Hepatocyte Nuclear Factor 4‐Alpha Is Essential for the Active Epigenetic State at Enhancers in Mouse Liver
Bibliographic record
Abstract
Cell-fate determination is influenced by interactions between master transcription factors (TFs) and cis-regulatory elements. Hepatocyte nuclear factor 4 alpha (HNF4A), a liver-enriched TF, acts as a master controller in specification of hepatic progenitor cells by regulating a network of TFs to control onset of hepatocyte cell fate. Using analysis of genome-wide histone modifications, DNA methylation, and hydroxymethylation in mouse hepatocytes, we show that HNF4A occupies active enhancers in hepatocytes and is essential for active histone and DNA signatures, especially acetylation of lysine 27 of histone 3 (H3K27ac) and 5-hydroxymethylcytosine (5hmC). In mice lacking HNF4A protein in hepatocytes, we observed a decrease in both H3K27ac and hydroxymethylation at regions bound by HNF4A. Mechanistically, HNF4A-associated hydroxymethylation (5hmC) requires its interaction with ten-eleven translocation methylcytosine dioxygenase 3 (TET3), a protein responsible for oxidation from 5mC to 5hmC. Furthermore, HNF4A regulates TET3 expression in liver by directly binding to an enhancer region. Conclusion: In conclusion, we identified that HNF4A is required for the active epigenetic state at enhancers that amplifies transcription of genes in hepatocytes.
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 | 0.001 |
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".