Activation of Transcription Factor Nrf2 Signalling by the Sphingosine Kinase Inhibitor SKI-II Is Mediated by the Formation of Keap1 Dimers
Bibliographic record
Abstract
BACKGROUND: Anti-oxidant capacity is crucial defence against environmental or endogenous oxidative stress. Nuclear factor erythroid 2-related factor 2 (Nrf2) is a redox-sensitive transcription factor that plays a key defensive role against oxidative and cytotoxic stress and cellular senescence. However, Nrf2 signalling is impaired in several aging-related diseases, such as chronic pulmonary obstructive disease (COPD), cancer, and neurodegenerative diseases. Thus, novel therapeutics that enhance Nrf2 signalling are an attractive approach to treat these diseases. METHODOLOGY/PRINCIPAL FINDINGS: Nrf2 was stabilized by SKI-II (2-(p-hydroxyanilino)-4-(p-chlorophenyl) thiazole), which is a known sphingosine kinase inhibitor, in human bronchial epithelial cell line, BEAS2B, and in primary human bronchial epithelial cells, leading to enhancement of anti-oxidant proteins, such as HO-1, NQO1 and GCLM. The activation of Nrf2 was achieved by the generation of inactive dimerized form of Keap1, a negative regulator of Nrf2 expression, which was independent of sphingosine kinase inhibition. Using mice that were exposed to cigarette smoke, SKI-II induced Nrf2 expression together with HO-1 in their lungs. In addition, SKI-II reduced cigarette smoke mediated oxidative stress, macrophages and neutrophil infiltration and markers of inflammation in mice. CONCLUSIONS/SIGNIFICANCE: SKI-II appears to be a novel activator of Nrf2 signalling via the inactivation of Keap1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
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".