Fragrant Unsaturated Aldehydes Elicit Activation of the Keap1/Nrf2 System Leading to the Upregulation of Thioredoxin Expression and Protection Against Oxidative Stress
Bibliographic record
Abstract
Thioredoxin, a key molecule in redox regulation, and many redox enzymes are regulated through the antioxidant responsive element (ARE). To search for antioxidative constituents, we screened extracts from vegetables and found that the extracts of Perilla frutescens and Artemisia princeps have potent thioredoxin-inducing activities. By activity-guided purification of Perilla frutescens extracts, we identified perillaldehyde as a novel thioredoxin inducer. Fragrant unsaturated aldehydes, such as trans-cinnamaldehyde, safranal, 2,4-octadienal, citral, trans-2, cis-6-nonadienal, and trans-2-hexenal showed the ability to activate ARE. Perillaldehyde-induced activation through the ARE was suppressed by the overexpression of wild-type Keap1, whereas sulforaphane-induced activation seemed to be partially suppressed. Mutant Keap1 (R272A/K287A or C273A/C288A) did not suppress this activation. Pretreatment with perillaldehyde reduced the H(2)O(2)-induced cytotoxicity. Thus, we show that fragrant unsaturated aldehydes from edible plants are novel thioredoxin inducers and ARE activators and may be beneficial for protection against oxidative stress-induced cellular damage. These results also suggest that perillaldehyde activates the Nrf2-Keap1 system and that the lysine and arginine residues juxtaposed to the critical cysteine residues of Keap1 are required for signal sensing.
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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) | 0.000 | 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".