Staphylococcal enterotoxin A (SEA) stimulates STAT3 activation and IL-17 expression in cutaneous T-cell lymphoma
Bibliographic record
Abstract
Cutaneous T-cell lymphoma (CTCL) is characterized by proliferation of malignant T cells in a chronic inflammatory environment. With disease progression, bacteria colonize the compromised skin barrier and half of CTCL patients die of infection rather than from direct organ involvement by the malignancy. Clinical data indicate that bacteria play a direct role in disease progression, but little is known about the mechanisms involved. Here, we demonstrate that bacterial isolates containing staphylococcal enterotoxin A (SEA) from the affected skin of CTCL patients, as well as recombinant SEA, stimulate activation of signal transducer and activator of transcription 3 (STAT3) and upregulation of interleukin (IL)-17 in immortalized and primary patient-derived malignant and nonmalignant T cells. Importantly, SEA induces STAT3 activation and IL-17 expression in malignant T cells when cocultured with nonmalignant T cells, indicating an indirect mode of action. In accordance, malignant T cells expressing an SEA-nonresponsive T-cell receptor variable region β chain are nonresponsive to SEA in monoculture but display strong STAT3 activation and IL-17 expression in cocultures with SEA-responsive nonmalignant T cells. The response is induced via IL-2 receptor common γ chain cytokines and a Janus kinase 3 (JAK3)-dependent pathway in malignant T cells, and blocked by tofacitinib, a clinical-grade JAK3 inhibitor. In conclusion, we demonstrate that SEA induces cell cross talk-dependent activation of STAT3 and expression of IL-17 in malignant T cells, suggesting a mechanism whereby SEA-producing bacteria promote activation of an established oncogenic pathway previously implicated in carcinogenesis.
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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".