Autophagy deficiency exacerbates iron overload induced reactive oxygen species production and apoptotic cell death in skeletal muscle cells
Bibliographic record
Abstract
Iron overload is associated with various pathological changes which contribute to metabolic syndrome, many of which have been proposed to occur via damaging tissue through an excessive amount of reactive oxygen species (ROS) production. In this study, we established a model of iron overload in L6 skeletal muscle cells and observed that iron enhanced cytochrome c release from depolarized mitochondria, assayed by immunofluorescent colocalization of cytochrome c with Tom20 and the use of JC-1, respectively. This subsequently elevated apoptosis, determined via use of a caspase-3/7 activatable fluorescent probe and western blotting for cleaved caspase-3. Using CellROX deep red and mBBr, we observed that iron increased generation of reactive oxygen species (ROS), and that pretreatment with the superoxide dismutase mimetic MnTBAP reduced ROS production and attenuated iron-induced intrinsic apoptosis and cell death. Furthermore, using MitoSox Red we observed that iron enhanced mROS and the mitochondria-targeted anti-oxidant SKQ1 reduced iron-induced ROS generation and cell death. Western blotting for LC3-II and P62 levels as well as immunofluorescent detection of autophagy flux with LC3B and P62 co-localization indicated that iron acutely (2-8 h) activated and later (12-24 h) attenuated autophagic flux. We used autophagy-deficient cell models generated by overexpressing a dominant-negative Atg5 mutant or CRISPR-mediated ATG7 knock out to test the functional significance of autophagy and observed that autophagy-deficiency exacerbated iron-induced ROS production and apoptosis. In conclusion, our study showed that high iron levels promoted ROS production, blunted the self-protective autophagy response and led to cell death in L6 skeletal muscle cells.
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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) | 0.001 | 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".