Do Staphylococcus epidermidis Genetic Clusters Predict Isolation Sources?

Abstract

Staphylococcus epidermidis is a ubiquitous colonizer of human skin and a common cause of medical device-associated infections. The extent to which the population genetic structure of S. epidermidis distinguishes commensal from pathogenic isolates is unclear. Previously, Bayesian clustering of 437 multilocus sequence types (STs) in the international database revealed a population structure of six genetic clusters (GCs) that may reflect the species' ecology. Here, we first verified the presence of six GCs, including two (GC3 and GC5) with significant admixture, in an updated database of 578 STs. Next, a single nucleotide polymorphism (SNP) assay was developed that accurately assigned 545 (94%) of 578 STs to GCs. Finally, the hypothesis that GCs could distinguish isolation sources was tested by SNP typing and GC assignment of 154 isolates from hospital patients with bacteremia and those with blood culture contaminants and from nonhospital carriage. GC5 was isolated almost exclusively from hospital sources. GC1 and GC6 were isolated from all sources but were overrepresented in isolates from nonhospital and infection sources, respectively. GC2, GC3, and GC4 were relatively rare in this collection. No association was detected between fdh-positive isolates (GC2 and GC4) and nonhospital sources. Using a machine learning algorithm, GCs predicted hospital and nonhospital sources with 80% accuracy and predicted infection and contaminant sources with 45% accuracy, which was comparable to the results seen with a combination of five genetic markers (icaA, IS256, sesD [bhp], mecA, and arginine catabolic mobile element [ACME]). Thus, analysis of population structure with subgenomic data shows the distinction of hospital and nonhospital sources and the near-inseparability of sources within a hospital.

Versatility of Biofilm Matrix Molecules in Staphylococcus epidermidis Clinical Isolates and Importance of Polysaccharide Intercellular Adhesin Expression during High Shear Stress

“…Regarding genetic cluster analysis, consistent with previous reports, we found that 93% of isolates tested were GC1 or GC5 or GC6 ( 61 , 79 ). Although trends were noted with respect to specific genetic clusters and high- or low-shear isolates (e.g., for GC1 and high shear, n = 8; for GC1 and low shear, n = 16), none of these associations were statistically significant.…”

“…Although trends were noted with respect to specific genetic clusters and high- or low-shear isolates (e.g., for GC1 and high shear, n = 8; for GC1 and low shear, n = 16), none of these associations were statistically significant. However, two previous reports of S. epidermidis isolates from New York and Illinois found an association between GC5 and the presence of icaADBC ( 61 , 79 ). Our data are consistent with these observations in that 16/17 (94%) of the GC5 isolates in our study carried icaADBC , whereas only 33% of the isolates overall carried this locus .…”

Commentary: Significance of Staphylococcus epidermidis in Health Care-Associated Infections, from Contaminant to Clinically Relevant Pathogen: This Is a Wake-Up Call!

“…In light of the current challenges facing clinical microbiology and professionals addressing control of infectious diseases, the comprehensive article by Tolo and colleagues included in this issue of Journal of Clinical Microbiology presents an interesting method for simplifying the identification of S. epidermidis genotypes in the clinical setting (27). By analyzing seven single nucleotide polymorphisms (SNPs), these researchers were able to accurately assign 545 (94%) of 578 sequence types to six genetic clusters (GCs), which were hypothesized to reflect three different sources of isolates: blood isolates representing true infection, blood isolates representing contaminants, and carriage isolates from nonhospital sources.…”

Pan-genome analysis reveals the molecular basis of niche adaptation ofStaphylococcus epidermidisstrains

“…From the phylogenetic tree, we found the ST2 isolates had an extremely short evolutionary distance from each other. The genetic markers mec A and ica A, which are used to predict the antimicrobial resistance and biofilm phenotypes, have been shown to be more common in hospital isolates than in non-hospital isolates; however, these markers have much less power to distinguish infection isolates from commensally available isolates that contaminate clinical specimens [41]. According to the enrichment analysis, we found it was impossible to distinguish the strains of blood from that of skin, both of which had a similar lifestyle and genetic background.…”

“…Whole genome sequencing has been proved to be a more powerful routine diagnostic tool than the traditional MLST or RT-PCR because it can rapidly identify the infection source and antibiotic resistance in an affordable manner [42, 43]. As more genetic data of S. epidermidis have been available and new machine learning algorithm is developed [41], WGS may help to predict the infection isolation sources and antibiotic resistance in a quicker and more accurate manner.…”