A novel blood-based bioassay to monitor adiponectin signaling
Bibliographic record
Abstract
The diverse beneficial effects of adiponectin-receptor signaling, including its impact on the regulation of inflammatory processes in vivo, have resulted in development of adiponectin receptor agonists as a treatment for metabolic disorders. However, there are no established non-invasive bioassays for detection of adiponectin target engagement in humans or animal models. Here, we designed an assay using small amounts of blood to assess adiponectin action. Specifically, we tested effects of the small 10-amino acid peptide adiponectin receptor agonist, ALY688, in a sublethal LPS endotoxemia model in mice. LPS-induced pro-inflammatory cytokine levels in serum were significantly reduced in mice treated with ALY688, assessed via multiplex ELISA in flow cytometry. Furthermore, ALY688 alone significantly induced TGF-β release in serum 1 h after treatment and was elevated for up to 24 h. Additionally, using a flow-cytometry panel for detection of changes in circulating immune cell phenotypes, we observed a significant increase in absolute T cell counts in mice after ALY688 treatment. To assess changes in intracellular signaling effectors downstream of adiponectin, phospho-flow cytometry was conducted. There was a significant increase in phosphorylation of AMPK and p38-MAPK in mice after ALY688 treatment. We then used human donor immune cells (PBMCs) treated with ALY688 ex vivo and observed elevation of AMPK and p38-MAPK phosphorylation from baseline in response to ALY688. Together, these results indicate we can detect adiponectin action on immune cells in vivo by assessing adiponectin signaling pathway for AMPK and p38-MAPK, as well as pro-inflammatory cytokine levels. This new approach provides a blood-based bioassay for screening adiponectin action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".