Peroxisome Proliferator–Activated Receptor-δ Acts within Peripheral Myeloid Cells to Limit Th Cell Priming during Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Abstract Peroxisome proliferator-activated receptor (PPAR)-δ is a fatty acid–activated transcription factor that regulates metabolic homeostasis, cell growth, and differentiation. Previously, we reported that mice with a global deficiency of PPAR-δ develop an exacerbated course of experimental autoimmune encephalomyelitis (EAE), highlighting a role for this nuclear receptor in limiting the development of CNS inflammation. However, the cell-specific contribution of PPAR-δ to the more severe CNS inflammatory response remained unclear. In this study, we studied the specific involvement of PPAR-δ in myeloid cells during EAE using mice that had Cre-mediated excision of floxed Ppard driven by the lysozyme M (LysM) promoter (LysMCre:Ppardfl/fl). We observed that LysMCre:Ppardfl/fl mice were more susceptible to EAE and developed a more severe course of this disease compared with Ppardfl/fl controls. The more severe EAE in LysMCre:Ppardfl/fl mice was associated with an increased accumulation of pathogenic CD4+ T cells in the CNS and enhanced myelin-specific Th1 and Th17 responses in the periphery. Adoptive transfer EAE studies linked this EAE phenotype in LysMCre:Ppardfl/fl mice to heightened Th responses. Furthermore, studies using an in vitro CD11b+ cell:Th cell coculture system revealed that CD11b+CD11c+ dendritic cells (DC) from LysMCre:Ppardfl/fl mice had a heightened capacity to prime myelin oligodendrocyte glycoprotein (MOG)–specific Th cells compared with Ppardfl/fl counterparts; the effects of DC on Th1 cytokine production were mediated through production of the IL-12p40 homodimer. These studies revealed a role for PPAR-δ in DC in limiting Th cell priming during EAE.
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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".