to formulating broad principles. These principles should enable predictions of how similar species will adapt to similar selective pressures. Evolve-and-resequence experiments with multiple species allow testing forecasts on different biological levels and elucidating the causes for failed predictions. Here, we show that forecasts for adaptation to static culture conditions can be extended to multiple species by testing previous predictions for <i>Pseudomonas syringae</i> and <i>Pseudomonas savastanoi</i>. In addition to sequence divergence, these species differ in their repertoire of biofilm regulatory genes and structural components. Consistent with predictions, both species repeatedly produced biofilm mutants with a wrinkly spreader phenotype. Predominantly, mutations occurred in the <i>wsp</i> operon, with less frequent promoter mutations near uncharacterized diguanylate cyclases. However, mutational patterns differed on the gene level, which was explained by a lack of conservation in relative fitness of mutants between more divergent species. The same mutation was the most frequent for both species suggesting that conserved mutation hotspots can increase parallel evolution. This study shows that evolutionary forecasts can be extended across species, but that differences in the genotype-phenotype-fitness map and mutational biases limit predictability on a detailed molecular level.

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Publication Details

Journal
Proceedings of the Royal Society B Biological Sciences
Published
2024-12-01
DOI
https://doi.org/10.1098/rspb.2024.2312
Citations
2
Primary Topic
Evolution and Genetic Dynamics
Type
article
Field-Weighted Citation Impact
1.10

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article

Extending evolutionary forecasts across bacterial species

Peter A. Lind, Jennifer T. Pentz, Aparna Biswas, Bassel Alsaed
2 citations
Proceedings of the Royal Society B Biological Sciences
Evolution and Genetic Dynamics
1.10
article

Extending evolutionary forecasts across bacterial species

Peter A. Lind, Jennifer T. Pentz, Aparna Biswas, Bassel Alsaed
article en
2 citations

Abstract

Improving evolutionary forecasting requires progressing from studying repeated evolution of a single genotype under identical conditions to formulating broad principles. These principles should enable predictions of how similar species will adapt to similar selective pressures. Evolve-and-resequence experiments with multiple species allow testing forecasts on different biological levels and elucidating the causes for failed predictions. Here, we show that forecasts for adaptation to static culture conditions can be extended to multiple species by testing previous predictions for <i>Pseudomonas syringae</i> and <i>Pseudomonas savastanoi</i>. In addition to sequence divergence, these species differ in their repertoire of biofilm regulatory genes and structural components. Consistent with predictions, both species repeatedly produced biofilm mutants with a wrinkly spreader phenotype. Predominantly, mutations occurred in the <i>wsp</i> operon, with less frequent promoter mutations near uncharacterized diguanylate cyclases. However, mutational patterns differed on the gene level, which was explained by a lack of conservation in relative fitness of mutants between more divergent species. The same mutation was the most frequent for both species suggesting that conserved mutation hotspots can increase parallel evolution. This study shows that evolutionary forecasts can be extended across species, but that differences in the genotype-phenotype-fitness map and mutational biases limit predictability on a detailed molecular level.

Proceedings of the Royal Society B Biological SciencesVol. 291(2036)
Los Alamos National Laboratory (US), Umeå University (SE)
Kempe Foundation, Carl Tryggers Stiftelse för Vetenskaplig Forskning, Vetenskapsrådet, Magnus Bergvalls Stiftelse, Kempestiftelserna, O. E. och Edla Johanssons Vetenskapliga Stiftelse, Los Alamos National Laboratory
Life in Land
Openalex Percentile: Top 18%
Evolution and Genetic Dynamics
1.10
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