HNF1α transcriptional activation and repression maintain human islet α and β cell function
Bibliographic record
Abstract
ABSTRACT HNF1A haploinsufficiency underlies the most common form of human monogenic diabetes (HNF1A-MODY) and hypomorphic HNF1A variants confer type 2 diabetes risk, but a lack of experimental systems has limited our understanding of how the transcription factor HNF1α regulates adult human islet function. Here, we combined human islet genetics, RNA sequencing, Cleavage Under Targets & Release Using Nuclease (CUT&RUN) chromatin mapping, patch-clamp electrophysiology and transplantation-based assays to elucidate HNF1α-regulated mechanisms in mature pancreatic α and β cells. shRNA-mediated suppression of HNF1A in primary human pseudoislets led to blunted insulin output and dysregulated glucagon secretion both in vitro and after transplantation into immunocompromised mice, recapitulating phenotypes observed in HNF1A-MODY patients. These deficits corresponded with altered expression of genes encoding factors critical for hormone secretion, including calcium channel subunits, ATP-transporters and extracellular matrix constituents. Additionally, HNF1A loss led to upregulation of transcriptional repressors, providing evidence for a mechanism of transcriptional de-repression through HNF1α. CUT&RUN mapping of HNF1α DNA-binding sites in primary human islets verified that a subset of HNF1α-regulated genes were direct targets. These data provide unprecedented mechanistic links between HNF1A loss and diabetic phenotypes in mature human α and β cells.
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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 | 0.000 |
| 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".