Adiponectin improves insulin sensitivity via activation of autophagic flux
Bibliographic record
Abstract
Skeletal muscle insulin resistance is known to play an important role in the pathogenesis of diabetes, and one potential causative cellular mechanism is endoplasmic reticulum (ER) stress. Adiponectin mediates anti-diabetic effects via direct metabolic actions and by improving insulin sensitivity, and we recently demonstrated an important role in stimulation of autophagy by adiponectin. However, there is limited knowledge on crosstalk between autophagy and ER stress in skeletal muscle and in particular how they are regulated by adiponectin. Here, we utilized the model of high insulin/glucose (HIHG)-induced insulin resistance, determined by measuring Akt phosphorylation (T308 and S473) and glucose uptake in L6 skeletal muscle cells. HIHG reduced autophagic flux measured by LC3 and p62 Western blotting and tandem fluorescent RFP/GFP-LC3 immunofluorescence (IF). HIHG also induced ER stress assessed by thioflavin T/KDEL IF, pIRE1, pPERK, peIF2α and ATF6 Western blotting and induction of a GRP78-mCherry reporter. Induction of autophagy by adiponectin or rapamycin attenuated HIHG-induced ER stress and improved insulin sensitivity. The functional significance of enhanced autophagy was validated by demonstrating a lack of improved insulin sensitivity in response to adiponectin in autophagy-deficient cells generated by overexpression of dominant negative mutant of Atg5. In summary, adiponectin-induced autophagy in skeletal muscle cells alleviated HIHG-induced ER stress and insulin resistance.
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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".