Faculty Opinions recommendation of A SUMOylation-dependent pathway mediates transrepression of inflammatory response genes by PPAR-gamma.
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) has essential roles in adipogenesis and glucose homeostasis, and is a molecular target of insulin-sensitizing drugs. Although the ability of PPAR-gamma agonists to antagonize inflammatory responses by transrepression of nuclear factor kappa B (NF-kappaB) target genes is linked to antidiabetic and antiatherogenic actions, the mechanisms remain poorly understood. Here we report the identification of a molecular pathway by which PPAR-gamma represses the transcriptional activation of inflammatory response genes in mouse macrophages. The initial step of this pathway involves ligand-dependent SUMOylation of the PPAR-gamma ligand-binding domain, which targets PPAR-gamma to nuclear receptor corepressor (NCoR)-histone deacetylase-3 (HDAC3) complexes on inflammatory gene promoters. This in turn prevents recruitment of the ubiquitylation/19S proteosome machinery that normally mediates the signal-dependent removal of corepressor complexes required for gene activation. As a result, NCoR complexes are not cleared from the promoter and target genes are maintained in a repressed state. This mechanism provides an explanation for how an agonist-bound nuclear receptor can be converted from an activator of transcription to a promoter-specific repressor of NF-kappaB target genes that regulate immunity and homeostasis. PMID: 16127449 Funding information This work was supported by: NIDDK NIH HHS, United States Grant ID: R37 DK039949 NIDDK NIH HHS, United States Grant ID: R01 DK091183 NCI NIH HHS, United States Grant ID: R01 CA052599
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.493 | 0.250 |
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".