Distinct transcriptomic profile of small arteries of hypertensive patients with chronic kidney disease identified miR-338-3p targeting GPX3 and PTPRS
Bibliographic record
Abstract
OBJECTIVE: Hypertension is associated with vascular injury, which contributes to end-organ damage. MicroRNAs regulating mRNAs have been shown to play a role in vascular injury in hypertensive mice. We aimed to identify differentially expressed microRNAs and their mRNA targets in small arteries of hypertensive patients with/without chronic kidney disease (CKD) to shed light on the pathophysiological molecular mechanisms of vascular remodeling. METHODS AND RESULTS: Normotensive individuals and hypertensive patients with/without CKD were recruited ( n = 15-16 per group). Differentially expressed microRNAs and mRNAs were identified uniquely associated with hypertension (microRNAs: 10, mRNAs: 68) or CKD (microRNAs: 68, mRNAs: 395), and in both groups (microRNAs: 2, mRNAs: 32) with a P less than 0.05 and a fold change less than or greater than 1.3 in subcutaneous small arteries ( n = 14-15). One of the top three differentially expressed microRNAs, miR-338-3p that was down-regulated in CKD, presented the best correlation between RNA sequencing and reverse transcription-quantitative PCR (RT-qPCR, R2 = 0.328, P < 0.001). Profiling of human aortic vascular cells showed that miR-338-3p was mostly expressed in endothelial cells. Two of the selected top nine up-regulated miR-338-3p predicted targets, glutathione peroxidase 3 ( GPX3 ) and protein tyrosine phosphatase receptor type S ( PTPRS ), were validated with mimics by RT-qPCR in human aortic endothelial cells ( P < 0.05) and by a luciferase assay in HEK293T cells ( P < 0.05). CONCLUSION: A distinct transcriptomic profile was observed in gluteal subcutaneous small arteries of hypertensive patients with CKD. Down-regulated miR-338-3p could contribute to GPX3 and PTPRS up-regulation via the canonical microRNA targeting machinery in hypertensive patients with CKD. http://links.lww.com/HJH/C27.
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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 | 0.000 | 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".