Natural (dihydro)phenanthrene plant compounds are direct activators of AMPK through its allosteric drug and metabolite–binding site
Bibliographic record
Abstract
AMP-activated protein kinase (AMPK) is a central energy sensor that coordinates the response to energy challenges to maintain cellular ATP levels. AMPK is a potential therapeutic target for treating metabolic disorders, and several direct synthetic activators of AMPK have been developed that show promise in preclinical models of type 2 diabetes. These compounds have been shown to regulate AMPK through binding to a novel allosteric drug and metabolite (ADaM)-binding site on AMPK, and it is possible that other molecules might similarly bind this site. Here, we performed a high-throughput screen with natural plant compounds to identify such direct allosteric activators of AMPK. We identified a natural plant dihydrophenathrene, Lusianthridin, which allosterically activates and protects AMPK from dephosphorylation by binding to the ADaM site. Similar to other ADaM site activators, Lusianthridin showed preferential activation of AMPKβ1-containing complexes in intact cells and was unable to activate an AMPKβ1 S108A mutant. Lusianthridin dose-dependently increased phosphorylation of acetyl-CoA carboxylase in mouse primary hepatocytes, which led to a corresponding decrease in de novo lipogenesis. This ability of Lusianthridin to inhibit lipogenesis was impaired in hepatocytes from β1 S108A knock-in mice and mice bearing a mutation at the AMPK phosphorylation site of acetyl-CoA carboxylase 1/2. Finally, we show that activation of AMPK by natural compounds extends to several analogs of Lusianthridin and the related chemical series, phenanthrenes. The emergence of natural plant compounds that regulate AMPK through the ADaM site raises the distinct possibility that other natural compounds share a common mechanism of regulation.
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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 | 0.000 |
| 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".