<scp>MitoNEET</scp> preserves muscle insulin sensitivity during iron overload by regulating mitochondrial iron, reactive oxygen species and fission
Bibliographic record
Abstract
Iron overload (IO) is known to contribute to metabolic dysfunctions such as type 2 diabetes and insulin resistance. Using L6 skeletal muscle cells overexpressing the CDGSH iron-sulfur domain-containing protein 1 (CISD1, also known as mitoNEET) (mitoN) protein, we examined the potential role of MitoN in preventing IO-induced insulin resistance. In L6 control cells, IO resulted in insulin resistance which could be prevented by MitoN as demonstrated by western blot of p-Akt and Akt biosensor cells. Mechanistically, IO increased; mitochondrial iron accumulation, mitochondrial reactive oxygen species (ROS), Fis1-dependent mitochondrial fission, mitophagy, FUN14 domain-containing protein 1 (FUNDC1) expression, and decreased Parkin. MitoN overexpression was able to reduce increases in mitochondrial iron accumulation, mitochondrial ROS, mitochondrial fission, mitophagy and FUNDC1 upregulation due to IO. MitoN did not have any effect on the IO-induced downregulation of Parkin. MitoN alone also upregulated peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC1α) protein levels, a master regulator of mitochondrial biogenesis. The use of mitochondrial antioxidant, Skq1, or fission inhibitor, Mdivi-1, prevented IO-induced insulin resistance implying both mitochondrial ROS and fission play a causal role in the development of insulin resistance. Taken together, MitoN is able to confer protection against IO-induced insulin resistance in L6 skeletal muscle cells through regulation of mitochondrial iron content, mitochondrial ROS, and mitochondrial fission.
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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) | 0.005 | 0.001 |
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".