Functional analysis of splice variants of human constitutive androstane receptor: Investigation with flavonol (3‐hydroxyflavone) and its metabolites
Bibliographic record
Abstract
Constitutive androstane receptor (CAR) controls the transcription of genes involved in diverse biological functions, including bioactivation and detoxification of drugs and toxicants. Naturally occurring splice variants of human CAR (hCAR) exist, including hCAR‐SV23 (insertion of amino acids SPTV), hCAR‐SV24 (APYLT), and hCAR‐SV25 (SPTV and APYLT). In the present study, we investigated the effect of flavonol (3‐hydroxyflavone) and its metabolites (galangin, datiscetin, kaempferol, morin, quercetin, isorhamnetin, tamarixetin, myricetin, and syringetin) on the functionality of hCAR‐SV23, hCAR‐SV24, and hCAR‐SV25. As assessed in a cell‐based reporter gene assay, only flavonol activated hCAR‐SV23 and hCAR‐SV24, whereas none of them activated hCAR‐SV25. Flavonol did not recruit steroid receptor coactivators SRC‐1, SRC‐2, or SRC‐3 to the ligand‐binding domain of hCAR‐SV23 or hCAR‐SV24. By comparison, flavonol, galangin, datiscetin, kaempferol, quercetin, isorhamnetin, and tamarixetin activated hCAR‐WT, but only flavonol recruited coactivators. Thus, addition of OH or OCH 3 substituent at the C2′ or C5′ position of flavonol abolished activation of hCAR‐WT. In summary, flavonol and its metabolites differentially activated hCAR isoforms and, in some cases, this occurred by a mechanism that did not involve coactivator recruitment. [Supported by CIHR and MSFHR]
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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".