Dicks, J. | bioRxiv Science

bioRxiv2026

Machine learning approaches for the identification and analysis of enterotoxin genes in Staphylococcus aureus genomes

2.Staphylococcus aureus produces a broad range of enterotoxins that act as superantigens, disrupting host immune responses and resulting in a myriad of clinical symptoms. However, large-scale analyses determining enterotoxin gene diversity, lineage structure and isolate metadata remain scarce. We analysed 15,887 S. aureus RefSeq genomes using a machine learning pipeline combining profile Hidden Markov Model-based enterotoxin gene identification, lineage typing, gene profile-based strain clustering and association rule mining using a broad range of gene and metadata features. This approach identified 35 distinct enterotoxin genes and five variant forms, including two putative novel enterotoxin genes, sel34 and sel35. HDBSCAN clustering distinguished 45 enterotoxin gene profile groups, revealing strong associations between the two major egc enterotoxin gene cluster variants (OMIWNG and OMIUNG) and Clonal Complex membership: CC5, CC22 and CC45 with OMIWNG; CC30 and CC121 with OMIUNG. Integration of isolate metadata exposed distinct geographic and temporal trends, including a recent rise in non-egc lineages derived from Asia and animal sources. These findings show that S. aureus enterotoxin diversity is structured by lineage, mobile genetic element composition and Clonal Complex association. The discovery of sel34 and sel35, together with the comprehensive overview of lineage-specific enterotoxin profiles, expands current understanding of S. aureus virulence evolution and provides a scalable analytical framework for monitoring toxin gene dynamics in clinical and environmental populations. 3. Impact StatementUnderstanding how virulence genes evolve and spread in Staphylococcus aureus is vital for predicting pathological potential and managing infection risk for this species. By analysis of over 15,000 publicly available S. aureus genomes, this study provides the most comprehensive overview to date of enterotoxin gene diversity and lineage structure. Using machine learning and large-scale genomic mining, we reveal clear evolutionary and epidemiological patterns linking enterotoxin gene clusters to specific Clonal Complexes and identify two previously unknown enterotoxin genes. These findings highlight how recombination and horizontal gene transfer shape S. aureus toxins across hosts, continents and time. The resulting analytical framework offers a scalable foundation for future genomic surveillance of virulence evolution in both clinical and environmental settings. 4. Data SummaryGenome assemblies and associated metadata for the 15,887 Staphylococcus aureus strains analysed within this study were generated elsewhere prior to this study and were here downloaded from the NCBI RefSeq database. Secondary datasets and descriptions of algorithmic approaches used to develop them are presented in the main text and within the Supplementary Files 1 and 2.

bioinformatics↗

bioRxiv2024

Pre and Post antibiotic epoch: insights into the historical spread of antimicrobial resistance

Plasmids are now the primary vectors of antimicrobial resistance, but our understanding of how human industrialisation of antibiotics influenced this is limited by a paucity of data predating the antibiotic era (PAE). By investigating plasmids from clinically relevant bacteria isolated between 1917 and 1954 and comparing them to modern plasmids, we captured over 100 years of evolution. We show that while all PAE plasmids were devoid of resistance genes and most never acquired them, a small minority evolved to drive the global spread of resistance to first-line and last-resort antibiotics in Gram-negative bacteria. They have evolved through complex microevolution and fusion events into a distinct group of highly recombinogenic, multi-replicon, self-transmissible plasmids that now pose the highest risk to resistance dissemination, and therefore human health. One Sentence SummaryPre-antibiotic era bacteria reveal the origin and evolution of drug-resistance vectors.

genomics↗

bioRxiv2018

Diverse lineages of Candida albicans live on old oaks

The human pathogen, Candida albicans, is considered an obligate commensal of animals, yet it is occasionally isolated from trees, shrubs and grass. We generated deep genome sequence data for three strains of C. albicans that we isolated from oak trees in an ancient wood-pasture, and compared these to the genomes of the type strain and 21 other clinical strains. C. albicans strains from oak are similar to clinical C. albicans in that they are predominantly diploid and can become naturally homozygous at the mating locus through whole-chromosome loss of heterozygosity (LOH). LOH regions in all genomes arose recently suggesting that LOH mutations usually occur transiently in C. albicans populations. Oak strains differed from clinical strains in showing less LOH, and higher levels of heterozygosity genome-wide. Using phylogenomic analyses, in silico chromosome painting, and comparisons with thousands more C. albicans strains at seven loci, we show that each oak strain is more closely related to strains from humans and other animals than to strains from other oaks. Therefore, the isolation of C. albicans from oak is not easily explained as contamination from a single animal source. The high heterozygosity of oak strains could arise as a result of reduced mitotic recombination in asexual lineages, recent parasexual reproduction or because of natural selection. Regardless of mechanism, the diversity of C. albicans on oaks implies that they have lived in this environment long enough for genetic differences from clinical strains to arise.

genomics↗