Staphylococcal superantigens trigger rapid human IL-17A production by a novel population of memory T cells (HUM1P.268)
Bibliographic record
Abstract
Abstract Toxic shock syndrome (TSS) is a life-threatening illness characterized by fever, hypotensive shock and multi-organ failure within hours of onset. TSS is caused by exposure to bacterial toxins known as superantigens (SAgs), which are produced by common Gram-positive bacteria such as Staphylococcus aureus. During infection, SAgs can activate of up to 20% of all T cells, resulting in massive and rapid release of pro-inflammatory cytokines, systemic intravascular coagulation, organ damage and death. However, the exact cellular mechanisms underlying the rapidity of this potentially fatal inflammatory reaction are far from clearly understood. In our preliminary experiments, human peripheral blood mononuclear cells (PBMCs) stimulated with SAgs in vitro showed immediate up-regulation (up to 2000-fold) of mRNA for the pro-inflammatory cytokine IL-17A. SAg-stimulated PBMCs also produced substantial amounts of IL-17A protein within hours of activation. The cells responsible for the rapid production of IL-17A were identified as a subset of memory T cells exhibiting a novel phenotype. Furthermore, neutralizing IL-17A at the onset significantly reduced both morbidity and mortality in a humanized mouse model of TSS. Our results reveal a critical role for memory T cells in TSS and define a novel contribution by IL-17A to the initiation and pathogenesis of the syndrome.
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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) | 0.002 | 0.001 |
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".