Identification of common polymorphisms in the promoter of the UGT1A9 gene
Bibliographic record
Abstract
OBJECTIVES: Polymorphisms in UDP-glucuronosyltransferases (UGTs) can influence detoxifying capacities and have considerable therapeutic implications in addition to influence various (patho)physiological processes. UGT1A9 plays a central role in the metabolism of various classes of therapeutic drugs in addition to carcinogens and steroids. The great interindividual variability of UGT1A9-mediated glucuronidation remains poorly explained, while evidence for its genetic origin exists. METHODS: The proximal UGT1A9 promoter was screened for polymorphisms by sequencing and, the contribution of single nucleotide polymorphisms (SNPs) to the variability of UGT1A9 protein levels and activity was evaluated. RESULTS: We confirmed the presence of the -109 to -98 T10 polymorphism and found ten novel SNPs that generated a diversity of haplotypes in two independent populations. In a panel of 48 human liver microsomes, the UGT1A9 expression varied by 17-fold and was significantly correlated with SNPs -275, -331/-440, -665 and -2152. The base insertion T10 reported to increase reporter gene expression in HepG2 cells [] was not linked to -275 and -2152 SNPs and was not associated with changes in UGT1A9 protein levels. Compared to wild-type individuals, there were statistically significant higher glucuronidating activities in livers with the -275 and -2152 using mycophenolic acid and propofol as UGT1A9 substrates, indicating an extensive glucuronidator phenotype associated with these variants. CONCLUSIONS: This is the first study to demonstrate that naturally occurring sequence variations in the UGT1A9 promoter are informative in predicting the levels of protein and glucuronidating activity, providing a potential mechanism for interindividual variation in UGT1A9-mediated metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".