Abnormal regulation of glucagon secretion by human islet alpha cells in the absence of beta cells
Bibliographic record
Abstract
BACKGROUND: The understanding of the regulation of glucagon secretion by pancreatic islet α-cells remains elusive. We aimed to develop an in vitro model for investigating the function of human α-cells under direct influence of glucose and other potential regulators. METHODS: Highly purified human α-cells from islets of deceased donors were re-aggregated in the presence or absence of β-cells in culture, evaluated for glucagon secretion under various treatment conditions, and compared to that of intact human islets and non-sorted islet cell aggregates. FINDINGS: The pure human α-cell aggregates maintained proper glucagon secretion capability at low concentrations of glucose, but failed to respond to changes in ambient glucose concentration. Addition of purified β-cells, but not the secreted factors from β-cells at low or high concentrations of glucose, partly restored the responsiveness of α-cells to glucose with regulated glucagon secretion. The EphA stimulator ephrinA5-fc failed to mimic the inhibitory effect of β-cells on glucagon secretion. Glibenclamide inhibited glucagon secretion from islets and the α- and β-mixed cell-aggregates, but not from the α-cell-only aggregates, at 2.0 mM glucose. INTERPRETATION: This study validated the use of isolated and then re-aggregated human islet cells for investigating α-cell function and paracrine regulation, and demonstrated the importance of cell-to-cell contact between α- and β-cells on glucagon secretion. Loss of proper β- and α-cell physical interaction in islets likely contributes to the dysregulated glucagon secretion in diabetic patients. Re-aggregated select combinations of human islet cells provide unique platforms for studying islet cell function and regulation.
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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 | 0.001 | 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 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".