Vitamin D and lumisterol derivatives can act on liver X receptors (LXRs)
Bibliographic record
Abstract
Abstract The interactions of derivatives of lumisterol (L3) and vitamin D3 (D3) with liver X receptors (LXRs) were investigated. Molecular docking using crystal structures of the ligand binding domains (LBDs) of LXRα and β revealed high docking scores for L3 and D3 hydroxymetabolites, similar to those of the natural ligands, predicting good binding to the receptor. RNA sequencing of murine dermal fibroblasts stimulated with D3-hydroxyderivatives revealed LXR as the second nuclear receptor pathway for several D3-hydroxyderivatives, including 1,25(OH)2D3. This was validated by their induction of genes downstream of LXR. L3 and D3-derivatives activated an LXR-response element (LXRE)-driven reporter in CHO cells and human keratinocytes, and by enhanced expression of LXR target genes. L3 and D3 derivatives showed high affinity binding to the LBD of the LXRα and β in LanthaScreen TR-FRET LXRα and β coactivator assays. The majority of metabolites functioned as LXRα/β agonists; however, 1,20,25(OH)3D3, 1,25(OH)2D3, 1,20(OH)2D3 and 25(OH)D3 acted as inverse agonists of LXRα, but as agonists of LXRβ. Molecular dynamics simulations for the selected compounds, including 1,25(OH)2D3, 1,20(OH)2D3, 25(OH)D3, 20(OH)D3, 20(OH)L3 and 20,22(OH)2L3, showed different but overlapping interactions with LXRs. Identification of D3 and L3 derivatives as ligands for LXRs suggests a new mechanism of action for these compounds.
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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) | 0.003 | 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".