eLifeGenomicsPeer-reviewed journal article
Niche-specific genome degradation and convergent evolution shaping Staphylococcus aureus adaptation during severe infections
Stefano G. GiulieriiD, Romain GuérillotiD, Sebastián DuchêneiD, Abderrahman HachaniiD, Diane S. Daniel, Torsten SeemanniD, Joshua Saul DavisiD, Steven Y. C. TongiD, Bernadette YoungiD, Daniel J. WilsoniD, Timothy Paul StineariD, Benjamin P. HowdeniD
Abstract
During severe infections, Staphylococcus aureus moves from its colonising sites to blood and tissues, and is exposed to new selective pressures, thus potentially driving adaptive evolution. Previous studies have shown the key role of the agr locus in S. aureus pathoadaptation, however a more comprehensive characterisation of genetic signatures of bacterial adaptation may enable prediction of clinical outcomes and reveal new targets for treatment and prevention of these infections. Here, we measured adaptation using within-host evolution analysis of 2,590 S. aureus genomes from 396 independent episodes of infection. By capturing a comprehensive repertoire of single-nucleotide and structural genome variations, we found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. These invasive strains had up to 20-fold enrichments for genome degradation signatures and displayed significantly convergent mutations in a distinctive set of genes, linked to antibiotic response and pathogenesis. In addition to agr-mediated adaptation we identified non-canonical, genome-wide significant loci including sucA-sucB and stp1. The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform correlation of infection outcomes with adaptation signatures.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 54
- FWCI
- 4.88
- Field percentile
- top 3%
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