T-bet negatively regulates autoimmune myocarditis by suppressing local production of interleukin 17
Bibliographic record
Abstract
Experimental autoimmune myocarditis (EAM) appears after infectious heart disease, the most common cause of dilated cardiomyopathy in humans. Here we report that mice lacking T-bet, a T-box transcription factor required for T helper (Th)1 cell differentiation and interferon (IFN)-gamma production, develop severe autoimmune heart disease compared to T-bet+/+ control mice. Experiments in T-bet-/- IL-4-/- and T-bet-/- IL-4Ralpha-/- mice, as well as transfer of heart-specific Th1 and Th2 cell lines, showed that autoimmune heart disease develops independently of Th1 or Th2 polarization. Analysis of T-bet-/- IL-12Rbeta1-/- and T-bet-/- IL-12p35-/- mice then identified interleukin (IL)-23 as critical for EAM pathogenesis. In addition, T-bet-/- mice showed a marked increase in production of the IL-23-dependent cytokine IL-17 by heart-infiltrating lymphocytes, and in vivo IL-17 depletion markedly reduced EAM severity in T-bet-/- mice. Heart-infiltrating T-bet-/- CD8+ but not CD8- T cells secrete IFN-gamma, which inhibits IL-17 production and protects against severe EAM. In contrast, T-bet-/- CD8+ lymphocytes completely lost their capacity to release IFN-gamma within the heart. Collectively, these data show that severe IL-17-mediated EAM can develop in the absence of T-bet, and that T-bet can regulate autoimmunity via the control of nonspecific CD8+ T cell bystander functions in the inflamed target organ.
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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) | 0.001 | 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".