The muscle-specific microRNAs <i>miR-1</i> and <i>miR-133</i> produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The microRNAs miR-1 and miR-133 are preferentially expressed in cardiac and skeletal muscles and have been shown to regulate differentiation and proliferation of these cells. We report here a novel aspect of cellular function of miR-1 and miR-133 regulation of cardiomyocyte apoptosis. miR-1 and miR-133 produced opposing effects on apoptosis, induced by oxidative stress in H9c2 rat ventricular cells, with miR-1 being pro-apoptotic and miR-133 being anti-apoptotic. miR-1 level was significantly increased in response to oxidative stress. We identified single target sites for miR-1 only, in the 3'-untranslated regions of the HSP60 and HSP70 genes, and multiple putative target sites for miR-133 throughout the sequence of the caspase-9 gene. miR-1 reduced the levels of HSP60 and HSP70 proteins without changing their transcript levels, whereas miR-133 did not affect HSP60 and HSP70 expression at all. By contrast, miR-133 repressed caspase-9 expression at both the protein and mRNA levels. The post-transcriptional repression of HSP60 and HSP70 and caspase-9 was further confirmed by luciferase reporter experiments. Our results indicate that miR-1 and miR-133 are involved in regulating cell fate with increased miR-1 and/or decreased miR-133 levels favoring apoptosis and decreased miR-1 and/or miR-133 levels favoring survival. Post-transcriptional repression of HSP60 and HSP70 by miR-1 and of caspase-9 by miR-133 contributes significantly to their opposing actions.
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 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".