Iron induces insulin resistance in cardiomyocytes via regulation of oxidative stress
Bibliographic record
Abstract
Iron overload is associated with various pathological changes which contribute to heart failure. Here, we examined mechanisms via which iron alters cardiomyocyte insulin sensitivity. Treatment of primary adult and neonatal cardiomyocytes as well as H9c2 cells with iron decreased insulin sensitivity determined via Western blotting or immunofluorescent detection of Akt and p70S6K phosphorylation and glucose uptake. Using CellROX deep red or DCF-DA probes we also 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 insulin resistance. SKQ1 and allopurinol but not apocynin reduced iron-induced ROS suggesting mitochondria and xanthine oxidase contribute to cellular ROS in response to iron. Western blotting for LC3-I, LC3-II and P62 levels as well as immunofluorescent co-detection of autophagosomes with Cyto-ID and lysosomal cathepsin activity indicated that iron attenuated autophagic flux without altering total expression of Atg7 or beclin-1 and phosphorylation of mTORC1 and ULK1. This conclusion was reinforced via protein accumulation detected using Click-iT HPG labelling after iron treatment. The adiponectin receptor agonist AdipoRon increased autophagic flux and improved insulin sensitivity both alone and in the presence of iron. We created an autophagy-deficient cell model by overexpressing a dominant-negative Atg5 mutant in H9c2 cells and this confirmed that reduced autophagy flux correlated with less insulin sensitivity. In conclusion, our study showed that iron promoted a cascade of ROS production, reduced autophagy and insulin resistance in cardiomyocytes.
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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".