A statistical genomics framework to trace bacterial genomic predictors of clinical outcomes in Staphylococcus aureus bacteraemia

Outcomes for patients with severe bacterial infections are determined by the interplay between host, pathogen, and treatments. Most notably, patient age and antibiotic resistance contributes significantly to poor outcomes. While human genomics studies have provided insights into the host genetic factors impacting outcomes of Staphylococcus aureus infections, comparatively little is known about S. aureus genotypes and disease severity. Building on the idea that bacterial pathoadaptation is a key driver of clinical outcomes, we develop a new genome-wide association study (GWAS) framework to identify adaptive bacterial mutations associated with clinical treatment failure and mortality in three large and independent S. aureus bacteraemia cohorts, comprising 1358 episodes. We discovered S. aureus loci with previously undescribed convergent mutations linked to both poorer infection outcomes and reduced susceptibility to vancomycin. Our research highlights the potential of vancomycin-selected mutations and vancomycin MIC as key explanatory variables to predict SAB severity. The contribution of bacterial variation was much lower for clinical outcomes (heritability < 5%), however, GWAS allowed us to identify additional, MIC-independent candidate pathogenesis loci. Using supervised machine-learning, we were able to quantify the predictive potential of these adaptive S. aureus signatures, along with host determinants of bacteraemia outcomes. The statistical genomics framework we have developed is a powerful means to capture adaptive mutations and find bacterial factors that influence and predict severe infections. Our findings underscore the importance of systematically collected, rich clinical and microbiological data to understand bacterial mechanisms promoting treatment failure.