Targeting PPARγ in the epigenome rescues genetic metabolic defects in mice
Bibliographic record
Abstract
Obesity causes insulin resistance, and PPARγ ligands such as rosiglitazone are insulin sensitizing, yet the mechanisms remain unclear. In C57BL/6 (B6) mice, obesity induced by a high-fat diet (HFD) has major effects on visceral epididymal adipose tissue (eWAT). Here, we report that HFD-induced obesity in B6 mice also altered the activity of gene regulatory elements and genome-wide occupancy of PPARγ. Rosiglitazone treatment restored insulin sensitivity in obese B6 mice, yet, surprisingly, had little effect on gene expression in eWAT. However, in subcutaneous inguinal fat (iWAT), rosiglitazone markedly induced molecular signatures of brown fat, including the key thermogenic gene Ucp1. Obesity-resistant 129S1/SvImJ mice (129 mice) displayed iWAT browning, even in the absence of rosiglitazone. The 129 Ucp1 locus had increased PPARγ binding and gene expression that were preserved in the iWAT of B6x129 F1-intercrossed mice, with an imbalance favoring the 129-derived alleles, demonstrating a cis-acting genetic difference. Thus, B6 mice have genetically defective Ucp1 expression in iWAT. However, when Ucp1 was activated by rosiglitazone, or by iWAT browning in cold-exposed or young mice, expression of the B6 version of Ucp1 was no longer defective relative to the 129 version, indicating epigenomic rescue. These results provide a framework for understanding how environmental influences like drugs can affect the epigenome and potentially rescue genetically determined disease phenotypes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".