A Neuroligin Isoform Translated by circNlgn Contributes to Cardiac Remodeling
Bibliographic record
Abstract
Background: Fibrotic cardiac remodeling is a maladaptive response to acute or chronic injury that leads to arrhythmia and progressive heart failure. The underlying mechanisms remain unclear. We performed high-throughput RNA sequencing to analyze circular RNA profile in human cardiac disease and developed transgenic mice to explore the roles of circNlgn. Methods and Results: Using RNA sequencing, we found that circular neuroligin RNA (circNlgn) was highly upregulated in myocardial tissues of patients with selected congenital heart defects with cardiac overload. Back-splicing of the neuroligin gene led to the translation of a circular RNA–derived peptide (Nlgn173) with a 9-amino-acid nuclear localization motif. Binding of this motif to the structural protein LaminB1 facilitated the nuclear localization of Nlgn173. CHIP analysis demonstrated subsequent binding of Nlgn173 to both ING4 (inhibitor of growth protein 4) and C8orf44-SGK3 (serum and glucocorticoid-inducible kinase-3) promoters, resulting in aberrant collagen deposition, cardiac fibroblast proliferation, and reduced cardiomyocyte viability. Three-dimensional ultrasound imaging of circNlgn-transgenic mice showed impaired left ventricular function, with further impairment when subjected to left ventricular pressure overload compared with WT (wild type) mice. Nuclear translocation of Nlgn173, dysregulated expression of ING4 and C8orf44-SGK3, and immunohistochemical markers of cardiac fibrosis were detected in a panel of 145 patient specimens. Phenotypic changes observed in left ventricular pressure overload and transgenic mice were abrogated with silencing of circNlgn or its targets ING4 and SGK3. Conclusions: We show that a circular RNA can be translated into a novel protein isoform. Dysregulation of this process contributes to fibrosis and heart failure in cardiac overload–induced remodeling. This mechanism may hold therapeutic implications for cardiac disease.
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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) | 0.001 | 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".