Machine learning analysis of type VII secretion system expression and its relationships with virulence traits and antibiotic resistance in Staphylococcus aureus

Abstract Even after decades of scrutiny, Staphylococcus aureus refuses to yield all its secrets. At the heart of its virulence lies the type VII secretion system (T7SS)—a molecular weapon whose alliances with other pathogenic traits remain largely uncharted. Here, we combined wet-lab microbiology with machine learning to dissect the networks linking T7SS to enzymatic virulence factors, antibiotic resistance, and environmental triggers. We quantified associations with DNase, hemolysin, protease, lipase, staphyloxanthin and assessed modulation by physical (ultraviolet light), chemical (disinfectant), and biological (coculture with Escherichia coli) factors. Among 150 clinical S. aureus isolates, 74% were methicillin-resistant, 0.6% vancomycin-resistant, and multidrug resistance observed in 88%. T7SS expression showed positive correlations with virulence factors but no association with MRSA status or antibiotic resistance. Machine learning analyses identified protease as the strongest predictor of T7SS expression and revealed clustering of high-expression phenotypes, highlighting complex interdependencies often overlooked by conventional approaches. Environmental factors significantly influenced T7SS expression: UV light downregulated it 1.5-fold, while sodium hypochlorite and E. coli coculture upregulated it by 2.5-fold and 2.2-fold, respectively. The positive correlation between T7SS and other virulence determinants suggests regulation by a shared accessory gene regulator (agr) system, though T7SS appears to act independently of resistance traits. Future research should investigate whether agr inhibitors can suppress T7SS and mitigate infection severity.