Pathoadapative Genomic Determinants of Staphylococcus aureus Community Skin Infections and Nasal Colonization

<i>Staphylococcus aureus</i> is a leading cause of skin and soft tissue infections (SSTIs), yet the bacterial genomic adaptations underlying the transition from nasal colonization to invasive infection remain incompletely defined. We sequenced and analyzed 157 <i>S. aureus</i> isolates (126 from SSTIs and 31 from asymptomatic nasal colonization) from a primary care network in South Texas. Using genome-wide association studies, non-synonymous single-nucleotide variant (NSNV) profiling, and machine learning, we identified strain-specific adaptations in metabolic and regulatory pathways. SSTI isolates exhibited significant enrichment of nitrogen assimilation, purine biosynthesis, menaquinone production, and anaerobic respiration genes. Elevated copy number and colocalization of phage-linked metabolic genes-including <i>nirB</i>, <i>narH</i>, and <i>nifR3</i>-suggest a pathoadaptive genomic island supporting infection-specific energy generation. The enrichment of α/β-hydrolase domain-encoding genes was associated with clinical severity. To quantify severity, we developed the Purulent Ulcer Skin (PUS) score, which integrates wound size, drainage, and erythema. The α/β-hydrolase and lipoprotein genes were significantly associated with higher PUS scores (higher SSTI severity) and phage-encoded virulence gene products were linked to larger wound size. Machine learning prioritized <i>purL</i> and other metabolic loci as key infection classifiers. NSNVs and unitig-level changes co-localized within nutrient transport, stress resistance, and cytolytic genes, supporting a model of multi-layered genomic selection. Metagenomic assemblies of nasal microbiota were enriched for <i>Staphylococcus</i>, <i>Enterococcus</i>, and <i>Micrococcus</i> species, core metabolic pathways, and taxon-specific virulence determinants. This underscores the roles of metabolic and virulent co-networks within nasal commensals and their adaptive capacity for pathogenic transition. These findings provide a potential genomic blueprint of <i>S. aureus</i> pathoadaptation during SSTI and are a step towards the development of novel therapeutic targets.

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Journal
Microorganisms
Published
2025-08-29
DOI
https://doi.org/10.3390/microorganisms13092023
Primary Topic
Antimicrobial Resistance in Staphylococcus
Type
article
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article

Pathoadapative Genomic Determinants of Staphylococcus aureus Community Skin Infections and Nasal Colonization

Sarah M. Bandy, Steven S. Dallas, Cody A. Black, Raymond Benavides et al.
Microorganisms
Antimicrobial Resistance in Staphylococcus
article

Pathoadapative Genomic Determinants of Staphylococcus aureus Community Skin Infections and Nasal Colonization

Sarah M. Bandy, Steven S. Dallas, Cody A. Black, Raymond Benavides, Christopher R. Frei, Julianne Mercer, Wonhee So, Justina Lipscomb, James F. Shurko, Adriana Vargus, Grace C. Lee, Benjamin A. Encino
article en

Abstract

<i>Staphylococcus aureus</i> is a leading cause of skin and soft tissue infections (SSTIs), yet the bacterial genomic adaptations underlying the transition from nasal colonization to invasive infection remain incompletely defined. We sequenced and analyzed 157 <i>S. aureus</i> isolates (126 from SSTIs and 31 from asymptomatic nasal colonization) from a primary care network in South Texas. Using genome-wide association studies, non-synonymous single-nucleotide variant (NSNV) profiling, and machine learning, we identified strain-specific adaptations in metabolic and regulatory pathways. SSTI isolates exhibited significant enrichment of nitrogen assimilation, purine biosynthesis, menaquinone production, and anaerobic respiration genes. Elevated copy number and colocalization of phage-linked metabolic genes-including <i>nirB</i>, <i>narH</i>, and <i>nifR3</i>-suggest a pathoadaptive genomic island supporting infection-specific energy generation. The enrichment of α/β-hydrolase domain-encoding genes was associated with clinical severity. To quantify severity, we developed the Purulent Ulcer Skin (PUS) score, which integrates wound size, drainage, and erythema. The α/β-hydrolase and lipoprotein genes were significantly associated with higher PUS scores (higher SSTI severity) and phage-encoded virulence gene products were linked to larger wound size. Machine learning prioritized <i>purL</i> and other metabolic loci as key infection classifiers. NSNVs and unitig-level changes co-localized within nutrient transport, stress resistance, and cytolytic genes, supporting a model of multi-layered genomic selection. Metagenomic assemblies of nasal microbiota were enriched for <i>Staphylococcus</i>, <i>Enterococcus</i>, and <i>Micrococcus</i> species, core metabolic pathways, and taxon-specific virulence determinants. This underscores the roles of metabolic and virulent co-networks within nasal commensals and their adaptive capacity for pathogenic transition. These findings provide a potential genomic blueprint of <i>S. aureus</i> pathoadaptation during SSTI and are a step towards the development of novel therapeutic targets.

MicroorganismsVol. 13(9)
The University of Texas at San Antonio Health Science Center (US), University Health System (US), South Texas Veterans Health Care System (US), The University of Texas at San Antonio (US), The University of Texas at Austin (US), Western University of Health Sciences (US)
Openalex Percentile: Top 73%
Antimicrobial Resistance in Staphylococcus
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