Metformin Inhibits Mammalian Target of Rapamycin–Dependent Translation Initiation in Breast Cancer Cells
Bibliographic record
Abstract
Metformin is used for the treatment of type 2 diabetes because of its ability to lower blood glucose. The effects of metformin are explained by the activation of AMP-activated protein kinase (AMPK), which regulates cellular energy metabolism. Recently, we showed that metformin inhibits the growth of breast cancer cells through the activation of AMPK. Here, we show that metformin inhibits translation initiation. In MCF-7 breast cancer cells, metformin treatment led to a 30% decrease in global protein synthesis. Metformin caused a dose-dependent specific decrease in cap-dependent translation, with a maximal inhibition of 40%. Polysome profile analysis showed an inhibition of translation initiation as metformin treatment of MCF-7 cells led to a shift of mRNAs from heavy to light polysomes and a concomitant increase in the amount of 80S ribosomes. The decrease in translation caused by metformin was associated with mammalian target of rapamycin (mTOR) inhibition, and a decrease in the phosphorylation of S6 kinase, ribosomal protein S6, and eIF4E-binding protein 1. The effects of metformin on translation were mediated by AMPK, as treatment of cells with the AMPK inhibitor compound C prevented the inhibition of translation. Furthermore, translation in MDA-MB-231 cells, which lack the AMPK kinase LKB1, and in tuberous sclerosis complex 2 null (TSC2(-/-)) mouse embryonic fibroblasts was unaffected by metformin, indicating that LKB1 and TSC2 are involved in the mechanism of action of metformin. These results show that metformin-mediated AMPK activation leads to inhibition of mTOR and a reduction in translation initiation, thus providing a possible mechanism of action of metformin in the inhibition of cancer cell growth.
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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) | 0.001 | 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".