Abstract P159: MicroRNA Profiling in Small Resistance Arteries of Hypertensive Patients With or Without Chronic Kidney Disease
Bibliographic record
Abstract
Background: Hypertension (HTN) and chronic kidney disease (CKD) are global health disorders that are epidemiologically associated. Vascular injury is an early manifestation in HTN and contributes to CKD. It is characterized by endothelial dysfunction and vascular remodeling that are accompanied by gene expression changes. MicroRNAs (miRs) are important non-coding RNA regulators of gene expression. Dysregulation of miRs has been shown in HTN and CKD, but their implication in vascular injury remains unclear. We aimed to identify differentially expressed (DE) miRs in small arteries of HTN and CKD human subjects to get further insight into pathophysiological molecular mechanisms in these conditions. Methods and Results: Normotensive, HTN (systolic blood pressure (BP) > 135 mm Hg or diastolic BP of 85-115 mm Hg with BpTRU) and CKD subjects (estimated glomerular filtration rate <60mL/min/m 2 ) (n=15-16) were studied. Small arteries were dissected from a subcutaneous gluteal biopsy under RNAse free condition and used for total RNA extraction with the mirVana miR isolation kit. cDNA libraries were prepared using the TruSeq small RNA prep kit and the TruSeq stranded total RNA prep kit, and sequenced by Illumina HiSeq 2500. DE miRs and DE mRNAs ( P <0.05) were identified using EdgeR, which found 3 up- and 6 down-regulated miRs, as well as 134 up- and 149 down-regulated mRNAs uniquely associated with HTN, 42 up- and 39 down-regulated miRs, as well as 743 up- and 348 down-regulated mRNAs uniquely associated with CKD, while 2 up-regulated miRs and 101 up- and 75 down-regulated mRNAs were found in both groups. Target Scan was used to predict DE miR targets in the DE mRNAs. Enrichment analysis showed that the HTN-associated DE miR-targeting DE mRNAs were highly enriched in gene ontology (GO) terms involved in peptidase activity, mitochondrial activity and immune response ( q <0.01), while the CKD-associated DE miR-targeting DE genes were highly enriched in GO terms involved in tube formation, fibroblast proliferation and EGF response ( q <0.001). Conclusions: DE miRs were identified in small arteries of HTN and CKD patients. Enrichment analysis in DE miR-targeting DE mRNAs revealed GO terms that could be linked to different degrees of vascular changes in HTN and CKD.
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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 | 0.001 | 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) | 0.003 | 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".