A Novel Circular RNA circITGa9 Predominantly Generated in Human Heart Disease Induces Cardiac Remodeling and Fibrosis
Bibliographic record
Abstract
Recent studies have highlighted the pivotal roles of circular RNAs (circRNAs) in cardiovascular diseases. Through high-throughput circRNA sequencing of both normal myocardial tissues and hypertrophic patients, we unveiled 32,034 previously undiscovered circRNAs with distinct cardiac expression patterns. Notably, circITGa9, a circRNA derived from integrin-α9, exhibited substantial up-regulation in cardiac hypertrophy patients. This elevation was validated across extensive sample pools from cardiac patients and donors. In vivo experiments revealed heightened cardiac fibrosis in mice subjected to transverse aortic constriction (TAC) after circITGa9 injection. We identified circITGa9 binding proteins through circRNA precipitation followed by liquid chromatography tandem-mass spectrometry. Furthermore, circRNA pull-down/precipitation assays demonstrated that increased circITGa9 expression facilitated binding with tropomyosin 3 (TPM3). Specific binding sites between circITGa9 and TPM3 were identified through computational algorithms and further validated by site-directed mutagenesis. We further showed that circITGa9 induced actin polymerization, characteristic of tissue fibrosis. Finally, we developed approaches that improved cardiac function and decreased fibrosis by delivering small interfering RNA targeting circITGa9 or blocking oligo inhibiting the interaction of circITGa9 and TPM3 into TAC mice, which is amenable for further preclinical and translational development. We conclude that elevated circITGa9 levels drive cardiac remodeling and fibrosis. By pinpointing circITGa9 as a therapeutic target, we open doors to innovative interventions for mitigating cardiac remodeling and 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) | 0.001 | 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".