Superantigens Modulate Bacterial Density during Staphylococcus aureus Nasal Colonization
Bibliographic record
Abstract
Superantigens (SAgs) are potent microbial toxins that function to activate large numbers of T cells in a T cell receptor (TCR) Vβ-specific manner, resulting in excessive immune system activation. Staphylococcus aureus possesses a large repertoire of distinct SAgs, and in the context of host-pathogen interactions, staphylococcal SAg research has focused primarily on the role of these toxins in severe and invasive diseases. However, the contribution of SAgs to colonization by S. aureus remains unclear. We developed a two-week nasal colonization model using SAg-sensitive transgenic mice expressing HLA-DR4, and evaluated the role of SAgs using two well-studied stains of S. aureus. S. aureus Newman produces relatively low levels of staphylococcal enterotoxin A (SEA), and although we did not detect significant TCR-Vβ specific changes during wild-type S. aureus Newman colonization, S. aureus Newman Δsea established transiently higher bacterial loads in the nose. S. aureus COL produces relatively high levels of staphylococcal enterotoxin B (SEB), and colonization with wild-type S. aureus COL resulted in clear Vβ8-specific T cell skewing responses. S. aureus COL Δseb established consistently higher bacterial loads in the nose. These data suggest that staphylococcal SAgs may be involved in regulating bacterial densities during nasal colonization.
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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 | 0.000 |
| 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 | 0.001 |
| 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".