Salubrinal promotes phospho-eIF2α-dependent activation of UPR leading to autophagy-mediated attenuation of iron-induced insulin resistance
Bibliographic record
Abstract
Identification of new mechanisms mediating insulin sensitivity is important to allow validation of corresponding therapeutic targets. In this study, we first used a cellular model of skeletal muscle cell iron overload and found that endoplasmic reticulum (ER) stress and insulin resistance occurred after iron treatment. Insulin sensitivity was assessed using cells engineered to express an Akt biosensor, based on nuclear FoxO localization, as well as western blotting for insulin signaling proteins. Use of salubrinal to elevate eIF2α phosphorylation and promote the unfolded protein response (UPR) attenuated iron-induced insulin resistance. Salubrinal induced autophagy flux and its beneficial effects on insulin sensitivity were not observed in autophagy-deficient cells generated by overexpressing a dominant-negative ATG5 mutant or via knockout of ATG7. This indicated the beneficial effect of salubrinal-induced UPR activation was autophagy-dependent. We translated these observations to an animal model of systemic iron overload-induced skeletal muscle insulin resistance where administration of salubrinal as pretreatment promoted eIF2α phosphorylation, enhanced autophagic flux in skeletal muscle and improved insulin responsiveness. Together, our results show that salubrinal elicited an eIF2α-autophagy axis leading to improved skeletal muscle insulin sensitivity both in vitro and in mice.
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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".