Liver kinase <scp>B1</scp> ( <scp>LKB1</scp> ) regulates the epigenetic landscape of mouse pancreatic beta cells
Bibliographic record
Abstract
Liver kinase B1 (LKB1/STK11) is an important regulator of pancreatic β-cell identity and function. Elimination of Lkb1 from the β-cell results in improved glucose-stimulated insulin secretion and is accompanied by profound changes in gene expression, including the upregulation of several neuronal genes. The mechanisms through which LKB1 controls gene expression are, at present, poorly understood. Here, we explore the impact of β cell-selective deletion of Lkb1 on chromatin accessibility in mouse pancreatic islets. To characterize the role of LKB1 in the regulation of gene expression at the transcriptional level, we combine these data with a map of islet active transcription start sites and histone marks. We demonstrate that LKB1 elimination from β-cells results in widespread changes in chromatin accessibility, correlating with changes in transcript levels. Changes occurred in hundreds of promoter and enhancer regions, many of which were close to neuronal genes. We reveal that dysregulated enhancers are enriched in binding motifs for transcription factors (TFs) important for β-cell identity, such as FOXA, MAFA or RFX6, and we identify microRNAs (miRNAs) that are regulated by LKB1 at the transcriptional level. Overall, our study provides important new insights into the epigenetic mechanisms by which LKB1 regulates β-cell identity and function.
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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".