Biology subjects

McInerney, J.

Publications and source records attributed to McInerney, J..

5 recordsLinked to original sources

bioRxiv2026

Prokaryotic Pangenomes Are Bet-Hedging Devices

Prokaryotic pangenomes consist of distributed gene pools that far exceed any individual genome. Though universal across prokaryotic life, a satisfactory evolutionary explanation is lacking. Proposed frameworks describe how genes accumulate but not why this architecture persists. Here I show that pangenomes are bet-hedging devices: strategies that buffer environmental uncertainty at the population level. Because fitness compounds multiplicatively, the geometric mean determines long-term success, creating selection for distributed genetic portfolios. Two thresholds emerge from first principles: a selection direction threshold p* = c/(s + c) determining which genes selection favours, and a complexity threshold Ecrit = s/c above which no single genome can afford full environmental coverage. The framework dissolves the core-accessory distinction: both obey the same dynamics, differing only in whether beneficial conditions are constant or intermittent. Predictions accord with empirical observations, including that environment dominates phylogeny in explaining pangenome variation, rare genes persist far longer than neutral drift predicts, and genes appearing neutral under arithmetic fitness models show signatures consistent with variance-reducing strategies. From p* and Ecrit, the full phenomenology follows, including the U-shaped distribution, the complexity scaling, the bounded genomes, the rare-gene persistence. No patchwork of mechanisms is required. Analysis of the Escherichia coli pangenome provides direct empirical support: niche-specific genes retain 63% of their home-niche frequency in away environments, gene-environment coupling matches bet-hedging predictions rather than migration-selection balance, and the observed strategy performs within 4% of pure bet-hedging across all environmental switching rates. The pangenome is a unified, obligate solution to prokaryotic life under uncertainty.

evolutionary biology↗

bioRxiv2023

Fungal phylogeny reconstructed using heterogeneous models reveals new placement of Microsporidia

Fungi have significant impacts on global ecosystems, playing roles in organic matter decomposition, as disease agents, as well as having both positive and negative economic effects. Fungal evolutionary history remains partly unresolved with the presence on many incertae sedis clades, lacking a robust placement on the fungal phylogenetic tree. Some of the more problematic groups whose placement remains unclear are the intracellular parasites Microsporidia and Cryptomycota, both of whom have accelerated rates of nucleotide substitution and reduced genomes, which makes the resolution of these groups hard. In this project we used tree and data heterogeneous models, that can account for different substitution rates between different protein families as well as different parts of the phylogenetic tree, to reconstruct the phylogeny. We recovered a well-supported topology for much of the fungal phylogeny as well as a robust placement for both Microsporidia and Cryptomycota, the former being rooted deeply within the fungi and the latter a placement on a sister branch to the fungi.

evolutionary biology↗

bioRxiv2023

Contingency, Repeatability and Predictability in the Evolution of a Prokaryotic Pangenome.

Pangenomes exhibit remarkable variability in many prokaryotic species. This variation 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 background of the recipients mean that post-acquisition 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 the presence of other accessory genes within the genome. We are, in effect, asking whether gene acquisitions potentiate similar evolutionary trajectories or not. Our analysis leverages the repeated transfer of genes through the E. coli pangenome to observe patterns of repeated evolution following similar events. 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 despite differences in host evolutionary history. We propose that the pangenome can be understood as a set of genes with relationships that govern their likely cohabitants, analogous to an ecosystems set of interacting organisms. Our findings highlight intra-genomic gene fitness effects as key drivers of prokaryotic evolution, with ensuing pangenome-wide emergence of repeated patterns of community structure.

evolutionary biology↗

bioRxiv2022

Pangenome evolution in Escherichia coli is sequence type, not phylogroup, specific

The Escherichia coli species contains a diverse set of sequence types and there remain important questions regarding differences in genetic content within this population that need to be addressed. Pangenomes are useful vehicles for studying gene content within sequence types. Here, we analyse 21 E. coli sequence type pangenomes using comparative pangenomics to identify variance in both pangenome structure and content. We present functional breakdowns of sequence type core genomes and identify sequence types that are enriched in metabolism, transcription and cell membrane biogenesis genes. We also uncover metabolism genes that have variable core classification depending on which allele is present. Our comparative pangenomics approach allows for detailed exploration of sequence type pangenomes within the context of the species. We show that pangenome evolution is independent of phylogenetic signal at the phylogroup level, which may be a consequence of distinct sequence type-specific driving factors relating to ecology and pathogenic phenotype. Data SummarySupporting data and code have been provided within the article or through Supplementary Data files available at https://doi.org/10.6084/m9.figshare.19793758. Custom Python scripts used to perform analyses are available at github.com/lillycummins/InterPangenome unless otherwise stated in the text.

microbiology↗

bioRxiv2019

Selection-based model of prokaryote pangenomes

The genomes of different individuals of the same prokaryote species can vary widely in gene content, displaying different proportions of core genes, which are present in all genomes, and accessory genes, whose presence varies between genomes. Together, these core and accessory genes make up a species pangenome. The reasons behind this extensive diversity in gene content remain elusive, and there is an ongoing debate about the contribution of accessory genes to fitness, that is, whether their presence is on average advantageous, neutral, or deleterious. In order to explore this issue, we developed a mathematical model to simulate the gene content of prokaryote genomes and pangenomes. Our model focuses on testing how the fitness effects of genes and their rates of gene gain and loss would affect the properties of pangenomes. We first show that pangenomes with large numbers of low-frequency genes can arise due to the gain and loss of neutral and nearly neutral genes in a population. However, pangenomes with large numbers of highly beneficial, low-frequency genes can arise as a consequence of genotype-by-environment interactions when multiple niches are available to a species. Finally, pangenomes can arise, irrespective of the fitness effect of the gained and lost genes, as long as gene gain and loss rates are high. We argue that in order to understand the contribution of different mechanisms to pangenome diversity, it is crucial to have empirical information on population structure, gene-by-environment interactions, the distributions of fitness effects and rates of gene gain and loss in different prokaryote groups.

microbiology↗