Do Staphylococcus epidermidis Genetic Clusters Predict Isolation Sources?

June 24, 2016 at 2:01 pm

Journal of Clinical Microbiology July 2016 V.54 N.7 P.1711-1719

Isaiah Tolo, Jonathan C. Thomas, Rebecca S. B. Fischer, Eric L. Brown, Barry M. Gray, and D. Ashley Robinson

aDepartment of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, Mississippi, USA

bDepartment of Biology, University of Bolton, Bolton, United Kingdom

cCenter for Infectious Disease, University of Texas Health Science Center, Houston, Texas, USA

dDepartment of Pediatrics, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA

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.



Entry filed under: Antimicrobianos, Bacterias, Bacteriemias, Health Care-Associated Infections, Infecciones nosocomiales, Metodos diagnosticos, Resistencia bacteriana, Update.

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