Effect of desacyl ghrelin, obestatin and related peptides on triglyceride storage, metabolism and GHSR signaling in 3T3‐L1 adipocytes
Bibliographic record
Abstract
Acyl-ghrelin (AG), desacyl-ghrelin (DAG) and obestatin are all derived from the same gene transcript; however their plasma levels do not necessarily change in parallel. The influence of these peptides towards the development of obesity and their direct effects on adipocyte physiology has not been thoroughly investigated. This study was designed to evaluate the direct effects of peptides of the ghrelin family on preadipocyte proliferation, differentiation and adipocyte lipid and glucose metabolism in 3T3-L1 cells. 3T3 cells were treated with physiological peptide concentrations for 1 h to 9 days, and the relevant assays measured. In preadipocytes, AG, GHRP-6 and DAG stimulated proliferation, measured as (3)H-thymidine incorporation (up to 200%, P < 0.05), while all peptides stimulated differentiation (up to 300%, P < 0.01) as compared to standard differentiation conditions. In adipocytes, FA uptake was increased in a concentration-dependent manner especially with obestatin (three- to fourfold, P < 0.001) and DAG (three- to fivefold, P < 0.001). By contrast, glucose transport was unchanged. DAG and obestatin significantly decreased lipolysis measured as non-esterified fatty acid and glycerol release by 50%, P < 0.05-0.01 and 51%, P < 0.01, respectively. Interestingly, DAG stimulation of FA uptake was blocked with GHSR1 antagonist (D-lys(3))-GHRP-6 (P < 0.05), phospholipase C inhibitor U73122 and phosphatidylinositol-3-kinase inhibitor wortmannin (P < 0.001). Finally, in omental but not subcutaneous human adipose tissue, GHSR1 correlated with BMI (r = 0.549, P < 0.05) and insulin (r = 0.681, P < 0.01). Taken together, these results suggest that ghrelin-related peptides may directly affect adipose tissue metabolism.
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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".