Prosthetic Joint Infection Caused by Staphylococcus argenteus: mNGS-Guided Diagnosis and Whole-Genome...

Staphylococcus argenteus, a member of the Staphylococcus aureus complex, has increasingly been recognized as a human pathogen but is frequently misidentified as S. aureus in routine clinical laboratories. Reports of prosthetic joint infection (PJI) caused by this species remain rare.

We describe a case of delayed-onset PJI in a 71-year-old woman following total knee arthroplasty. Repeated conventional cultures were negative after empirical vancomycin therapy. Metagenomic next-generation sequencing (mNGS) of wound exudate detected S. argenteus, which guided extended culture and subsequent isolation of low-abundance colonies. Species identification was confirmed by whole-genome sequencing (WGS), multilocus sequence typing (MLST), and reinterpretation of MALDI-TOF MS results. Antimicrobial susceptibility testing (AST) was performed and compared with WGS-based resistance prediction. Phylogenetic analysis was conducted using 452 publicly available S. argenteus genomes.

This study aimed to describe the clinical diagnosis, microbiological identification, and genomic characterization of a Staphylococcus argenteus strain causing prosthetic joint infection.

The isolate was identified as sequence type ST2250 and lacked the staphyloxanthin operon, consistent with the non-pigmented phenotype. WGS and phenotypic AST showed 100% concordance across 11 clinically relevant antibiotics. Phylogenomic analysis revealed that the strain clustered closely with Southeast Asian lineages. Following targeted therapy with intravenous vancomycin and surgical wound management, the patient showed rapid clinical improvement with resolution of local inflammation and complete wound healing.

This is the first confirmed case of S. argenteus PJI in Suzhou, China. The case highlights the diagnostic value of mNGS in culture-negative PJI, the importance of molecular tools for correctly differentiating S. argenteus from S. aureus, and the potential of WGS to support resistance prediction for rare staphylococcal pathogens.