Glycine receptor activation promotes pancreatic islet cell proliferation via the PI3K/mTORC1/p70S6K pathway
Bibliographic record
Abstract
Glycine and β-alanine activate glycine receptors (GlyRs), with glycine known to enhance insulin secretion from pancreatic islet β cells, primarily through GlyR activation. However, the effects of GlyR activation on β cell proliferation have not been examined. Here, we aim to investigate the potential proliferative effects of glycine and β-alanine on islets. In vitro experiments on mouse and human islets revealed that glycine and β-alanine, via GlyR activation, stimulated the proliferation of β cells and α cells, without affecting insulin or glucagon secretion. Further analysis indicated the involvement of the PI3K/mTORC1/p70S6K signaling pathway in this process. Inhibition of GlyRs and PI3K/mTORC1/p70S6K signaling attenuated proliferative effects of glycine and β-alanine. In vivo and ex vivo studies supported these findings, showing increased β and α cell mass after 12 weeks of oral administration of glycine and β-alanine, with no changes in insulin secretion or glucose homeostasis under normal conditions. However, during an acute insulin resistance induced by insulin receptor antagonist S961, glycine and β-alanine enhanced insulin secretion and reduced blood glucose levels by increasing β cell secretory capacity. These findings demonstrate glycine and β-alanine in vivo and in vitro promote islet cell proliferation via GlyR activation and the PI3K/mTORC1/p70S6K pathway, potentially providing a target to enhance islet capacity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".