GABA requires GLP-1R to exert its pancreatic function during STZ challenge
Bibliographic record
Abstract
Gamma-aminobutyric acid (GABA) administration attenuates streptozotocin (STZ)-induced diabetes in rodent models with unclear underlying mechanisms. We found that GABA and Sitagliptin possess additive effect on pancreatic β-cells, which prompted us to ask the existence of common or unique targets of GLP-1 and GABA in pancreatic β-cells. Effect of GABA on expression of thioredoxin-interacting protein (TxNIP) was assessed in the INS-1 832/13 (INS-1) cell line, WT and GLP-1R-/- mouse islets. GABA was also orally administrated in STZ-challenged WT or GLP-1R-/- mice, followed by immunohistochemistry assessment of pancreatic islets. Effect of GABA on Wnt pathway effector β-catenin (β-cat) was examined in INS-1 cells, WT and GLP-1R-/- islets. We found that GABA shares a common feature with GLP-1 on inhibiting TxNIP, while this function was attenuated in GLP-1R-/- islets. In WT mice with STZ challenge, GABA alleviated several 'diabetic syndromes', associated with increased β-cell mass. These features were virtually absent in GLP-1R-/- mice. Knockdown TxNIP in INS-1 cells increased GLP-1R, Pdx1, Nkx6.1 and Mafa levels, associated with increased responses to GABA or GLP-1 on stimulating insulin secretion. Cleaved caspase-3 level can be induced by high-glucose, dexamethasone, or STZ in INS-1 cell, while GABA treatment blocked the induction. Finally, GABA treatment increased cellular cAMP level and β-cat S675 phosphorylation in WT but not GLP-1R-/- islets. We, hence, identified TxNIP as a common target of GABA and GLP-1 and suggest that, upon STZ or other stress challenge, the GLP-1R-cAMP-β-cat signaling cascade also mediates beneficial effects of GABA in pancreatic β-cell, involving TxNIP reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".