Fibroblast activation protein (FAP) as a novel metabolic target
Bibliographic record
Abstract
OBJECTIVE: Fibroblast activation protein (FAP) is a serine protease belonging to a S9B prolyl oligopeptidase subfamily. This enzyme has been implicated in cancer development and recently reported to regulate degradation of FGF21, a potent metabolic hormone. Using a known FAP inhibitor, talabostat (TB), we explored the impact of FAP inhibition on metabolic regulation in mice. METHODS: To address this question we evaluated the pharmacology of TB in various mouse models including those deficient in FGF21, GLP1 and GIP signaling. We also studied the ability of FAP to process FGF21 in vitro and TB to block FAP enzymatic activity. RESULTS: TB administration to diet-induced obese (DIO) animals led to profound decreases in body weight, reduced food consumption and adiposity, increased energy expenditure, improved glucose tolerance and insulin sensitivity, and lowered cholesterol levels. Total and intact plasma FGF21 were observed to be elevated in TB-treated DIO mice but not lean animals where the metabolic impact of TB was significantly attenuated. Furthermore, and in stark contrast to naïve DIO mice, the administration of TB to obese FGF21 knockout animals demonstrated no appreciable effect on body weight or any other measures of metabolism. In support of these results we observed no enzymatic degradation of human FGF21 at either end of the protein when FAP was inhibited in vitro by TB. CONCLUSIONS: We conclude that pharmacological inhibition of FAP enhances levels of FGF21 in obese mice to provide robust metabolic benefits not observed in lean animals, thus validating this enzyme as a novel drug target for the treatment of obesity and diabetes.
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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".