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Updated: Jul 7, 2025
08:03
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
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Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome
Alan J S Beavan1, Maria Rosa Domingo-Sananes1,2, James O McInerney1
1School of Life Sciences, The University of Nottingham, Nottingham NG7 2UH, United Kingdom.
Summary
Machine learning predicts gene presence in Escherichia coli pangenomes. Gene co-occurrence patterns reveal deterministic selection, suggesting intragenomic gene fitness drives prokaryotic evolution.
Area of Science:
- Microbial genomics
- Evolutionary biology
- Bioinformatics
Background:
- Prokaryotic pangenomes show high variability due to horizontal gene transfer and gene loss.
- Repeated gene acquisitions raise questions about parallel evolutionary trajectories versus host genetic background influence.
Purpose of the Study:
- To develop a machine learning method for predicting gene presence/absence in the Escherichia coli pangenome.
- To investigate if selection maintains deterministic gene-gene co-occurrence and avoidance relationships.
Main Methods:
- Utilized a machine learning approach to analyze gene presence/absence patterns.
- Leveraged repeated gene transfer events in the E. coli pangenome to observe evolutionary patterns.
Main Results:
- Gene presence/absence is highly predictable based on other accessory genes.
- Selection deterministically maintains gene-gene relationships, robust to host evolutionary history.
Conclusions:
- A significant portion of the pangenome can be viewed as interacting gene sets, similar to ecological communities.
- Intragenomic gene fitness effects are likely key drivers of prokaryotic evolution and gene relationship emergence.
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