The RNA-binding protein QKI governs a muscle-specific alternative splicing program that shapes the contractile function of cardiomyocytes
Bibliographic record
Abstract
AIMS: In the heart, splicing factors orchestrate the functional properties of cardiomyocytes by regulating the alternative splicing of multiple genes. Work in embryonic stem cells has shown that the splicing factor Quaking (QKI) regulates alternative splicing during cardiomyocyte differentiation. However, the relevance and function of QKI in adult cardiomyocytes remains unknown. In this study, we aim to identify the in vivo function of QKI in the adult mouse heart. METHODS AND RESULTS: We generated mice with conditional deletion of QKI in cardiomyocytes by the Cre-Lox system. Mice with cardiomyocyte-specific deletion of QKI died during the foetal period (E14.5), without obvious anatomical abnormalities of the heart. Adult mice with tamoxifen-inducible QKI deletion rapidly developed heart failure associated with severe disruption of sarcomeres, already 7 days after knocking out QKI. RNA sequencing revealed that QKI regulates the alternative splicing of more than 1000 genes, including sarcomere and cytoskeletal components, calcium-handling genes, and (post-)transcriptional regulators. Many of these splicing changes corresponded to the loss of muscle-specific isoforms in the heart. Forced overexpression of QKI in cultured neonatal rat ventricular myocytes directed these splicing events in the opposite direction and enhanced contractility of cardiomyocytes. CONCLUSION: Altogether, our findings show that QKI is an important regulator of the muscle-specific alternative splicing program that builds the contractile apparatus of cardiomyocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".