Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome

Beavan, Alan; Domingo-Sananes, Maria Rosa; McInerney, James O.

Abstract

Pangenomes exhibit remarkable variability in many prokaryotic species, much of which is maintained through the processes of horizontal gene transfer and gene loss. Repeated acquisitions of near-identical homologs can easily be observed across pangenomes, leading to the question of whether these parallel events potentiate similar evolutionary trajectories, or whether the remarkably different genetic backgrounds of the recipients mean that postacquisition evolutionary trajectories end up being quite different. In this study, we present a machine learning method that predicts the presence or absence of genes in the Escherichia coli pangenome based on complex patterns of the presence or absence of other accessory genes within a genome. Our analysis leverages the repeated transfer of genes through the E. coli pangenome to observe patterns of repeated evolution following similar events. We find that the presence or absence of a substantial set of genes is highly predictable from other genes alone, indicating that selection potentiates and maintains gene–gene co-occurrence and avoidance relationships deterministically over long-term bacterial evolution and is robust to differences in host evolutionary history. We propose that at least part of the pangenome can be understood as a set of genes with relationships that govern their likely cohabitants, analogous to an ecosystem’s set of interacting organisms. Our findings indicate that intragenomic gene fitness effects may be key drivers of prokaryotic evolution, influencing the repeated emergence of complex gene–gene relationships across the pangenome.

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2023
• Online Publication Date: Dec 26, 2023
• Publication Date: Jan 2, 2024
• Deposit Date: Dec 30, 2023
• Publicly Available Date: Jan 2, 2024
• Journal: Proceedings of the National Academy of Sciences
• Print ISSN: 0027-8424
• Electronic ISSN: 1091-6490
• Publisher: National Academy of Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 121
• Issue: 1
• Article Number: e2304934120
• DOI: https://doi.org/10.1073/pnas.2304934120
• Keywords: Pangenomes; machine learning; evolution
• Public URL: https://nottingham-repository.worktribe.com/output/29001452
• Additional Information: Received: 2023-03-27; Accepted: 2023-11-05; Published: 2023-12-26 Copyright © 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).