Shuffling of mobile genetic elements (MGEs) in successful healthcare-associated MRSA (HA-MRSA)
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) CC22 SCCmecIV is a successful hospital-associated (HA-) MRSA, widespread throughout the world, and now the dominant clone in UK hospitals. We have recently shown that MRSA CC22 is a particularly fit clone, and it rose to dominance in a UK hospital at the same time as it began acquiring an increased range of antibiotic resistances. These resistances were not accumulated by individual CC22 isolates, but appear to shuffle frequently between isolates of the MRSA CC22 population. Resistances are often encoded on mobile genetic elements (MGEs) that include plasmids, transposons, bacteriophage and S. aureus pathogenicity islands (SaPIs). Using multi-strain whole genome microarrays, we show that there is enormous diversity of MGE carried within a MRSA CC22 SCCmecIV population, even among isolates from the same hospital and time period. MGE profiles were so variable that they could be used to track the spread of variant isolates within the hospital. We exploited this to show that the majority of patients colonised with MRSA at hospital admission that subsequently became infected were infected with their own colonising isolate. Our studies reveal MGE spread, stability, selection and clonal adaptation to the healthcare setting may be key to the success of HA-MRSA clones, presumably by allowing rapid adaptation to antibiotic exposure and new hosts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".