Abstract 77: MiR-125b Regulates Myofibroblast Transition and Cardiac Fibrosis
Bibliographic record
Abstract
Transforming growth factor-β (TGF-β)-induced fibroblast-to-myofibroblast transition (FMT) is a critical determinant of cardiac fibrosis. However, the contribution of microRNAs leading to TGF-β-induced FMT and cardiac fibrosis are not well-understood. Our results elucidate that blocking the canonical TGF-β pathway protects from FMT in primary cultures of human cardiac fibroblasts and that miR-125b is significantly upregulated during cardiac FMT. Furthermore, we observed significant upregulation of miR-125b in fibrotic human myocardium and two murine models of cardiac fibrosis. Importantly, we discovered that miR-125b is sufficient to induce cardiac FMT. In contrast, the knockdown of miR-125b using an antagomir approach attenuated TGF-β-induced FMT. In silico analysis and biochemical analysis revealed that miR-125b directly targets multiple anti-fibrotic mediators including p53 and apelin. In addition, miR-125b also plays a potent role in the regulation of fibroblast proliferation, an important cause of cardiac fibrosis. Finally, miR-125b was successfully inhibited in vivo by the systemic delivery of locked nucleic acid (LNA) targeted against miR-125b both in the presence and absence of Angiotensin II (Ang II). These results demonstrated that LNA-125b protected against Ang II-induced proliferation and fibrosis in the mouse heart in vivo. We conclude that TGF-β-induced miR-125b is an important regulator of both fibroblast proliferation and FMT, and miR-125b inhibits key anti-fibrotic mediators to promote cardiac fibrosis. We propose that miR-125b may serve as a novel therapeutic target for the preventative therapy for 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".