The bacterium Staphylococcus aureus is a major human pathogen for which the emergence of antibiotic resistance is a global public health concern. Infection severity, and in particular bacteraemia-associated mortality, has been attributed to several host-related factors, such as age and the presence of comorbidities. The role of the bacterium in infection severity is less well understood, as it is complicated by the multifaceted nature of bacterial virulence, which has so far prevented a robust mapping between genotype, phenotype and infection outcome. To investigate the role of bacterial factors in contributing to bacteraemia-associated mortality, we phenotyped a collection of sequenced clinical S. aureus isolates from patients with bloodstream infections, representing two globally important clonal types, CC22 and CC30. By adopting a genome-wide association study approach we identified and functionally verified several genetic loci that affect the expression of cytolytic toxicity and biofilm formation. By analysing the pooled data comprising bacterial genotype and phenotype together with clinical metadata within a machine-learning framework, we found significant clonal differences in the determinants most predictive of poor infection outcome. Whereas elevated cytolytic toxicity in combination with low levels of biofilm formation was predictive of an increased risk of mortality in infections by strains of a CC22 background, these virulence-specific factors had little influence on mortality rates associated with CC30 infections. Our results therefore suggest that different clones may have adopted different strategies to overcome host responses and cause severe pathology. Our study further demonstrates the use of a combined genomics and data analytic approach to enhance our understanding of bacterial pathogenesis at the individual level, which will be an important step towards personalized medicine and infectious disease management.
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The authors thank T. Gouliouris, for his role in collecting and supplying the clinical data, and E. Nickerson and S. Aliyu for their role in the collection of clinical information. The authors also thank the Sanger Institute’s core Pathogen Production Groups and Pathogen Informatics Group.
M.R., M.L., M.S.T., R.B.S., B.B., M.E.T., S.J.P. and R.C.M. collected the data. M.R., M.L. and R.C.M. designed the experiments. M.L., K.O., E.S., M.Y., L.B., J.S., L.T. and G.M. performed experiments. M.R., M.L., M.S.T., S.R., S.B., S.J.P. and R.C.M. analysed the data. M.R., M.L., S.J.P. and R.C.M. wrote the manuscript.
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Epistasis analysis uncovers hidden antibiotic resistance-associated fitness costs hampering the evolution of MRSA
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