Doxorubicin impairs cardiomyocyte viability by suppressing transcription factor EB expression and disrupting autophagy
Bibliographic record
Abstract
Doxorubicin (DOX) is an effective anti-cancer agent. However, DOX treatment increases patient susceptibility to dilated cardiomyopathy. DOX predisposes cardiomyocytes to insult by suppressing mitochondrial energy metabolism, altering calcium flux, and disrupting proteolysis and proteostasis. Prior studies have assessed the role of macroautophagy in DOX cardiotoxicity; however, limited studies have examined whether DOX mediates cardiac injury through dysfunctions in inter- and/or intra-lysosomal signaling events. Lysosomal signaling and function is governed by transcription factor EB (TFEB). In the present study, we hypothesized that DOX caused myocyte injury by impairing lysosomal function and signaling through negative regulation of TFEB. Indeed, we found that DOX repressed cellular TFEB expression, which was associated with impaired cathepsin proteolytic activity across in vivo, ex vivo, and in vitro models of DOX cardiotoxicity. Furthermore, we observed that loss of TFEB was associated with reduction in macroautophagy protein expression, inhibition of autophagic flux, impairments in lysosomal cathepsin B activity, and activation of cell death. Restoration and/or activation of TFEB in DOX-treated cardiomyocytes prevented DOX-induced suppression of cathepsin B activity, reduced DOX-mediated reactive oxygen species (ROS) overproduction, attenuated activation of caspase-3, and improved cellular viability. Collectively, loss of TFEB inhibits lysosomal autophagy, rendering cardiomyocytes susceptible to DOX-induced proteotoxicity and injury. Our data reveal a novel mechanism wherein DOX primes cardiomyocytes for cell death by depleting cellular TFEB.
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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 | 0.000 |
| 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 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".