Abstract 199: Specific MicroRNAs Regulate Cardiac Fibroblast-to-Myofibroblast Transition And Fibrosis.
Bibliographic record
Abstract
Cardiac fibrosis is the pathological consequence of fibroblast-to-myofibroblast transition (FMT) within the human myocardium, resulting in heart dysfunction. Transforming growth factor-β (TGF-β) plays a pivotal role in the induction of both FMT and cardiac fibrosis. However, the molecular basis of TGF-β-induced FMT in cardiac fibrosis is not clear. In this study, we propose a novel miRNA-mediated approach to attenuate cardiac fibrosis by blocking TGF-β-induced FMT. We observed that the canonical TGF-β/SMAD pathway, and not the MEK pathway, plays a pivotal role in the induction of FMT in primary cultures of human cardiac fibroblasts. Importantly, we have demonstrated that the specific miRNA is significantly upregulated during cardiac FMT. In addition, we observed significant upregulation of the same miRNA in fibrotic human myocardium and two murine models of cardiac fibrosis (transverse aortic constriction and Angiotensin II). Furthermore, overexpression of the miRNA using mimics augmented TGF-β-induced FMT. Downregulation of the miRNA using an antagomiR approach attenuated TGF-β-induced FMT. Notably, in silico analysis and qRT-PCR analysis revealed that this miRNA directly targets apelin, an anti-fibrotic mediator. Next, efficient delivery of cy3-tagged antagomiRs in the heart, liver and spleen was confirmed by confocal microscopy. In vivo silencing of miRNAs in the heart was achieved by systemic delivery of locked nucleic acid (LNA), both in the presence and absence of Angiotensin II. We conclude that TGF-β-induced specific miRNA is both sufficient and necessary for the induction of cardiac FMT and is a novel repressor of apelin. Our data suggests that the inhibition of miRNAs necessary for FMT may serve as a novel therapeutic strategy to prevent human cardiac fibrosis.
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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 | 0.002 |
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".