A pharmacogenomics study of the human estrogen glucuronosyltransferase UGT1A3
Bibliographic record
Abstract
UGT1A3 is one of the most efficient at conjugating estrone, a precursor for biosynthesis of estradiol in peripheral tissues. We established the genetic mechanisms that might contribute to individual variation in UGT1A3 expression and activity. UGT1A3 first exon and 5'-flanking regions were sequenced in 249 Caucasians. We identified 17 polymorphisms, among them seven regulatory and 10 exonic polymorphisms with six leading to amino-acid changes. Luciferase reporter assays, site-directed mutagenesis and electrophoretic mobility shift assays using hepatoma HepG2 cells were carried out to show functionality of variant promoters. Reduced transcriptional activity was associated with all six variant promoters (two-fold; P<0.001). One of the potential mechanisms would involve the -148 T>C and -581 C>T variations that modulate gene function by affecting hepatocyte nuclear factor-1alpha and hepatocyte nuclear factor-4alpha binding, respectively. Then, estrone-conjugating activity was assessed with 11 heterologously expressed allozymes. Three phenotypes were observed; UGT1A3*1, *2 (WR, VA) and *3 (WR) with high intrinsic clearance values; UGT1A3*5 (QR, WR), *7 (FI), *9 (WR, ML), *10 (VA) and *11 (WR, VA and MI) had intermediate CLint (2X-10X lower vs. *1), whereas UGT1A3*4 (RW), *6 (WR, VA, MV) and *8 (AV) had low CLint (>10X lower vs. *1). Diplotype analyses indicate that 20.1% of individuals carry two alleles affecting UGT1A3 expression and/or activity. This study did not investigate genotype-phenotype association, but raise the possibility that genetically determined variation might contribute to variability in the inactivation of estrone by UGT1A3 and subsequent changes in lifetime exposure to estrogens potentially modifying risk of cancer.
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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) | 0.000 | 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".