Targeting MCOLN1/TRPML1 channels to protect against ischemia-reperfusion injury by restoring the inhibited autophagic flux in cardiomyocytes
Bibliographic record
Abstract
Accumulating evidence suggests that macroautophagy/autophagy dysfunction plays a critical role in myocardial ischemia-reperfusion (I/R) injury. However, the underlying mechanisms responsible for malfunctional autophagy in cardiomyocytes subjected to I/R are poorly understood. As a result, there are no effective therapeutic options that target autophagy to prevent myocardial I/R injury. We recently revealed that MCOLN1/TRPML1, a lysosomal cationic channel, directly contributes to the inhibition of autophagic flux in cardiomyocytes post I/R. We found that MCOLN1 is activated secondary to reactive oxygen species (ROS) elevation following I/R, which in turn induces the release of lysosomal zinc into the cytosol. This ultimately blocks autophagic flux in cardiomyocytes by disrupting the fusion between autophagosomes containing engulfed mitochondria and lysosomes. Furthermore, we discovered that the MCOLN1-mediated inhibition of autophagy induced by I/R impairs mitochondrial function, which results in further detrimental ROS release that directly contributes to cardiomyocyte death. More importantly, restoration of blocked autophagic flux in cardiomyocytes subjected to I/R achieved by blocking MCOLN1 channels significantly rescues cardiomyocyte death in vitro and greatly improves cardiac function of mice subjected to I/R in vivo. Therefore, targeting MCOLN1 represents a novel therapeutic strategy to protect against myocardial I/R injury.Abbreviations: I/R: ischemia-reperfusion; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MCOLN1/TRPML1: mucolipin TRP cation channel 1; ROS: reactive oxygen species; SQSTM1/p62: sequestosome 1.
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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".