Epigenetics of the vascular endothelium
Bibliographic record
Abstract
Classical models of transcription in vascular endothelial cells, specifically the cis/trans paradigm, have limitations. For instance, how does the environment have chronic effects on gene expression in endothelial cells after weeks or years? When an endothelial cell divides, how is this information transmitted to daughter cells? Epigenetics refers to chromatin-based pathways important in the regulation of gene expression and includes three distinct, but highly interrelated, mechanisms: DNA methylation, histone density and posttranslational modifications, and RNA-based mechanisms. Together they offer a newer perspective on transcriptional control paradigms in vascular endothelial cells and provide a molecular basis for understanding how the environment impacts the genome to modify disease susceptibility. This alternative viewpoint for transcriptional regulation allows a reassessment of the cis/trans model and even helps explain some of its limitations. This review provides an introduction to epigenetic concepts for vascular biologists and uses topical examples in cell biology to provide insight into how cell types or even whole organisms, such as monozygotic human twins with the same DNA sequence, can exhibit heterogeneous patterns of gene expression, phenotype, or diseases prevalence. Using endothelial nitric oxide synthase (NOS3) as an example, we examine the growing body of evidence implicating epigenetic pathways in the control of vascular endothelial gene expression in health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".