Abstract P625: Non-coding Rna Regulation Of Gene Expression In Angiotensin Ii-induced Vascular Damage
Bibliographic record
Abstract
Introduction: Non-coding RNAs (ncRNAs), including long ncRNAs (lncRNAs) and microRNAs (miRs), account for ~98% of the transcribed RNAs. They have been shown to play a role in cardiovascular disease. Vascular damage is an early manifestation and a cause of end-organ damage in hypertension. However, it is unknown whether ncRNAs are involved in the development of vascular injury in hypertension. We hypothesize that ncRNA regulation participates in mechanisms of vascular remodeling and plays an important role in the pathophysiology of hypertension. Methods and Results: Ten-week old male C57BL/6 mice were infused or not with angiotensin (Ang) II for 14 days. Systolic blood pressure (BP) determined by telemetry was increased by Ang II infusion compared to control (146±8 vs 113±5 mmHg, P<0.001). Total RNA was extracted from mesenteric arteries for total and small RNA deep sequencing using Illumina HiSeq-2500. Sequences were aligned to the mm10 genome with STAR, annotated and counted using HTSeq-count or miRDeep2. Differential expression analysis was done in R. Differentially expressed (DE) mRNAs (550 up & 266 down), lncRNAs (7 up & 42 down), miRs (23 up & 12 down) were identified in the Ang II-treated group (1.5 fold change, q<0.05). Targetscan was used to predict interactions between DE miRs and the inversely correlated DE mRNAs or DE lncRNAs. MEME Suite was used to predict DE transcription factor binding sites in the promoter region of genes encoding DE mRNAs, lncRNAs and miRs. Cytoscape was used to construct molecular networks integrating the above interactions and the gene expression profile and to perform functional enrichment analysis, which revealed enrichment of extracellular matrix and developmental processes in DE miR-targeting DE mRNAs (q<1E-20). Ten DE miRNAs whose expression levels correlated (P<0.05) with BP were identified, 9 of which are located in a single miRNA cluster that is conserved in humans. Conclusions: We have identified a conserved miRNA cluster that may play a pivotal role in the regulation of vascular damage in hypertension. A sub-network of genes that participates in the interaction between the miRNA cluster and other BP-correlated RNAs was selected for future investigation to identify therapeutic targets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".