Histone acetyltransferase 7 (KAT7)-dependent intragenic histone acetylation regulates endothelial cell gene regulation
Bibliographic record
Abstract
Although the functional role of chromatin marks at promoters in mediating cell-restricted gene expression has been well characterized, the role of intragenic chromatin marks is not well understood, especially in endothelial cell (EC) gene expression. Here, we characterized the histone H3 and H4 acetylation profiles of 19 genes with EC-enriched expression via locus-wide chromatin immunoprecipitation followed by ultra-high–resolution (5 bp) tiling array analysis in ECs versus non-ECs throughout their genomic loci. Importantly, these genes exhibit differential EC enrichment of H3 and H4 acetylation in their promoter in ECs versus non-ECs. Interestingly, VEGFR-2 and VEGFR-1 show EC-enriched acetylation across broad intragenic regions and are up-regulated in non-ECs by histone deacetylase inhibition. It is unclear which histone acetyltransferases (KATs) are key to EC physiology. Depletion of KAT7 reduced VEGFR-2 expression and disrupted angiogenic potential. Microarray analysis of KAT7-depleted ECs identified 263 differentially regulated genes, many of which are key for growth and angiogenic potential. KAT7 inhibition in zebrafish embryos disrupted vessel formation and caused loss of circulatory integrity, especially hemorrhage, all of which were rescued with human KAT7. Notably, perturbed EC-enriched gene expression, especially the VEGFR-2 homologs, contributed to these vascular defects. Mechanistically, KAT7 participates in VEGFR-2 transcription by mediating RNA polymerase II binding, H3 lysine 14, and H4 acetylation in its intragenic region. Collectively, our findings support the importance of differential histone acetylation at both promoter and intragenic regions of EC genes and reveal a previously underappreciated role of KAT7 and intragenic histone acetylation in regulating VEGFR-2 and endothelial 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) | 0.002 | 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".