Combining Genome Surveillance and Metadata To Characterize the Diversity of Staphylococcus aureus Circulating in an Italian Hospital over a 9-Year Period

Genome sequencing is an important tool in clinical microbiology, as it allows in-depth characterization of isolates of interest and can propel genome-based surveillance studies. Such studies can benefit from ad hoc methods of sample selection to capture the genomic diversity present in a data set.Staphylococcus aureus is an opportunistic pathogen and a leading cause of morbidity and mortality worldwide. Genomic-based surveillance has greatly improved our ability to track the emergence and spread of high-risk clones, but the full potential of genomic data is only reached when used in conjunction with detailed metadata. Here, we demonstrate the utility of an integrated approach by leveraging a curated collection of clinical and epidemiological metadata of S. aureus in the San Matteo Hospital (Italy) through a semisupervised clustering strategy. We sequenced 226 sepsis S. aureus samples, recovered over a period of 9 years. By using existing antibiotic profiling data, we selected strains that capture the full diversity of the population. Genome analysis revealed 49 sequence types, 16 of which are novel. Comparative genomic analyses of hospital- and community-acquired infection ruled out the existence of genomic features differentiating them, while evolutionary analyses of genes and traits of interest highlighted different dynamics of acquisition and loss between antibiotic resistance and virulence genes. Finally, highly resistant clones belonging to clonal complexes (CC) 8 and 22 were found to be responsible for abundant infections and deaths, while the highly virulent CC30 was responsible for rare but deadly episodes of infections.IMPORTANCE Genome sequencing is an important tool in clinical microbiology, as it allows in-depth characterization of isolates of interest and can propel genome-based surveillance studies. Such studies can benefit from ad hoc methods of sample selection to capture the genomic diversity present in a data set. Here, we present an approach based on clustering of antibiotic resistance profiles that allows optimal sample selection for bacterial genomic surveillance. We apply the method to a 9-year collection of Staphylococcus aureus from a large hospital in northern Italy. Our method allows us to sequence the genomes of a large variety of strains of this important pathogen, which we then leverage to characterize the epidemiology in the hospital and to perform evolutionary analyses on genes and traits of interest. These analyses highlight different dynamics of acquisition and loss between antibiotic resistance and virulence genes.