Loss of STAT3 in CD4+ T Cells Prevents Development of Experimental Autoimmune Diseases
Bibliographic record
Abstract
Th17 cells are implicated in CNS autoimmune diseases. We show that mice with targeted-deletion of Stat3 in CD4(+) T cells (CD4(Stat3)(-/-)) do not develop experimental autoimmune uveoretinitis (EAU) or experimental autoimmune encephalomyelitis. Defective Th17 differentiation noted in CD4(Stat3)(-/-) mice is compensated by exaggerated increases in Foxp3-, IL-10-, IL-4-, and IFN-gamma-expressing T cells, suggesting critical roles of STAT3 in shaping Ag-specific CD4(+) T cell repertoire. In mice with EAU, a high percentage of IL-17-expressing T cells in their peripheral lymphoid organs also secrete IFN-gamma while these double-expressors are absent in CD4(Stat3)(-/-) and wild-type mice without EAU, raising the intriguing possibility that uveitis maybe mediated by Th17 and IL-17-expressing Th1 cells. Resistance of Stat3-deficient mice to EAU derives in part from an inability of uveitogenic Th17 and Th1 cells to enter eyes or brain of the CD4(Stat3)(-/-) mouse because of the reduction in the expression of activated alpha4/beta1 integrins on CD4(Stat3)(-/-) T cells. Adoptive transfer of activated interphotoreceptor retinoid-binding protein-specific uveitogenic T cells induced in CD4(Stat3)(-/-) mice a severe EAU characterized by development of retinal folds, infiltration of inflammatory cells into the retina, and destruction of retinal architecture, underscoring our contention that the loss of STAT3 in CD4(+) T cells results in an intrinsic developmental defect that renders CD4(Stat3)(-/-) resistant to CNS inflammatory diseases. STAT3 requirement for IL-17 production by Th17, generation of double positive T cells expressing IL-17 and IFN-gamma, and for T cell trafficking into CNS tissues suggests that STAT3 may be a therapeutic target for modulating uveitis, sceritis, or multiple sclerosis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".