PPARγ As a Potential Target for Adipogenesis Induced by Fine Particulate Matter in 3T3-L1 Preadipocytes
Bibliographic record
Abstract
Mounting evidence has shown that ambient PM 2.5 exposure is closely associated with the development of obesity, and adipose tissue represents an important endocrine target for PM 2.5 . In this study, the 3T3-L1 preadipocyte differentiation model was employed to comprehensively explore the adipogenic potential of PM 2.5 . After 8 days of PM 2.5 exposure, adipocyte fatty acid uptake and lipid accumulation were significantly increased, and adipogenic differentiation of 3T3-L1 cells was promoted in a concentration-dependent manner. Transcriptome and lipidome analyses revealed the systematic disruption of transcriptional and lipid profiling at 10 μg/mL PM 2.5 . Functional enrichment and visualized network analyses showed that the peroxisome proliferator-activated receptor (PPAR) pathway and the metabolism of glycerophospholipids, glycerolipids, and sphingolipids were most significantly affected during adipocyte differentiation. Reporter gene assays indicated that PPARγ was activated by PM 2.5, demonstrating that PM 2.5 promoted adipogenesis by activating PPARγ. The increased transcriptional and protein expressions of PPARγ and downstream adipogenesis-associated markers ( e.g., Fabp4 and CD36) were further cross-validated using qRT-PCR and western blot. PM 2.5 -induced adipogenesis, PPARγ pathway activation, and lipid remodeling were significantly attenuated by the supplementation of a PPARγ antagonist (T0070907). Overall, this study yielded mechanistic insights into PM 2.5 -induced adipogenesis in vitro by identifying the potential biomolecular targets for the prevention of PM 2.5 -induced obesity and related metabolic diseases.
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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 | 0.000 |
| 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".