Long noncoding RNA MALAT1 enhances the apoptosis of cardiomyocytes through autophagy modulation
Bibliographic record
Abstract
Induction of autophagy promotes cardiomyocyte survival and confers a cardioprotective effect on acute myocardial infarction (AMI). Our previous study showed that knockdown of long noncoding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) attenuated myocardial apoptosis in mouse AMI. Herein, this study further investigated whether the mechanisms by which MALAT1 enhanced cardiomyocyte apoptosis involved the autophagy regulation. To address this, cardiomyocytes were isolated from neonatal mice and then stimulated with hypoxia/reoxygenation (H/R) injury to mimic AMI. The cell apoptosis was evaluated using TUNEL staining and Western blot analysis of apoptosis-related proteins. The autophagy level was assessed using GFP-LC3 immunofluorescence and Western blot analysis of autophagy-related proteins. The results showed that H/R injury increased MALAT1 expression. Furthermore, MALAT1 overexpression significantly enhanced apoptosis and regulated autophagy of cardiomyocytes, whereas MALAT1 knockdown exerted the opposite effect. Moreover, rapamycin (an autophagy activator) effectively attenuated the MALAT1-mediated enhancement of cardiomyocyte apoptosis. Overall, our findings demonstrated that the increased MALAT1 expression induced by H/R injury enhances cardiomyocyte apoptosis, at least in part, through autophagy modulation.
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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".