Abstract P120: Mir-338-3p is Down-regulated in Subcutaneous Small Arteries of Hypertensive Patients With Chronic Kidney Disease
Bibliographic record
Abstract
Background: Hypertension (HTN) and chronic kidney disease (CKD) are among the most prevalent global health conditions that cause millions of deaths per year. They are associated with vascular damage characterized by vascular remodeling, stiffening and endothelial dysfunction. miRNAs (miRs) are a class of small non-coding RNA that regulate gene expression by binding to their target messenger RNAs (mRNAs), thereby leading to mRNA degradation or translational repression. 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 gather insight into pathophysiological molecular mechanisms in these conditions. Methods and Results: Normotensive, HTN [systolic blood pressure (BP) >135 mmHg or diastolic BP of 85-115 mmHg with BpTRU] and CKD subjects (eGFR<60mL/min/m 2 ) (n=15-16) were studied. Small arteries were isolated from subcutaneous gluteal biopsies and RNA extracted for small and total RNA sequencing using Illumina HiSeq-2500. EdgeR identified DE miRs ( P <0.05) uniquely associated with HTN (3 up and 6 down) or CKD (42 up and 39 down) or with both groups (2 down). Reverse transcription-quantitative PCR (RT-qPCR) was used to confirm miRNA differential expression. Correlation between RNA sequencing and RT-qPCR data was demonstrated for 3 miRs from 14 tested: mir-146a-5p (r=0.56, P <10 -4 ), miR-338-3p (r=0.91, P <10 -16 ) and mir-374a-3p (r=0.57, P <10 -4 ). The best correlated DE miRNA, miR-338-3p was down-regulated by 76% ( P <10 -4 ) and uniquely associated with CKD. TargetScan predicted that the up-regulated mRNA encoding glutathione peroxidase 3 ( GPX3 , 1.57 fold, P <0.001) to be a miR-338-3p target. Both miR-338-3p and GPX3 were found to be highly expressed in human aortic endothelial cells (HAECs) by RT-qPCR. Target prediction was validated by showing using RT-qPCR that GPX3 expression was up-regulated 1.73±0.28 fold in HAECs transfected with anti-miR-338-3p ( P <0.05). Conclusion: miR-338-3p down-regulation was found in small arteries, uniquely associated with CKD. GPX3 , which may be important for preservation of endothelial function, is a miR-338-3p potential target. miR-338-3p and associated target may play a role in vascular remodeling in 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 | 0.000 | 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) | 0.004 | 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".