Peroxisome proliferator–activated receptor δ limits the expansion of pathogenic Th cells during central nervous system autoimmunity
Bibliographic record
Abstract
Peroxisome proliferator-activated receptors (PPARs; PPAR-alpha, PPAR-delta, and PPAR-gamma) comprise a family of nuclear receptors that sense fatty acid levels and translate this information into altered gene transcription. Previously, it was reported that treatment of mice with a synthetic ligand activator of PPAR-delta, GW0742, ameliorates experimental autoimmune encephalomyelitis (EAE), indicating a possible role for this nuclear receptor in the control of central nervous system (CNS) autoimmune inflammation. We show that mice deficient in PPAR-delta (PPAR-delta(-/-)) develop a severe inflammatory response during EAE characterized by a striking accumulation of IFN-gamma(+)IL-17A(-) and IFN-gamma(+)IL-17A(+) CD4(+) cells in the spinal cord. The preferential expansion of these T helper subsets in the CNS of PPAR-delta(-/-) mice occurred as a result of a constellation of immune system aberrations that included higher CD4(+) cell proliferation, cytokine production, and T-bet expression and enhanced expression of IL-12 family cytokines by myeloid cells. We also show that the effect of PPAR-delta in inhibiting the production of IFN-gamma and IL-12 family cytokines is ligand dependent and is observed in both mouse and human immune cells. Collectively, these findings suggest that PPAR-delta serves as an important molecular brake for the control of autoimmune inflammation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".