Abstract P158: MicroRNA Profiling in Peripheral Blood Mononuclear Cells From 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. The immune system has been shown to play a role in HTN and CKD in animal models. Activation of T cells has been observed in 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 HTN and CKD in humans remains unknown. We aimed to address this question by identifying differentially expressed (DE) miRNAs and their mRNA targets in PBMCs of HTN and CKD patients. 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. PBMCs were isolated from 30 ml of whole blood and used for total RNA extraction with the mirVana miRNA isolation kit. cDNA libraries were prepared using the TruSeq small RNA prep kit and the TruSeq stranded total RNA prep kit, and were sequenced by Illumina HiSeq 2500. DE miRNAs ( P <0.05) were identified using EdgeR, which found 41 up- and 38 down-regulated miRNAs, as well as 101 up- and 316 down-regulated mRNAs uniquely associated with the hypertensive group, while 11 up- and 18 down-regulated miRNAs, as well as 153 up- and 73 down-regulated mRNAs were found uniquely associated with the CKD group. Meanwhile, 4 up- and 8 down-regulated miRNAs, as well as 13 up- and 19 down-regulated mRNAs were found in both groups. Target Scan was used to predict DE miRNA targets in the DE mRNAs. Enrichment analysis showed that the HTN-associated DE miRNA-targeting DE genes were highly enriched in gene ontology (GO) terms involved in cytosolic transport, protein kinase B (PKB) signalling and RNA 3’ processing ( q <0.001), while the CKD-associated DE miRNA-targeting DE genes were highly enriched in GO terms involved in immune response, ribosomal process and metal ion homeostasis ( q <0.001). Conclusions: DE miRNAs were identified in PBMCs of HTN and CKD patients. Enrichment analysis in DE miRNA-targeting DE mRNAs revealed GO terms that could be linked to different degrees of immune cell activation 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 | 0.000 | 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) | 0.003 | 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".