A Neuroligin Isoform Translated by circNlgn Contributes to Cardiac Remodeling
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it