Rexinoids Modulate Steroid and Xenobiotic Receptor Activity by Increasing Its Protein Turnover in a Calpain-dependent Manner
Bibliographic record
Abstract
The steroid and xenobiotic receptor SXR (human pregnane X receptor) is a nuclear receptor that plays a key role in the body's detoxification response by regulating genes involved in drug metabolism and transport. SXR ligands include a wide range of compounds, which induce transcription of SXR target genes via activation of a heterodimeric transcription factor consisting of SXR and the related nuclear receptor retinoid X receptor (RXR). We investigated the effect of RXR-selective ligands, rexinoids, on SXR/RXR activity. In agreement with previous reports, we found that rexinoids are weak activators of SXR, but we also found that they can antagonize SXR activation by the potent SXR agonist rifampicin. This antagonism included suppression of rifampicin-induced expression of SXR target genes, as well as reduced binding of SXR/RXR to SXR response elements both in vivo and in vitro. Interestingly, two rexinoids, bexarotene (LGD1069/Targretin) and LG100268, caused a rapid and sustained decrease in the protein levels of both SXR and RXR. The decrease in SXR level was due to an enhanced rate of protein degradation and was dependent on calpain activity, as opposed to rexinoid-induced RXR degradation, which is mediated via the proteasome. Thus, we have demonstrated a novel, rexinoid-modulated mechanism regulating SXR protein stability, which may explain why rexinoids are only weak activators of SXR/RXR-mediated transcription, despite reports that they bind to SXR with high affinity. We suggest that the ability of rexinoids to induce degradation of both SXR and RXR, in combination with competition for binding to SXR, can also explain why rexinoids antagonize the activation of SXR by drugs like rifampicin.
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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) | 0.001 | 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".