iScienceGenomicsPeer-reviewed journal article
Genomic analysis of Staphylococcus aureus isolates from bacteremia reveals genetic features associated with the COVID-19 pandemic
Miquel Sánchez−OsunaiD, Marc PedrosaiD, Paula BiergeiD, Inmaculada Gómez-SáncheziD, Marina Alguacil-GuilléniD, Mateu EspasaiD, Ivan ErilliD, Oriol GaschiD, Òscar Q. PichiD
Abstract
Genomic analyses of bacterial isolates are necessary to monitor the prevalence of antibiotic resistance genes and virulence determinants. Herein, we provide a comprehensive genomic description of a collection of 339 Staphylococcus aureus strains isolated from patients with bacteremia between 2014 and 2022. Nosocomial acquisition accounted for 56.6% of episodes, with vascular catheters being the predominant source of infection (31.8%). Cases of fatality (27.4%), persistent bacteremia (19.5%) and diagnosis of septic emboli (24.2%) were documented. During the COVID-19 pandemic, we observed a 140% increase of the episodes of S. aureus bacteremia per year, with a concomitant increase of the cases from nosocomial origin. This prompted us to investigate the existence of genetic features associated with S. aureus isolates from the COVID-19 pandemic. While genes conferring resistance to {beta}-lactams (blaI-blaR-blaZ), macrolides (ermA, ermC, ermT, mphC, msrA) and aminoglycosides (ant(4)-Ia, ant(9)-Ia, aph(3)-IIIa, aph(2)-Ih) were prevalent in our collection, detection of the msrA and mphC genes increased significantly in pandemic S. aureus isolates. Similarly, we observed a higher prevalence of isolates carrying the genes encoding the Clumping Factors A and B, involved in fibrinogen binding. Of note, macrolides were extensively used as accessory therapy for COVID-19 and fibrinogen levels were usually elevated upon SARS-CoV-2 infection. Therefore, our results reveal a remarkable adaptation of the S. aureus isolates to the COVID-19 pandemic context and demonstrates the potential of whole-genome sequencing to conduct molecular epidemiology studies.
- assay: whole-genome sequencing
- research focus: genome assembly
- research focus: variant calling
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 11
- FWCI
- 3.81
- Field percentile
- top 6%
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