Dietary isoflavone daidzein promotes Tfam expression that increases mitochondrial gene expression in C2C12 muscle cells
Bibliographic record
Abstract
Mitochondrial dysfunction in muscles leads to a wide range of metabolic and age-related disorders. Recently, it has been reported that a natural polyphenol, resveratrol, affects mitochondrial biogenesis. This study aimed to identify other natural polyphenolic compounds that regulate mitochondrial biogenesis in muscles. For this purpose, we used the C2C12 murine muscle cell line. Screening involved a reporter assay based on the promoter of mitochondrial transcription factor A (Tfam). We found that several polyphenols exhibited the ability to increase Tfam promoter activity and that the soy isoflavone daidzein was a most potent candidate that regulated mitochondrial biogenesis. When C2C12 myotubes were treated with 25–50 μM daidzein for 24 h, there were significant increases in the expression of Tfam and mitochondrial genes such as COX1 and Cytb as well as the mitochondrial content. Using several mutant Tfam promoter fragments, we found that the transcription factor, nuclear respiratory factor (NRF) and its coactivator, PGC1α, were necessary for the effect of daidzein on Tfam expression. Finally, silencing of sirtuin-1 (SIRT1) by shRNA resulted in inhibition of the daidzein effects on mitochondrial gene expression. In conclusion, daidzein regulates mitochondrial biogenesis in muscle cells by regulating transcriptional networks through a SIRT1-associated pathway. These results suggest that daidzein would be beneficial to protect against a wide range of diseases caused by muscle mitochondrial dysfunction.
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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 | 0.001 |
| 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".