Transcriptomics of angiotensin II-induced long noncoding and coding RNAs in endothelial cells
Bibliographic record
Abstract
OBJECTIVE: Angiotensin II (Ang II)-induced endothelial dysfunction plays an important role in the pathogenesis of cardiovascular diseases such as systemic hypertension, cardiac hypertrophy and atherosclerosis. Recently, long noncoding RNAs (lncRNAs) have been shown to play an essential role in the pathobiology of cardiovascular diseases; however, the effect of Ang II on lncRNAs and coding RNAs expression in endothelial cells has not been evaluated. Accordingly, we sought to evaluate the expression profiles of lncRNAs and coding RNAs in endothelial cells following treatment with Ang II. METHODS: Human umbilical vein endothelial cells (HUVECs) were cultured and treated with Ang II (10-6 mol/l) for 24 h. The cells were then profiled for the expression of lncRNAs and mRNAs using the Arraystar Human lncRNA Expression Microarray V3.0. RESULTS: In HUVECs following Ang II treatment, from a total of 30 584 lncRNA targets screened, 25 targets were significantly upregulated, while 69 were downregulated. In the same HUVECs samples, from 26 106 mRNA targets screened, 28 targets were significantly upregulated and 67 were downregulated. Of the differentially expressed lncRNAs, RP11-354P11.2 and RP11-360F5.1 were the most upregulated (11-fold) and downregulated (three-fold) lncRNAs, respectively. Assigning the differentially regulated genes into functional groups using bioinformatics reveals numerous genes involved in the nucleotide excision repair and ECM-receptor interaction. CONCLUSION: This is the first study to profile the Ang II-induced differentially expressed lncRNAs and mRNAs in human endothelial cells. Our results reveal novel targets and substantially extend the list of potential candidate genes involved in Ang II-induced endothelial dysfunction and cardiovascular diseases.
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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 | 0.001 |
| 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".