ALY688 elicits adiponectin-mimetic signaling and improves insulin action in skeletal muscle cells
Bibliographic record
Abstract
Adiponectin is well established to mediate many beneficial metabolic effects, and this has stimulated great interest in development and validation of adiponectin receptor agonists as pharmaceutical tools. This study investigated the effects of ALY688, a peptide-based adiponectin receptor agonist, in rat L6 skeletal muscle cells. ALY688 significantly increased phosphorylation of several adiponectin downstream effectors, including AMPK, ACC, and p38MAPK, assessed by immunoblotting and immunofluorescence microscopy. Temporal analysis using cells expressing an Akt biosensor demonstrated that ALY688 enhanced insulin sensitivity. This effect was associated with increased insulin-stimulated Akt and IRS-1 phosphorylation. The functional metabolic significance of these signaling effects was examined by measuring glucose uptake in myoblasts stably overexpressing the glucose transporter GLUT4. ALY688 treatment increased basal glucose uptake and enhanced insulin-stimulated glucose uptake. In the model of high-glucose/high-insulin (HGHI)-induced insulin-resistant cells, both temporal studies using the Akt biosensor as well as immunoblotting to assess Akt and IRS-1 phosphorylation indicated that ALY688 significantly reduced insulin resistance. Importantly, we observed that ALY688 administration to high-fat high-sucrose-fed mice also improves glucose handling, validating its efficacy in vivo. In summary, these data indicate that ALY688 activates adiponectin signaling pathways in skeletal muscle, leading to improved insulin sensitivity and beneficial metabolic effects.
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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) | 0.002 | 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".