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Staphylococcus aureus remains a leading cause of hospital-acquired and community-associated infection worldwide. The burden of disease is exacerbated by the emergence of virulent strains with reduced susceptibility to commonly used antibiotics and their dissemination in healthcare settings and in the community. Whole genome sequencing (WGS) has the potential to revolutionize our understanding and management of S. aureus infection. As a research tool, WGS has provided insights into the origins of antibiotic-resistant strains, the genetic basis of virulence, the emergence and spread of lineages, and the population structure of S. aureus. As a frontline tool, WGS offers the prospect of a method that could be used to predict resistance, assess virulence, and type isolates at the highest possible resolution. The results generated could be used to guide clinical management and infection control practice. Studies using bench-top sequencing machines have already demonstrated the feasibility of such approaches. Infection control management is compromised by our incomplete understanding of transmission, which in turn reflects the suboptimal resolution offered by conventional typing methods. As the costs of sequencing begin to approach those of conventional methods, high-resolution typing with WGS could realistically be implemented for hospital infection control, as well as for local and national surveillance practice. Translation into routine practice will require the development of a knowledge base, reliable automated bioinformatic tools, the capacity to store, exchange and interrogate large volumes of genomic data, and an acceptance of WGS by clinicians, infection control specialists, and laboratory staff.

Original publication




Journal article


Clin Microbiol Infect

Publication Date





784 - 789


MRSA, Staphylococcus aureus, transmission, virulence, whole genome sequencing, Bacterial Typing Techniques, Cross Infection, Disease Outbreaks, Drug Resistance, Bacterial, Genome, Bacterial, Humans, Infection Control, Metagenomics, Molecular Epidemiology, Sequence Analysis, DNA, Staphylococcal Infections, Staphylococcus aureus, Virulence