Curcumin alters EpRE and AP‐1 binding complexes and elevates glutamate‐cysteine ligase gene expression
Bibliographic record
Abstract
Dietary use of curcumin, the active component of tumeric, one of the most widely used spices, is linked to several beneficial health effects, although the underlying molecular mechanisms remain largely unknown. Correlations have been established between curcumin exposure and increases in enzymes for glutathione synthesis, particularly glutamate-cysteine ligase (GCL), and metabolism as well as glutathione content, suggesting the eliciting of an adaptive response to stress. In this study, using HBE1 cells, we found that the mechanism of curcumin-induced GCL elevation occurred via transcription of the two Gcl genes. Gcl transcription has been shown in several systems to be mediated through binding of transcription factor complexes to TRE and EpRE elements. Studies herein showed that curcumin caused modest but sustained increases in binding of proteins to DNA sequences for both cis elements but, more importantly, altered the compositions and nuclear content of proteins in these complexes. Curcumin exposure increased JunD and c-Jun content in AP-1 complexes and increased JunD while decreasing MafG/MafK in EpRE complexes. Thus, the beneficial effects elicited by curcumin appear to be due to changes in the pool of transcription factors that compose EpRE and AP-1 complexes, affecting gene expression of GCL and other phase II enzymes.
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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".