The thrombin receptor PAR4 supports visceral adipose tissue inflammation
Bibliographic record
Abstract
Abstract Thrombin inhibition suppresses adiposity, WAT inflammation and metabolic dysfunction in mice. Protease-activated receptor (PAR)1 does not account for thrombin-driven obesity, so we explored the culprit role of PAR4 in this context. Male WT and PAR-4-/- mice received a high fat diet (HFD) for 8 weeks, WT controls received standard chow. Body fat was quantified by NMR. Epididymal WAT was assessed by histology, immunohistochemistry, qPCR and lipase activity assay. 3T3-L1 preadipocytes were differentiated ± thrombin, acutely stimulated ± PAR4 activating peptide (AP) and assessed by immunoblot, qPCR and U937 monocyte adhesion. Epicardial adipose tissue (EAT) from obese and lean patients was assessed by immunoblot. PAR4 was upregulated in mouse WAT under HFD. PAR4-/- mice developed less visceral adiposity and glucose intolerance under HFD, featuring smaller adipocytes, fewer macrophages and lower expression of adipogenic (leptin, PPARγ) and pro-inflammatory genes (CCL2, IL-1β) in WAT. HFD-modified activity and expression of lipases or perilipin were unaffected by PAR4 deletion. 3T3-L1 adipocytes differentiated with thrombin retained Ki67 expression, further upregulated IL-1β and CCL2 and were more adhesive for monocytes. In mature adipocytes, PAR4-AP increased phosphorylated ERK1/2 and AKT, upregulated Ki67, CCl2, IL-β and hyaluronan synthase 1 but not TNF-α mRNA, and augmented hyaluronidase-sensitive monocyte adhesion. Obese human EAT expressed more PAR4, CD68 and CD54 than lean EAT. PAR4 upregulated in obesity supports adipocyte hypertrophy, WAT expansion and thrombo-inflammation. The emerging PAR4 antagonists provide a therapeutic perspective in this context beyond their canonical antiplatelet action.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".