Abstract MP44: A Novel And 2 Known Differentially Expressed MicroRNAs Were Identified In Peripheral Blood Mononuclear Cells Of Patients With Hypertension Associated With Metabolic Syndrome
Bibliographic record
Abstract
Background: Hypertension (HTN) is associated with subclinical target organ damage including cardiac, vascular and kidney injury. The immune system plays a role in hypertension and target organ damage. Activation of T cells has been reported among peripheral blood mononuclear cells (PBMCs) of patients with HTN. MicroRNAs (miRNAs) are crucial post-transcriptional regulators of immune cells. Whether miRNAs play a role in the activation of immune cells in hypertension complicated by target organ damage in humans remains unknown. We aimed to address this question by identifying differentially expressed (DE) miRNAs and their mRNA targets in PBMCs of patients with hypertension complicated or not with metabolic syndrome (MetS) or chronic kidney disease (CKD). Methods: Normotensive subjects and patients with hypertension (HTN) associated or not with at least 2 other features of MetS or CKD were studied (n=15-16). PBMCs were isolated from blood, RNA extracted for small and total RNA sequencing (RNA-seq) using Illumina HiSeq-2500 and data were analyzed using a systems biology approach. MiRDeep2 was used for novel miRNAs prediction, miRNA annotation and counting. TargetScan 7.07 was used to predict DE miRNA targets with weighted context score percentile >50%. DE genes miRNAs and mRNAs were identified with fold change (FC) >1.5 and P <0.005. DE miRNAs with FC>2 and mean read count number (MRCM) >500, and with predicted targets with MRCM>300 were validated by reverse transcription-quantitative PCR (RT-qPCR). Results: DE miRNAs, mRNAs and non-coding RNAs were identified in HTN (22, 19 and 0), MetS (57, 401 and 11) and CKD (6, 26 and 2) compared to NTN. TargetScan predicted that 7 miRNAs target 3 mRNAs in NTN, 57 miRNAs target 55 mRNAs in MetS and 3 miRNAs target 2 mRNAs in CKD. DE miR-409-5p (FC: 0.54±0.10 vs 1.00±0.09, P <0.05), miR-411-5p (FC: 0.40±0.06, vs 1.00±0.11, P <0.001) and the novel miR-pl-86 (FC: 1.96±0.17 vs 1.00±0.15, P <0.05) in MetS vs NTN were validated by RT-qPCR. RNA-seq data were correlated with RT-qPCR for miR-409-5p (R 2 =0.40, P <2.4E-07, n=55), miR-411-5p (R 2 =0.55, P <1.1E-10, n=55), miR-pl-86 (R 2 =0.37, P <5.5E-07, n=56). Conclusion: This study showed that DE miR-409-5p, miR-411-5p and miR-pl-86 may play a role in HTN associated with MetS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".