Unsuspected clonal spread of Methicillin-resistant Staphylococcus aureus causing bloodstream infections in hospitalized adults detected using whole genome sequencing

Background Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. Methods We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in two hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared three genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. Results While the three alignment approaches detected similar results, they showed some variation. A pangenome-based alignment method was most consistent across MRSA clonal complexes. We identified nine unique clusters of closely-related BSI isolates. Most BSI were healthcare-associated and community-onset. Our logistic model showed that with 13 single nucleotide polymorphisms the likelihood that any two patients in a cluster overlapped in a hospital was 50 percent. Conclusions Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in two hospitals. Genomic clustering of these infections suggest that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis. Summary Multiple clusters of closely related MRSA bloodstream infections were identified using WGS in two hospitals using three bioinformatic workflows. Genomic epidemiology suggests that transmission resulted from a mix of community spread and healthcare exposures long before symptom onset.