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Clinical Significance of Antibiotic Resistance

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Methicillin-resistant Staphylococcus aureus (MRSA) presents a critical public health threat, arising from its capacity to resist β-lactam antibiotics due to acquisition of the mecA gene within the staphylococcal cassette chromosome mec (SCCmec). This gene encodes penicillin-binding protein 2a (PBP2a), which impairs binding efficacy of methicillin and other β-lactams. MRSA has evolved into distinct clonal lineages impacting humans and animals alike, reinforcing its significance within...
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Staphylococcus aureus is a Gram-positive coccus that resides harmlessly on the skin and mucous membranes of healthy individuals. When the skin barrier is breached, it can shift from a commensal to an opportunistic pathogen. This transition is facilitated by surface adhesins, such as clumping factor B and S. aureus surface protein G (SasG), which bind to structural proteins, including loricrin and cytokeratin, in the damaged epidermis. Protein A, another key factor, binds the Fc region of...
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Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR RS-PCR
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Do Staphylococcus epidermidis Genetic Clusters Predict Isolation Sources?

Isaiah Tolo1, Jonathan C Thomas2, Rebecca S B Fischer3

  • 1Department of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, Mississippi, USA.

Journal of Clinical Microbiology
|April 15, 2016
PubMed
Summary
This summary is machine-generated.

Genetic clusters of Staphylococcus epidermidis can distinguish between hospital and non-hospital sources. This finding aids in understanding the population structure of this common skin bacterium and its role in infections.

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Area of Science:

  • Microbiology
  • Population Genetics
  • Infectious Diseases

Background:

  • Staphylococcus epidermidis is a common skin bacterium and a frequent cause of infections associated with medical devices.
  • The genetic structure differentiating commensal and pathogenic strains of S. epidermidis is not well understood.
  • Previous studies identified six genetic clusters (GCs) within S. epidermidis populations.

Purpose of the Study:

  • To verify the existence of six genetic clusters (GCs) in an updated S. epidermidis dataset.
  • To develop a single nucleotide polymorphism (SNP) assay for efficient GC assignment.
  • To investigate if GCs can differentiate between hospital-acquired infections, contaminants, and non-hospital carriage.

Main Methods:

  • Updated multilocus sequence typing (MLST) database analysis to confirm six GCs.
  • Development and validation of a SNP-based assay for GC identification.
  • SNP typing and GC assignment of isolates from various clinical and non-clinical sources.

Main Results:

  • The six GCs were confirmed, with GC3 and GC5 showing significant admixture.
  • The SNP assay accurately assigned 94% of isolates to GCs.
  • GC5 was predominantly found in hospital-associated isolates, while GC1 and GC6 were more broadly distributed.
  • Machine learning models predicted hospital vs. non-hospital sources with 80% accuracy using GCs.

Conclusions:

  • S. epidermidis population structure, analyzed via subgenomic data, can distinguish between hospital and non-hospital sources.
  • The study highlights the utility of GCs in understanding the ecological and pathogenic potential of S. epidermidis.
  • Distinguishing between infection and contaminant sources within hospitals using GCs showed limited accuracy.