The Polycomb-dependent epigenome controls β-cell dysfunction, dedifferentiation and diabetes
Bibliographic record
Abstract
SUMMARY Chromatin is the physical template that stabilizes and specifies transcriptional programs. To date, it remains largely unclear to what extent chromatin machinery contributes to the susceptibility and progression of complex diseases. Here, we combined deep epigenome mapping with single cell transcriptomics to mine for evidence of chromatin dysregulation in type-2 diabetes. We identify two chromatin-state signatures that track the trajectory of β-cell dysfunction in mice and humans: ectopic activation of bivalent Polycomb-domains and a loss of expression at a subclass of highly active domains containing key lineage-defining genes. β-cell specific deletion of Polycomb (Eed/PRC2) triggers parallel transcriptional signatures. Intriguingly, these β-cell Eed-knockouts also exhibit highly penetrant hyperglycemia-independent dedifferentiation indicating that Polycomb dysregulation sensitizes the β-cell for dedifferentiation. These findings provide novel resources for exploring transcriptional and epigenetic control of β-cell (dys)function. They identify PRC2 as necessary for long-term maintenance of β-cell identity. The data suggest a two-hit model for loss of β-cell identity in diabetes and highlight epigenetic therapeutic potential to block dedifferentiation.
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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".