MitoNEET prevents iron overload‐induced insulin resistance in H9c2 cells through regulation of mitochondrial iron
Bibliographic record
Abstract
Iron overload (IO) induces insulin resistance in H9c2 cardiomyoblast cells. Here, we used H9c2 cells overexpressing MitoNEET to examine the potential for protection against iron accumulation in the mitochondria and subsequent insulin resistance. In control H9c2 cells, IO was observed to increase mitochondrial iron content, reactive oxygen species (ROS) production, mitochondrial fission, and reduced insulin-stimulated Akt and ERK1/2 phosphorylation. IO did not significantly affect mitophagy, or mitochondrial content, however, an increase in peroxisome-proliferator-activated receptor gamma coactivator 1 alpha (PGC1α) protein expression, a key regulator of mitochondrial biogenesis, was observed. MitoNEET overexpression was able to attenuate the effects of IO on mitochondrial iron content, reactive oxygen species, mitochondrial fission, and insulin signaling. MitoNEET overexpression also upregulated levels of PGC1α protein. The mitochondria-targeted antioxidant, Skq1, prevented IO-induced ROS production and insulin resistance in control cells, indicating mitochondrial ROS plays a causal role in the onset of insulin resistance. The selective mitochondrial fission inhibitor, Mdivi-1, prevented IO-induced mitochondrial fission, however, it did not alleviate IO-induced insulin resistance. Collectively, IO causes insulin resistance in H9c2 cardiomyoblasts and this can be averted by reduction of mitochondrial iron accumulation and ROS production by overexpression of the MitoNEET protein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".