Enhanced activation of cellular AMPK by dual-small molecule treatment: AICAR and A769662
Bibliographic record
Abstract
AMP-activated protein kinase (AMPK) is a key cellular energy sensor and regulator of metabolic homeostasis. Activation of AMPK provides beneficial outcomes in fighting against metabolic disorders such as insulin resistance and type 2 diabetes. Currently, there is no allosteric AMPK activator available for the treatment of metabolic diseases, and limited compounds are available to robustly stimulate cellular/tissue AMPK in a specific manner. Here we investigated whether simultaneous administration of two different pharmacological AMPK activators, which bind and act on different sites, would result in an additive or synergistic effect on AMPK and its downstream signaling and physiological events in intact cells. We observed that cotreating primary hepatocytes with the AMP mimetic 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside (AICAR) and a low dose (1 μM) of the allosteric activator A769662 produced a synergistic effect on AMPK Thr172 phosphorylation and catalytic activity, which was associated with a more profound increase/decrease in phosphorylation of downstream AMPK targets and inhibition of hepatic lipogenesis compared with single-compound treatment. Mechanistically, we found that cotreatment does not stimulate LKB1, upstream kinase for AMPK, but it protects against dephosphorylation of Thr172 phosphorylation by protein phosphatase PP2Cα in an additive manner in a cell-free assay. Collectively, we demonstrate that AICAR sensitizes the effect of A769662 and promotes AMPK activity and its downstream events. The study demonstrates the feasibility of promoting AMPK activity by using two activators with distinct modes of action in order to achieve a greater activation of AMPK and downstream signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".