Chronic activation of pattern recognition receptors suppresses brown adipogenesis of multipotent mesodermal stem cells and brown pre-adipocytes
Bibliographic record
Abstract
Brown adipose tissue (BAT) holds promise to combat obesity through energy-spending, non-shivering thermogenesis. Understanding of regulation of BAT development can lead to novel strategies to increase BAT mass and function for obesity treatment and prevention. Here, we report the effects of chronic activation of PRR on brown adipogenesis of multipotent mesodermal stem C3H10T1/2 cells and immortalized brown pre-adipocytes from the classical interscapular BAT of mice. Activation of NOD1, TLR4, or TLR2 by their respective synthetic ligand suppressed brown marker gene expression and lipid accumulation during differentiation of brown-like adipocytes of C3H10T1/2. Activation of the PRR only during the commitment was sufficient to suppress the differentiation. PRR activation suppressed PGC-1α mRNA, but induced PRDM16 mRNA at the commitment. Consistently, PRR activation suppressed the differentiation of immortalized brown pre-adipocytes. Activation of PRR induced NF-κB activation in both cells, which correlated with their abilities to suppress PPARγ transactivation, a critical event for brown adipogenesis. Taken together, our results demonstrate that chronic PRR activation suppressed brown adipogenesis of multipotent mesodermal stem cells and brown pre-adipocytes, possibly through suppression of PPARγ transactivation. The results suggest that anti- inflammatory therapies targeting PRRs may be beneficial for the BAT development.
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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 | 0.000 |
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".