Characterization of Community-Acquired Methicillin-Resistant <em>Staphylococcus aureus</em> by Pulsed-Field Gel Electrophoresis, Multilocus Sequence Typing, and Staphylococcal Protein A Sequencing: Establishing a Strain Typing Database
Bibliographic record
Abstract
Staphylococcus aureus has long been recognized as a leading cause of nosocomial infection. However, several recent publications have demonstrated this pathogen as the cause of community-acquired severe wound infections and necrotizing pneumonia in otherwise healthy individuals. These highly virulent endemic clones have been reported in several locations in the United States and Canada. The rapid spread of the organism, the ability of certain clones to cause serious infection, and the antibiotic resistance of the endemic clones, illustrates the importance of infection control measures. In this study we examined three S. aureus typing techniques; pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), and Staphylococcal protein A (spa) sequencing for subspeciation of community-acquired methicillin-resistant S. aureus (CA-MRSA). It is hypothesized that PFGE will result in a higher level of discrimination among the strains, while MLST and spa typing will result in highly portable data that lacks the discriminatory power of PFGE. Thirty CA-MRSA isolates that were obtained from Florida and Washington State were characterized by molecular typing methods. Whole genome restriction analysis was performed by PFGE using the SmaI enzyme. Sequence-based typing analyses, MLST and spa typing, were performed by polymerase chain reaction (PCR) followed by sequencing. PFGE data was analyzed using the BioNumerics® software package and sequence-based data was analyzed using DNAstar®. MLST Alleles were assigned using the online MLST database (www.mlst.net) and spa types were assigned using the Ridom SpaServer (www.ridom.de/spaserver). Molecular characterization of the 30 isolates resulted in 21 pulsotypes, four MLST sequence types (STs), and six spa types. Combining data from both MLST and spa typing resulted in only seven strain categories, many of which grouped isolates that are not epidemiologically linked. These data demonstrate that techniques such as MLST and spa typing are not well suited for tracking isolates with limited evolutionary diversity such as the CA-MRSA epidemic clones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".