IL-17A is rapidly triggered by staphylococcal superantigens and is pathogenic in toxic shock syndrome
Bibliographic record
Abstract
Abstract Toxic shock syndrome (TSS) is a life-threatening illness characterized by high-grade fever, erythematous rash, hypotensive shock, and multi-organ dysfunction within hours of onset. It is caused by exposure to bacterial exotoxins known as superantigens (SAgs), which are produced by the common bacterium Staphylococcus aureus. During infection, SAgs activate up to 20% of T cells inducing a severe systemic inflammatory response that can lead to vascular leakage, multiple organ damage and death. However, the role of IL-17A, a potent inflammatory cytokine, in this potentially fatal reaction remains virtually unexplored. We have found that human peripheral blood mononuclear cells (PBMCs) stimulated with SAgs in vitro immediately up-regulate IL-17A mRNA (up to 12,000-fold) and produce substantial amounts of IL-17A protein within hours. Utilizing the cutting-edge RNA-Flow Cytometry technique, we identified a subset of memory T cells that was responsible for the rapid IL-17A response. We also investigated the effect of IL-17A signaling on downstream inflammatory cytokine and chemokine gene expression via IL-17A receptor blockade. Lastly, we used HLA-DR4 transgenic mice to demonstrate rapid IL-17A production in response to SAgs, similar to human PBMCs. Neutralizing IL-17A in this model greatly reduced tissue damage, morbidity and mortality. Taken together, our results reveal a novel role for memory T cells in TSS and define a previously unrecognized contribution by IL-17A to the initiation and pathogenesis of the syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".