RFamide Peptides 43RFa and 26RFa Both Promote Survival of Pancreatic β-Cells and Human Pancreatic Islets but Exert Opposite Effects on Insulin Secretion
Bibliographic record
Abstract
RFamide peptides 43RFa and 26RFa have been shown to promote food intake and to exert different peripheral actions through G-protein-coupled receptor 103 (GPR103) binding. Moreover, 26RFa was found to inhibit pancreatic insulin secretion, whereas the role of 43RFa on β-cell function is unknown, as well as the effects of both peptides on β-cell survival. Herein, we investigated the effects of 43RFa and 26RFa on survival and apoptosis of pancreatic β-cells and human pancreatic islets. In addition, we explored the role of these peptides on insulin secretion and the underlying signaling mechanisms. Our results show that in INS-1E β-cells and human pancreatic islets both 43RFa and 26RFa prevented cell death and apoptosis induced by serum starvation, cytokine synergism, and glucolipotoxicity, through phosphatidylinositol 3-kinase/Akt- and extracellular signal-related kinase 1/2-mediated signaling. Moreover, 43RFa promoted, whereas 26RFa inhibited, glucose- and exendin-4-induced insulin secretion, through Gαs and Gαi/o proteins, respectively. Inhibition of GPR103 expression by small interfering RNA blocked 43RFa insulinotropic effect, but not the insulinostatic action of 26RFa. Finally, 43RFa, but not 26RFa, induced cAMP increase and glucose uptake. In conclusion, because of their survival effects along with the effects on insulin secretion, these findings suggest potential for 43RFa and 26RFa as therapeutic targets in the treatment of diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".