γ‐aminobutyric acid stimulates β‐cell proliferation through the <scp>mTORC1</scp> / <scp>p70S6K</scp> pathway, an effect amplified by <scp>Ly49</scp> , a novel γ‐aminobutyric acid type A receptor positive allosteric modulator
Bibliographic record
Abstract
Abstract Aim To examine the mechanism of action of γ‐aminobutyric acid (GABA) on β‐cell proliferation and investigate if co‐treatment with Ly49, a novel GABA type A receptor positive allosteric modulator (GABA A ‐R PAM), amplifies this effect. Methods Human or mouse islets were co‐treated for 4‐5 days with GABA and selected receptor or cell signalling pathway modulators. Immunofluorescence was used to determine protein co‐localization, cell number or proliferation, and islet size. Osmotic minipumps were surgically implanted in mice to assess Ly49 effects on pancreatic β‐cells. Results Amplification of GABA A ‐R signalling enhanced GABA‐stimulated β‐cell proliferation in cultured mouse islets. Co‐treatment of GABA with an inhibitor specific for PI3K, mTORC1/2, or p70S6K, abolished GABA‐stimulated β‐cell proliferation in mouse and human islets. Nuclear p‐Akt Ser473 and p‐p70S6K Thr421/Ser424 expression in pancreatic β‐cells was increased in GABA‐treated mice compared with vehicle‐treated mice, an effect augmented with GABA and Ly49 co‐treatment. Mice co‐treated with GABA and Ly49 exhibited enhanced β‐cell area and proliferation compared with GABA‐treated mice. Furthermore, S961 injection (an insulin receptor antagonist) resulted in enhanced plasma insulin in GABA and Ly49 co‐treated mice compared with GABA‐treated mice. Importantly, GABA co‐treated with Ly49 increased β‐cell proliferation in human islets providing a potential application for human subjects. Conclusions We show that GABA stimulates β‐cell proliferation via the PI3K/mTORC1/p70S6K pathway in both mouse and human islets. Furthermore, we show that Ly49 enhances the β‐cell regenerative effects of GABA, showing potential in the intervention of 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".