IL-23R–activated STAT3/STAT4 is essential for Th1/Th17-mediated CNS autoimmunity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The factors that promote the differentiation of pathogenic T cells in autoimmune diseases are poorly defined. Use of genetically modified mice has provided insight into molecules necessary for the development of autoimmunity, but the sum of the data has led to contradictory observations based on what is currently known about specific molecules in specific signaling pathways. To define the minimum signals required for development of encephalitogenic T cells that cause CNS autoimmunity, myelin-specific T cells were differentiated with various cytokine cocktails, and pathogenicity was determined by transfer into mice. IL-6+IL-23 or IL-12+IL-23 generated encephalitogenic T cells and recapitulated the essential cytokine signals provided by antigen-presenting cells, and both IL-6 and IL-12 induced IL-23 receptor expression on both mouse and human naive T cells. IL-23 signaled through both STAT3 and STAT4, and disruption in STAT4 signaling impaired CNS autoimmunity independent of IL-12. These data explain why IL-12-deficient mice develop CNS autoimmunity, while STAT4-deficient mice are resistant. CD4+ memory T cells from multiple sclerosis patients had significantly higher levels of p-STAT3/p-STAT4, and p-STAT3/p-STAT4 heterodimers were observed upon IL-23 signaling, suggesting that p-STAT3/p-STAT4 induced by IL-23 signaling orchestrate the generation of pathogenic T cells in CNS autoimmunity, regardless of Th1 or Th17 phenotype.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 it