Endothelin-1 Regulation Is Entangled in a Complex Web of Epigenetic Mechanisms in Diabetes
Bibliographic record
Abstract
Endothelial cells (ECs) are primary targets of glucose-induced tissue damage. As a result of hyperglycemia, endothelin-1 (ET-1) is upregulated in organs affected by chronic diabetic complications. The objective of the present study was to identify novel transcriptional mechanisms that influence ET-1 regulation in diabetes. We carried out the investigation in microvascular ECs using multiple approaches. ECs were incubated with 5 mM glucose (NG) or 25 mM glucose (HG) and analyses for DNA methylation, histone methylation, or long non-coding RNA- mediated regulation of ET-1 mRNA were then performed. DNA methylation array analyses demonstrated the presence of hypomethylation in the proximal promoter and 5' UTR/first exon regions of EDN1 following HG culture. Further, globally blocking DNA methylation or histone methylation significantly increased ET-1 mRNA expressions in both NG and HG-treated HRECs. While, knocking down the pathogenetic lncRNAs ANRIL, MALAT1, and ZFAS1 subsequently prevented the glucose-induced upregulation of ET-1 transcripts. Based on our past and present findings, we present a novel paradigm that reveals a complex web of epigenetic mechanisms regulating glucose-induced transcription of ET-1. Improving our understanding of such processes may lead to better targeted therapies.
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 | 0.000 |
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".