GPR103b Functions in the Peripheral Regulation of Adipogenesis
Bibliographic record
Abstract
The activation of G protein-coupled receptor 103 (GPR103) by its endogenous peptidic ligands, QRFPs, is involved in the central regulation of feeding by increasing food intake, body weight, and fat mass after intracerebroventricular injection in mice. However, the role of GPR103 in regulating peripheral metabolic pathways has not yet been explored. The present study aimed to investigate the role of GPR103 in adipogenesis and lipid metabolism using 3T3-L1 adipocyte cells. Our results show that differentiated 3T3-L1 cells expressed the GPR103b subtype mRNA and protein, as well as QRFP mRNA. QRFP-43 and -26 induced an increase in triglyceride accumulation of 50 and 41%, respectively, and elicited a dose-dependent increase in fatty acid uptake, by up to approximately 60% at the highest concentration, in 3T3-L1-differentiated cells. QRFP-43 and -26 inhibited isoproterenol (ISO)-induced lipolysis in a dose-dependent manner, with IC(50)s of 2.3 +/- 1.2 and 1.1 +/- 1.0 nm, respectively. The expression of genes involved in lipid uptake (FATP1, CD36, LPL, ACSL1, PPAR-gamma, and C/EBP-alpha), was increased by 2- to 3-fold after treatment with QRFP. The effects of QRFP on ISO-induced lipolysis and fatty acid uptake were abolished when GPR103b was silenced. In a mouse model of diet-induced obesity, the expression of GPR103b in epididymal fat pads was elevated by 16-fold whereas that of QRFP was reduced by 46% compared to lean mice. Furthermore, QRFP was bioactive in omental adipocytes from obese individuals, inhibiting ISO-induced lipolysis in these cells. Our results suggest that GPR103b and QRFP work in an autocrine/paracrine manner to regulate adipogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".