Role of Mitochondrial Iron Overload in Mediating Cell Death in H9c2 Cells
Bibliographic record
Abstract
Iron overload (IO) is associated with cardiovascular diseases, including heart failure. Our study's aim was to examine the mechanism by which IO triggers cell death in H9c2 cells. IO caused accumulation of intracellular and mitochondrial iron as shown by the use of iron-binding fluorescent reporters, FerroOrange and MitoFerroFluor. Expression of cytosolic and mitochondrial isoforms of Ferritin was also induced by IO. IO-induced iron accumulation and cellular ROS was rapid and temporally linked. ROS accumulation was detected in the cytosol and mitochondrial compartments with CellROX, DCF-DA and MitoSOX fluorescent dyes and partly reversed by the general antioxidant N-acetyl cysteine or the mitochondrial antioxidant SkQ1. Antioxidants also reduced the downstream activation of apoptosis and lytic cell death quantified by Caspase 3 cleavage/activation, mitochondrial Cytochrome c release, Annexin V/Propidium iodide staining and LDH release of IO-treated cells. Finally, overexpression of MitoNEET, an outer mitochondrial membrane protein involved in the transfer of Fe-S clusters between mitochondrial and cytosol, was observed to lower iron and ROS accumulation in the mitochondria. These alterations were correlated with reduced IO-induced cell death by apoptosis in MitoNEET-overexpressing cells. In conclusion, IO mediates H9c2 cell death by causing mitochondrial iron accumulation and subsequent general and mitochondrial ROS upregulation.
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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) | 0.001 | 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".