Drug-induced cis-regulatory elements in human hepatocytes affect molecular phenotypes associated with adverse reactions
Bibliographic record
Abstract
Genomic variation drives phenotypic diversity, including individual differences in drug response. While coding polymorphisms linked to drug efficacy and adverse reactions are well characterized, the contribution of noncoding regulatory elements remains underexplored. Using CAGE (Cap Analysis of Gene Expression), profiling transcription initiations of mRNAs and enhancer RNAs, we identify candidate cis-regulatory elements (CREs) and assessed their activities simultaneously in HepG2 cells expressing the drug-responsive transcription factor pregnane X receptor (PXR). Comparison with GWAS data reveals strong enrichment of the drug-induced CREs near variants associated with bilirubin and vitamin D levels. Among those bound by PXR in primary hepatocytes, we identify enhancers of UGT1A1, TSKU, and CYP24A1 and functional alleles that alter regulatory activities. We also find that TSKU influences expression of vitamin D-metabolizing enzymes. This study expands the landscape of PXR-mediated regulatory elements and uncovers noncoding variants impacting drug response, providing insights into the genomic basis of adverse drug reactions. Here the authors map drug-induced cis-regulatory elements by quantitative profiling of messenger and enhancer RNAs with CAGE. Enhancers of UGT1A1, TSKU, and CYP24A1, along with alleles affecting their regulatory activities, are revealed, shedding light on the genomic basis of adverse drug reactions.
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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) | 0.002 | 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".