mRNA expression changes following drug treatment and pharmacogenomic responses

Introduction

The recognition that individuals respond differently to identical drug treatments has ancient origins, but the molecular basis for this variability has only recently been elucidated through advances in pharmacogenomics [1] . Genetic polymorphisms affecting drug-metabolizing enzymes, transporters, receptors, and downstream signaling molecules collectively contribute to the wide spectrum of drug responses observed in clinical practice. Understanding these genetic influences has become essential for developing personalized therapeutic approaches that maximize efficacy while minimizing adverse effects [2] . Cytochrome P450 enzymes constitute the primary drug-metabolizing system in humans, with CYP2D6, CYP2C19, CYP2C9, and CYP3A4 collectively responsible for metabolism of approximately 80% of clinically used medications [3] . Genetic polymorphisms in these enzymes create distinct metabolizer phenotypes ranging from poor metabolizers lacking functional enzyme activity to ultrarapid metabolizers with amplified gene copies. These phenotypic differences translate directly into altered drug pharmacokinetics and clinical outcomes [4] . Beyond affecting enzyme activity, genetic polymorphisms may influence gene expression at the transcriptional level, adding another layer of complexity to pharmacogenomic relationships [5] . The extent to which genetic variants affect mRNA expression in response to drug exposure, and whether these transcriptional responses differ according to baseline genotype, remains incompletely characterized. Such knowledge is critical for understanding the full spectrum of pharmacogenomic influences on drug response [6] . Drug transporters including P-glycoprotein (encoded by ABCB1) and organic anion transporting polypeptides play essential roles in drug absorption, distribution, and elimination [7] . Genetic polymorphisms in transporter genes affect substrate handling and contribute to variable drug exposure, while drug-induced changes in transporter expression can alter pharmacokinetics during chronic treatment. Integration of transporter genetics with metabolic enzyme genetics provides more comprehensive prediction of drug response [8] . This research was designed to characterize mRNA expression changes in pharmacogenes following drug treatment and determine how genetic polymorphisms modify these transcriptional responses [9] . The investigation employed RNA sequencing technology combined with genotype-stratified analysis to identify expression patterns associated with metabolizer phenotypes. The findings contribute to understanding of pharmacogenomic mechanisms and may inform development of improved models for predicting individual drug responses [10][11] .

Material and Methods

Material

This prospective pharmacogenomic investigation enrolled 156 healthy adult volunteers from the greater Toronto area between October 2022 and June 2024. Participants were recruited through institutional advertisement and community outreach with selection to ensure representation of diverse metabolizer phenotypes. Inclusion criteria comprised age 18-55 years, body mass index 18.5-30 kg/m², and documented genotypes for CYP2D6, CYP2C19, CYP3A4, and ABCB1 polymorphisms. Exclusion criteria included chronic disease, regular medication use, pregnancy, smoking, and abnormal baseline laboratory values. Probe drugs were administered according to validated cocktail phenotyping protocol: dextromethorphan (CYP2D6), omeprazole (CYP2C19), midazolam (CYP3A4), digoxin (P-glycoprotein), and pravastatin (OATP1B1). All medications were obtained from certified pharmaceutical suppliers with documented quality specifications. Blood collection tubes, RNA extraction kits, and sequencing reagents were obtained from established vendors with batch documentation. The research was conducted at the Clinical Pharmacology Unit, University Health Network.

Methods

Genotyping for pharmacogenomic variants employed the PharmaScan array covering 59 pharmacogenes with clinical relevance. Participants were classified according to Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines into metabolizer phenotype categories: poor metabolizer (PM), intermediate metabolizer (IM), normal metabolizer (NM), and rapid/ultrarapid metabolizer (RM). The final cohort included 38 PM, 42 IM, 52 NM, and 24 RM individuals for CYP2D6, with similar distributions for other enzymes. Drug administration followed standardized protocols with appropriate washout periods between probe drugs. Peripheral blood mononuclear cells were isolated using density gradient centrifugation at baseline (pre-dose) and 2, 4, 8, 12, 24, and 48 hours post-dose. RNA was extracted using TRIzol method followed by column purification with RNeasy Mini Kit. RNA quality was verified using Bioanalyzer with minimum RIN value of 8.0 required for sequencing. RNA sequencing was performed on Illumina NovaSeq 6000 platform with 50 million paired-end reads per sample. Data processing employed STAR aligner with reference genome GRCh38, followed by transcript quantification using RSEM. Differential expression analysis utilized DESeq2 with significance threshold of adjusted p-value < 0.05 and fold change > 1.5. Pathway enrichment analysis employed Gene Ontology and KEGG databases. Statistical comparisons between genotype groups used ANOVA with Tukey correction for multiple comparisons.

