Tudor-SN, a Novel Coactivator of Peroxisome Proliferator-activated Receptor γ Protein, Is Essential for Adipogenesis
Bibliographic record
Abstract
Adipogenesis, in which mesenchymal precursor cells differentiate into mature adipocytes, is a well orchestrated process. In the present study we identified Tudor-SN as a novel co-activator of the transcription factor peroxisome proliferator-activated receptor γ (PPARγ). We provide the first evidence that Tudor-SN and PPARγ exist in the same complex. Both are up-regulated by the early factor C/EBPβ during adipogenesis and significantly influence the regulation of PPARγ target genes in both 3T3-L1 pre-adipocyte and mouse embryonic fibroblasts (MEF) upon exposure to a mixture of hormonal mixture. Moreover, aP2-PPARγ response element (PPRE) interacts with both PPARγ and Tudor-SN, and the gene transcriptional activation of PPRE-luc is enhanced by ectopic expression of Tudor-SN. Deletion of Tudor-SN protein (MEF-KO) affects but does not completely abolish the association of PPARγ and aP2-PPRE. Loss-of-function studies further verified that Tudor-SN is required for adipogenesis, as deletion of Tudor-SN (MEF-KO) impairs dexamethasone, 3-isobutyl-1-methylxanthine, and insulin (DMI)-induced adipocyte differentiation and the expression of PPARγ target genes, such as aP2 and adipsin. Furthermore, H3 acetylation levels were lower in MEF-KO than MEF-WT. Both HDAC1 and HDAC3 are stably associated with PPARγ in MEF-KO, whereas only a small amount of association was observed in MEF-WT after 5 days of treatment during adipogenesis. PPARγ requires various co-activators or co-repressors, which may dynamically associate with and regulate the higher order chromatin remodeling of the promoter region of PPARγ-bound target genes; Tudor-SN is likely one of these co-activators.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".