Do Staphylococcus epidermidis genetic clusters predict isolation sources?
| Autor: | Tolo, I, Thomas, JC, Fischer, RSB, Brown, EL, Gray, BM, Robinson, DA, Carroll, KC |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Adult
Male Coagulase 0301 basic medicine Microbiology (medical) Staphylococcus 030106 microbiology Population Bacteremia Single-nucleotide polymorphism Polymorphism Single Nucleotide Young Adult 03 medical and health sciences Staphylococcus epidermidis Commentaries Arginine catabolic mobile element Genetic variation Cluster Analysis Humans Typing education Phylogeny Aged Aged 80 and over Genetics Cross Infection education.field_of_study biology Infant Newborn Genetic Variation Infant Bacteriology Middle Aged Staphylococcal Infections biology.organism_classification 3. Good health 030104 developmental biology Genetic marker Carrier State Multilocus sequence typing Female Multilocus Sequence Typing |
| ISSN: | 0095-1137 |
| Popis: | 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 , IS 256 , 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. |
| Databáze: | OpenAIRE |
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