Abstract MP60: Down-regulated Mir-338-3p In Subcutaneous Small Arteries Of Hypertensive Patients With Chronic Kidney Disease Targets Protein Tyrosine Phosphatase Receptor Type S And Glutathione Peroxidase 3
Bibliographic record
Abstract
Background: Hypertension (HTN) causes vascular injury identified by endothelial dysfunction, vascular stiffening, and remodeling, which contributes to kidney damage leading to chronic kidney disease (CKD). MicroRNAs (miRNAs) repress/degrade target mRNAs. Their role in vascular injury in HTN remains unclear. We aimed to identify differentially expressed (DE) miRNAs in gluteal subcutaneous arteries of patients with HTN associated or not with CKD to shed light on the pathophysiological molecular mechanisms. Methods: Normotensive subjects and patients with HTN associated or not with CKD grades 3-4 were studied (n=15-16). Small arteries were isolated from gluteal subcutaneous biopsies, RNA extracted and small and total RNA sequencing performed by Illumina HiSeq-2500. DE genes were identified with a P <0.05 and fold change (FC) >1.3. Top 3 DE miRNAs ( P <0.001, FC>2, mean read count number (MRCN) >3,000 in all groups and having predicted mRNA targets) were selected for validation by reverse transcription-quantitative PCR (RT-qPCR). The mRNA targets of the top selected miRNA were predicted by TargetScan with P <0.01, FC>1.5 and MRCN>150 and the top 9 targets were validated by RT-qPCR using gain- and loss-of-function in human aortic endothelial cells (HAECs). Gene ontology enrichment analysis (GOEA) was done in Cytoscape. Results: DE miRNAs and mRNAs were identified uniquely associated with HTN (miRNAs: 10, mRNAs: 68), CKD (miRNAs: 68, mRNAs: 395), and in both groups (miRNAs: 2, mRNAs: 32). miR-338-3p presented the best correlation between RNA sequencing and RT-qPCR (R 2 =0.328, P <0.001) among the top 3 DE miRNAs. Two of the selected top 9 miR-338-3p predicted targets were validated in HAECs. Protein tyrosine phosphatase receptor type S ( PTPRS , FC: 0.80±0.08 vs 1.00±0.00) and glutathione peroxidase 3 ( GPX3 , FC: 0.88±0.04 vs 1.00±0.00) were down-regulated in HAECs transfected with miR-338-3p mimics ( P <0.05). GOEA showed association of GPX3 with oxidative stress detoxification ( q <0.05), and of PTPRS with the immune system, neuronal system and developmental process ( q <0.001). Conclusion: Down-regulated miR-338-3p in gluteal subcutaneous small arteries of hypertensive patients with CKD targets PTPRS and GPX3 that may play a role in vascular injury in HTN.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".