ALY688 Attenuates Iron-Induced ER Stress and Insulin Resistance via Activation of ER-Phagy
Bibliographic record
Abstract
Iron overload (IO) is a common contributing factor to aspects of the metabolic syndrome (MetS), including insulin resistance. Mechanisms of IO-induced insulin resistance include elevated oxidative stress, endoplasmic reticulum (ER) stress and impaired autophagy. Using an Akt biosensor L6 skeletal muscle cell line, we found that the adiponectin receptor agonist ALY688 prevented impaired insulin signaling in response to IO. Mechanistically, ALY688 counteracted IO-dependent effects on ER stress, the unfolded protein response (UPR), and autophagic flux. Importantly, we found that ALY688 induced FAM134B-dependent ER-phagy (reticulophagy) to ameliorate ER stress. The beneficial effects of ALY688 were attenuated in cells lacking Atg7 or FAM134B, highlighting the importance of selective autophagy of the ER by FAM134B in mitigating IO-induced impaired insulin signaling. These findings translated to a mouse model of IO in which ALY688 improved glucose tolerance, insulin sensitivity, UPR activation, FAM134B expression, and autophagy flux. Collectively, our results demonstrate that ALY688 effectively attenuated IO-induced ER stress and insulin resistance in both mice and cellular skeletal muscle models via stimulation of the UPR and ER-phagy. ARTICLE HIGHLIGHTS: This study adds mechanistic insight to the association between excess iron and insulin resistance and identifies an effective intervention strategy. Using a cellular skeletal muscle cell model and a preclinical animal model, we show that iron elicits endoplasmic reticulum (ER) stress and impairs insulin signaling. The adiponectin receptor agonist peptide ALY688 counteracts iron-induced ER stress and maintains insulin sensitivity. Loss-of-function approaches indicated that ALY688 acts via an autophagy-dependent, and specifically ER-phagy-dependent, mechanism.
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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 | 0.001 |
| 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".