Ethical Considerations

This research received approval from the Research Ethics Board of the University of Toronto (Protocol 2022-0847-PHM, approved September 2022). All participants provided written informed consent following detailed explanation of research procedures, genetic testing implications, and potential risks. Genetic counseling services were available for participants with questions regarding their pharmacogenomic results. Data were anonymized and stored securely in compliance with Canadian privacy regulations. Participants received their genotyping results with interpretation guides to facilitate future healthcare decisions. RNA sequencing analysis identified 847 genes with significant differential expression following drug treatment, comprising 423 upregulated and 424 downregulated genes (Figure 1). Pathway enrichment analysis revealed significant enrichment of xenobiotic metabolism (p<10⁻¹²), oxidative stress response (p<10⁻⁸), and cellular transport (p<10⁻⁶) gene sets. Drug-metabolizing enzymes showed the most pronounced changes, with CYP3A4 increasing 3.4-fold and CYP2D6 increasing 2.9-fold following treatment.

Results

Comprehensive Interpretation

Genotype-stratified analysis revealed significant differences in transcriptional responses to drug treatment (Table 2, Figure 234). CYP3A4 induction was markedly attenuated in poor metabolizers (1.8-fold) compared to normal metabolizers (3.4-fold, p<0.001), suggesting that genetic variants affecting baseline enzyme activity also influence transcriptional responsiveness. Temporal analysis demonstrated progressive gene induction with peak expression at 24-48 hours post-treatment. Transporter genes showed inverse regulation with ABCB1 expression decreasing 2.5-fold, potentially affecting drug disposition during repeated dosing.

Discussion

This investigation demonstrates that genetic polymorphisms in drug-metabolizing enzymes influence both baseline mRNA expression and the magnitude of transcriptional responses to drug exposure [12][13][14] . The observation of elevated baseline CYP2D6 expression in poor metabolizers represents a compensatory mechanism whereby reduced functional enzyme activity triggers upregulation at the transcriptional level, potentially partially offsetting the metabolic deficit. The attenuated CYP3A4 induction observed in poor metabolizers has important clinical implications. This finding suggests that genetic variants not only affect initial drug metabolism but may also modify the degree of enzyme induction during chronic treatment [15] . Patients with poor metabolizer genotypes may therefore exhibit different pharmacokinetic profiles during long-term therapy compared to normal metabolizers, even for drugs not directly metabolized by the polymorphic enzyme [16] . The inverse regulation of ABCB1 transporter expression following drug treatment represents an adaptive response that may limit drug accumulation during repeated exposure [17] . The consistent downregulation across metabolizer phenotypes suggests this represents a drug-specific effect independent of genotype, though the functional consequences for drug disposition merit further investigation [18][19] .

Clinical Implications

These findings have several implications for clinical pharmacogenomics implementation. First, baseline genotyping provides incomplete prediction of drug response because transcriptional responses to treatment also vary according to genotype. Second, the compensatory expression changes in poor metabolizers may partially attenuate predicted differences in drug metabolism, suggesting that current dosing algorithms may require refinement. Third, transporter expression changes during treatment should be considered when developing pharmacokinetic models for chronic therapy. Integration of transcriptional data with genotype information may improve prediction accuracy for individualized dosing.

Future Perspectives

Continued investigation should address several questions arising from this work. Longitudinal studies examining whether genotype-dependent transcriptional patterns persist during chronic treatment are needed. The contribution of epigenetic mechanisms to observed expression differences warrants evaluation. Development of integrated pharmacogenomic-transcriptomic models for drug response prediction represents an important direction. Extension of this approach to patient populations with specific diseases will determine translational relevance of findings from healthy volunteers.

Conclusion

This research demonstrates that genetic polymorphisms in drug-metabolizing enzymes influence mRNA expression patterns both at baseline and in response to drug treatment. Poor metabolizers exhibited compensatory upregulation of CYP2D6 expression, with 2.4-fold higher baseline levels compared to normal metabolizers, suggesting transcriptional mechanisms partially offset reduced enzyme activity. Drug treatment induced significant expression changes in 847 genes, with metabolizing enzymes and transporters showing the most pronounced alterations. Importantly, the magnitude of transcriptional induction differed according to genotype, with CYP3A4 induction attenuated in poor metabolizers. These genotype-dependent transcriptional responses add complexity to pharmacogenomic predictions and suggest that static genotype-based dosing algorithms may require refinement. The findings support integration of gene expression monitoring with genotype-guided therapy to optimize individualized drug treatment. As pharmacogenomics implementation expands in clinical practice, understanding the full spectrum of genetic influences on drug response, including transcriptional effects, will be essential for realizing the promise of personalized medicine.

Acknowledgement

Acknowledgements

References

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