Aryl Hydrocarbon Receptor Nuclear Translocator/Hypoxia-inducible Factor-1β Plays a Critical Role in Maintaining Glucose-stimulated Anaplerosis and Insulin Release from Pancreatic β-Cells
Bibliographic record
Abstract
The metabolic pathways that are involved in regulating insulin secretion from pancreatic β-cells are still incompletely understood. One potential regulator of the metabolic phenotype of β-cells is the transcription factor aryl hydrocarbon receptor nuclear translocator (ARNT)/hypoxia-inducible factor (HIF)-1β. ARNT/HIF-1β levels are profoundly reduced in islets obtained from type 2 diabetic patients. However, no study to date has investigated key pathways involved in regulating insulin release in β-cells that lack ARNT/HIF-1β. In this study, we confirm that siRNA-mediated knockdown of ARNT/HIF-1β inhibits glucose-stimulated insulin secretion. We next investigated the metabolic consequence of the loss of ARNT/HIF-1β knockdown. We demonstrate that β-cells with reduced ARNT/HIF-1β expression levels exhibit a 31% reduction in glycolytic flux without significant changes in glucose oxidation or the ATP:ADP ratio. Metabolic profiling of β-cells treated with siRNAs against the ARNT/HIF-1β gene revealed that glycolysis, anaplerosis, and glucose-induced fatty acid production were down-regulated, and all are key events involved in glucose-stimulated insulin secretion. In addition, both first and second phase insulin secretion in islets were significantly reduced after ARNT/HIF-1β knockdown. Together, our data suggest an important role for ARNT/HIF-1β in anaplerosis, and it may play a critical role in maintaining normal secretion competence of β-cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".