Omega-3 polyunsaturated fatty acid promotes the inhibition of glycolytic enzymes and mTOR signaling by regulating the tumor suppressor LKB1
Bibliographic record
Abstract
The omega-3 polyunsaturated fatty acids (ω3PUFAs) are a class of lipids biologically effective for the treatment of inflammatory disorders, cardiovascular disease and cancer. Patients consuming a high dietary intake of ω3PUFAs have shown a low incidence of metabolic disorders, including cancer. Although the effects of ω3PUFAs intake was shown to be involved in the prevention and treatment of these diseases, the underlying molecular mechanisms involved are not well understood. Here, we show that ω3PUFA, docosahexaenoic acid (DHA) enhanced the tumor suppressor function of LKB1. We observed that when LKB1 expressing cells are treated with DHA, there is an increase in LKB1 activity leading to phosphorylation of AMPK and inhibition of mTOR signaling. Abrogation of LKB1 in MCF-7 cells by siRNA reversed this phenotype. Furthermore, cellular metabolism was altered and ATP levels were reduced in response to DHA treatment, which was further attenuated in cells expressing LKB1. More importantly, in mammary epithelial cells expressing LKB1, the rate of glycolysis was decreased as a result of diminished expression of glycolytic enzymes. Functionally, these events lead to a decrease in the migration potential of these cells. Overall, our discovery shows for the first time that LKB1 function is enhanced in response to ω3PUFA treatment, thereby resulting in the regulation of cell metabolism.
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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) | 0.002 | 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".