ZEB1-AS1 as a TRPML1 Inhibitor to Cause Lysosome Dysfunction and Cardiac Damage in Aged Mice
Bibliographic record
Abstract
The prevalence of cardiovascular diseases (CVDs) has increased markedly as the world population has aged. Long non-coding RNAs (lncRNAs) have been reported as novel regulators in diverse pathophysiological conditions. Here, we performed RNA sequencing (RNA-seq) and observed that the lncRNA Zeb1os1 (zinc finger E-box binding homeobox 1, opposite strand 1), which is known as ZEB1-AS1 (zinc finger E-box binding homeobox 1 antisense 1) in humans, was upregulated in the aged mice hearts, senescent cardiomyocytes, and human blood from elderly individuals. The human blood ZEB1-AS1 level was positively relevant to human age but negatively relevant to peak E to peak A (E/A). Silencing Zeb1os1 ameliorated diastolic dysfunction and cardiac senescence in aged mice. On the other hand, Zeb1os1 overexpression triggered cardiac dysfunction resembling that observed in aged mice. Mechanistically, we provide compelling evidence that Zeb1os1 interacts with the transient receptor potential mucolipin 1 (TRPML1) for ubiquitination (UB)-mediated degradation. This process inhibits lysosomal Ca 2+ efflux, impairing lysosome function. In addition, the functional domain of Zeb1os1, which contains the key nucleotides responsible for the pro-senescence property of full-length Zeb1os1 in cardiomyocytes. Together, these data suggest that Zeb1os1 is a potential target for ameliorating lysosomal dysfunction and aging-related cardiac impairment.
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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) | 0.002 | 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".