Indirect activation of the <scp>SV23</scp> and <scp>SV24</scp> splice variants of human constitutive androstane receptor: analysis with 3‐hydroxyflavone and its analogues
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Naturally occurring splice variants of human CAR (hCAR), including hCAR-SV23 (insertion of amino acids SPTV) and hCAR-SV24 (APYLT), have been shown to be expressed in liver. However, little is known regarding how hCAR-SV23 and hCAR-SV24 are activated. Therefore, we investigated the mode of activation of these hCAR splice variants. EXPERIMENTAL APPROACH: Cell-based reporter gene assays, including ligand-binding domain transactivation assays and coactivator recruitment assays, were conducted on cultured HepG2 cells transfected with various constructs and treated with 3-hydroxyflavone or a hydroxylated (galangin, datiscetin, kaempferol, morin, quercetin or myricetin) or methylated (isorhamnetin, tamarixetin, or syringetin) analogue. KEY RESULTS: Among the flavonols investigated, only 3-hydroxyflavone increased hCAR-SV23 and hCAR-SV24 activities. 3-Hydroxyflavone did not transactivate the ligand-binding domain of these isoforms or recruit steroid receptor coactivators (SRC-1, SRC-2, or SRC-3). By comparison, 3-hydroxyflavone, galangin, datiscetin, kaempferol, quercetin, isorhamnetin and tamarixetin activated hCAR-WT, whereas none of the flavonols activated hCAR-SV25 (both SPTV and APYLT insertions). The flavonols 3-Hydroxyflavone, galangin, quercetin and tamarixetin transactivated the ligand-binding domain of hCAR-WT, but only 3-hydroxyflavone recruited SRC-1, SRC-2 and SRC-3 to the receptor. CONCLUSION AND IMPLICATIONS: hCAR-SV23 and hCAR-SV24 can be activated by a mechanism that does not involve the ligand-binding domain of the receptor or recruitment of SRC-1, SRC-2, or SRC-3. 3-Hydroxyflavone and its structural analogues activated hCAR in an isoform-selective and chemical-specific manner. Overall, our study provides insight into a novel mode of ligand activation of hCAR-SV23 and hCAR-SV24.
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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".