Multiple Phytoestrogens Inhibit Cell Growth and Confer Cytoprotection By Inducing Manganese Superoxide Dismutase Expression
Bibliographic record
Abstract
Phytoestrogens are of interest because of their reported beneficial effects on many human maladies including cancer, neurodegeneration, cardiovascular disease and diabetes. As data on phytoestrogens continues to accumulate, it is clear that there is significant overlap in the cellular effects elicited by these various compounds. Here, we show that one mechanism by which a number of phytoestrogens achieve their growth inhibitory and cytoprotective effects is via induction of the mitochondrial manganese superoxide dismutase (MnSOD). Eight phytoestrogens, including resveratrol, coumestrol, kaempferol, genistein, daidzein, apigenin, isoliquirtigenin and glycitin, were tested for their ability to induce MnSOD expression in mouse C2C12 and primary myoblasts. Five of these, resveratrol, coumestrol, kaempferol, genistein and daidzein, significantly increased MnSOD expression, slowed proliferative growth and enhanced stress resistance (hydrogen peroxide LD50) . When siRNA was used to prevent the MnSOD induction by genistein, coumestrol or daidzein, none of these compounds exerted any effect on proliferative growth, and only the effect of coumestrol on stress resistance persisted. The estrogen antagonist ICI182780 prevented the increased MnSOD expression and also the changes in cell growth and stress resistance, indicating that these effects are mediated by estrogen receptors (ER). The absence of effects of resveratrol or coumestrol, but not genistein, in ERβ-null cells further indicated that this ER in particular is important in mediating these effects. Thus, an ER-mediated induction of MnSOD expression appears to underlie the growth inhibitory and cytoprotective activities of multiple phytoestrogens.
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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".