Identification of the signals for glucose-induced insulin secretion in INS1 (832/13) β-cells using metformin-induced metabolic deceleration as a model
Bibliographic record
Abstract
Metabolic deceleration in pancreatic β-cells is associated with inhibition of glucose-induced insulin secretion (GIIS), but only in the presence of intermediate/submaximal glucose concentrations. Here, we used acute metformin treatment as a tool to induce metabolic deceleration in INS1 (832/13) β-cells, with the goal of identifying key pathways and metabolites involved in GIIS. Metabolites and pathways previously implicated as signals for GIIS were measured in the cells at 2–25 mm glucose, with or without 5 mm metformin. We defined three criteria to identify candidate signals: 1) glucose-responsiveness, 2) sensitivity to metformin-induced inhibition of the glucose effect at intermediate glucose concentrations, and 3) alleviation of metformin inhibition by elevated glucose concentrations. Despite the lack of recovery from metformin-induced impairment of mitochondrial energy metabolism (glucose oxidation, O 2 consumption, and ATP production), insulin secretion was almost completely restored at elevated glucose concentrations. Meeting the criteria for candidates involved in promoting GIIS were the following metabolic indicators and metabolites: cytosolic NAD + /NADH ratio (inferred from the dihydroxyacetone phosphate:glycerol-3-phosphate ratio), mitochondrial membrane potential, ADP, Ca 2+ , 1-monoacylglycerol, diacylglycerol, malonyl-CoA, and HMG-CoA. On the contrary, most of the purine and nicotinamide nucleotides, acetoacetyl-CoA, H 2 O 2 , reduced glutathione, and 2-monoacylglycerol were not glucose-responsive. Overall these results underscore the significance of mitochondrial energy metabolism-independent signals in GIIS regulation; in particular, the candidate lipid signaling molecules 1-monoacylglycerol, diacylglycerol, and malonyl-CoA; the predominance of K ATP /Ca 2+ signaling control by low ADP·Mg 2+ rather than by high ATP levels; and a role for a more oxidized state (NAD + /NADH) in the cytosol during GIIS that favors high glycolysis rates.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".