The T cell response to staphylococcal superantigens is modulated by the bacterial cell wall (43.33)
Bibliographic record
Abstract
Abstract Bacterial superantigens (SAgs) are pyrogenic exotoxins that cause oligoclonal T cell activation leading to toxic shock and death. The selective advantage of SAg expression by bacteria remains unknown. We hypothesized that the T cell response to SAg is modulated by the bacteria. If so, one should see intense T cell responses when SAg are introduced into the body alone, but not when the producing bacteria are also present. We examined the responses of primary T cells to a panel of pure staphylococcal SAg or in the presence of heat-killed bacteria. We found that the IL-2 response to SAg was inhibited by the presence of bacteria, while the TNF-? and IFN-? responses were not affected or were even increased. Such a modulation of T cell responses to SAg was mediated by TLR2/6 agonists but not by agonists of TLR1/2, TLR-9 or NOD proteins, involved activation of the NF-_B pathway, and was prevented by anti-TLR-2 blocking antibodies. At the cellular level, this modulation resulted from death of antigen-presenting cells (APCs) but not of T cells. Thus, we show that the response to SAg is modulated by TLR2/6 agonists from the bacterial cell wall. Such a modulation involves the induction of APC death that then limits the capacity of SAg to cause massive T cell activation. Our findings provide an explanation for the long-standing question of selective advantage of SAg expression, and point to novel therapeutic strategies for SAg-associated diseases.
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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 | 0.000 |
| 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".