Serotonin activates glycolysis and mitochondria biogenesis in human breast cancer cells through activation of the Jak1/STAT3/ERK1/2 and adenylate cyclase/PKA, respectively
Bibliographic record
Abstract
Abstract Background Although produced by several types of tumours, the role of serotonin on cancer biology is yet to be understood. Methods The effects of serotonin (5-HT) on human breast cancer cells proliferation, signalling pathways and metabolic profile were evaluated by cytometry, western blotting, qPCR, enzymology and confocal microscopy. Results Our results revealed that incubation of MCF-7 cells with 10 µM 5-HT increased cell growth rate by 28%, an effect that was prevented by the 5-HTR 2A/C antagonist, ketanserin. Conversely, increasing concentrations of 5-HT promoted glucose consumption and lactate production by MCF-7 cells. We also showed that increased glucose metabolism is provoked by the upregulation of pyruvate kinase M2 (PKM2) isoform through 5-HTR 2A/C -triggered activation of Jak1/STAT3 and ERK1/2 subcellular pathways. However, we noticed a decrease in the rate of produced lactate per consumed glucose as a function of the hormone concentration, suggesting a disruption of the Warburg effect. The latter effect is due to 5-HTR 2A/C -dependent mitochondrial biogenesis and metabolism, which is triggered by adenylyl cyclase/PKA, enhancing the oxidation of lactate within these cells. Conclusions We showed that serotonin, through 5-HTR 2A/C , interferes with breast cancer cells proliferation and metabolism by triggering two distinct signalling pathways: Jak1/STAT3 that boosts glycolysis through upregulation of PKM2, and adenylyl cyclase/PKA that enhances mitochondrial biogenesis.
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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".