Data from Berberine Modifies Cysteine 179 of IκBα Kinase, Suppresses Nuclear Factor-κB–Regulated Antiapoptotic Gene Products, and Potentiates Apoptosis
Bibliographic record
Abstract
Abstract Berberine, an isoquinoline alkaloid derived from a plant used traditionally in Chinese and Ayurvedic medicine, has been reported to exhibit chemopreventive and anti-inflammatory activities through unknown mechanism. Because of the critical role of the transcription factor nuclear factor-κB (NF-κB) in these processes, we investigated the effect of berberine on this pathway. We found that berberine suppressed NF-κB activation induced by various inflammatory agents and carcinogens. This alkaloid also suppressed constitutive NF-κB activation found in certain tumor cells. Suppression of NF-κB activation occurred through the inhibition of phosphorylation and degradation of IκBα by the inhibition of IκB kinase (IKK) activation, leading to suppression of phosphorylation and nuclear translocation of p65, and finally to inhibition of NF-κB reporter activity. Inhibition of IKK by berbeine was direct and could be reversed by reducing agents. Site-specific mutagenesis suggested the involvement of cysteine residue 179 in IKK. Berberine also suppressed the expression of NF-κB–regulated gene products involved in antiapoptosis (Bcl-xL, Survivin, IAP1, IAP2, and cFLIP), proliferation (cyclin D1), inflammation (cyclooxygenase-2), and invasion (matrix metalloproteinase-9). Suppression of antiapoptotic gene products correlated with enhancement of apoptosis induced by tumor necrosis factor (TNF)-α and chemotherapeutic agents and with inhibition of TNF-induced cellular invasion. Overall, our results indicate that chemopreventive, apoptotic, and anti-inflammatory activities displayed by berberine may be mediated in part through the suppression of the NF-κB activation pathway. This may provide the molecular basis for the ability of berberine to act as an anticancer and anti-inflammatory agent. [Cancer Res 2008;68(13):5370–9]
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".