The aryl hydrocarbon receptor (AHR) is a nuclear receptor that modulates the response to environmental stimuli. It was recognized historically for its role in toxicology but, in recent decades, it has been increasingly recognized as an important modulator of disease—especially for its role in modulating immune and inflammatory responses. AHR has been implicated in many diseases that are driven by immune/inflammatory processes, including major depressive disorder, multiple sclerosis, rheumatoid arthritis, asthma, and allergic responses, among others. The mechanisms by which AHR has been suggested to impact immune/inflammatory diseases include targeted gene expression and altered immune differentiation. It has been suggested that single nucleotide polymorphisms (SNPs) that are near AHR-regulated genes may contribute to AHR-dependent disease mechanisms/pathways. Further, we have found that SNPs that are outside of nuclear receptor binding sites (i.e., outside of AHR response elements (AHREs)) may contribute to AHR-dependent gene regulation in a SNP- and ligand-dependent manner. This review will discuss the evidence and mechanisms of AHR contributions to immune/inflammatory diseases and will consider the possibility that SNPs that are outside of AHR binding sites might contribute to AHR ligand-dependent inter-individual variation in disease pathophysiology and response to pharmacotherapeutics.

Millions of patients suffer from Major Depressive Disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, four and eight weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (p<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (p<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (p=7.84E-09) SNP clusters on chromosome four 5’ of TSPAN5 and a cluster across ERICH3 on chromosome one (p=9.28E-08) that were also observed during GWAS for change in serotonin at four (p=5.6E-08 and p=7.54E-07, respectively) and eight weeks (p=1.25E-06 and p=3.99E-07, respectively). The SNPs on chromosome four were eQTLs for TSPAN5. Knockdown (KD) and over expression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may play a role in SSRI treatment outcomes.

Major depressive disorder (MDD) is a heterogeneous disease. Efforts to identify biomarkers for sub-classifying MDD and antidepressant therapy by genome-wide association studies (GWAS) alone have generally yielded disappointing results. We applied a metabolomics-informed genomic research strategy to study the contribution of genetic variation to MDD pathophysiology by assaying 31 metabolites, including compounds from the tryptophan, tyrosine, and purine pathways, in plasma samples from 290 MDD patients. Associations of metabolite concentrations with depressive symptoms were determined, followed by GWAS for selected metabolites and functional validation studies of the genes identified. Kynurenine (KYN), the baseline plasma metabolite that was most highly associated with depressive symptoms, was negatively correlated with severity of those symptoms. GWAS for baseline plasma KYN concentrations identified SNPs across the beta-defensin 1 (DEFB1) and aryl hydrocarbon receptor (AHR) genes that were cis-expression quantitative trait loci (eQTLs) for DEFB1 and AHR mRNA expression, respectively. Furthermore, the DEFB1 locus was associated with severity of MDD symptoms in a larger cohort of 803 MDD patients. Functional studies demonstrated that DEFB1 could neutralize lipopolysaccharide-stimulated expression of KYN-biosynthesizing enzymes in monocytic cells, resulting in altered KYN concentrations in the culture media. In addition, we demonstrated that AHR was involved in regulating the expression of enzymes in the KYN pathway and altered KYN biosynthesis in cell lines of hepatocyte and astrocyte origin. In conclusion, these studies identified SNPs that were cis-eQTLs for DEFB1 and AHR and, which were associated with variation in plasma KYN concentrations that were related to severity of MDD symptoms.

The testis‐specific Y‐encoded‐like protein (TSPYL) gene family includes TSPYL1 to TSPYL6. We previously reported that TSPYL5 regulates cytochrome P450 (CYP) 19A1 expression. Here we show that TSPYLs, especially TSPYL 1, 2, and 4, can regulate the expression of many CYP genes, including CYP17A1, a key enzyme in androgen biosynthesis, and CYP3A4, an enzyme that catalyzes the metabolism of abiraterone, a CYP17 inhibitor. Furthermore, a common TSPYL1 single nucleotide polymorphism (SNP), rs3828743 (G/A) (Pro62Ser), abolishes TSPYL1's ability to suppress CYP3A4 expression, resulting in reduced abiraterone concentrations and increased cell proliferation. Data from a prospective clinical trial of 87 metastatic castration‐resistant prostate cancer patients treated with abiraterone acetate/prednisone showed that the variant SNP genotype (A) was significantly associated with worse response and progression‐free survival. In summary, TSPYL genes are novel CYP gene transcription regulators, and genetic alteration within these genes significantly influences response to drug therapy through transcriptional regulation of CYP450 genes.

1. To analyze the polymorphic activities of CYP2C8 and evaluate their impact on drug inhibitory potential, three CYP2C8 allelic variants (CYP2C8.2, CYP2C8.3, and CYP2C8.4), two non-synonymous single nucleotide polymorphic variants (R139K and K399R, carried by CYP2C8.3), and wild-type CYP2C8 (CYP2C8.1) were heterologously expressed in yeast, and their enzymatic activities were characterized. CYP2C8 inhibition-based in vitro and in vivo drug-drug interactions (DDIs) in wild-type and variant CYP2C8s were then predicted. 2. Functional characterization of five CYP2C8 variants revealed similar enzymatic activity in R139K and low activity in CYP2C8.2, CYP2C8.3, CYP2C8.4, and K399R compared with CYP2C8.1. The systematic analysis of these CYP2C8 variants can provide more homogeneous data for predicting CYP2C8 phenotypes and could be applied to personalized drug therapy. 3. Prediction of DDIs indicated that CYP2C8.4, R139K, and K399R dramatically alter the IC(50) values of nifedipine, troglitazone, and raloxifene, and R139K qualitatively and quantitatively reduces the risk of in vivo paclitaxel-raloxifene and paclitaxel-troglitazone interactions. The results provide the first evidence that CYP2C8 inhibition-based DDIs may be influenced by CYP2C8 genetic polymorphisms. These inhibition data can be used by pharmacologists in the design of in vivo studies to further assess and address the potential role of CYP2C8 genotype-dependent inhibition in clinical DDIs.

Metabolomics provides valuable tools for the study of drug effects, unraveling the mechanism of action and variation in response due to treatment. In this study we used electrochemistry-based targeted metabolomics to gain insights into the mechanisms of action of escitalopram/citalopram focusing on a set of 31 metabolites from neurotransmitter-related pathways. Overall, 290 unipolar patients with major depressive disorder were profiled at baseline, after 4 and 8 weeks of drug treatment. The 17-item Hamilton Depression Rating Scale (HRSD 17 ) scores gauged depressive symptom severity. More significant metabolic changes were found after 8 weeks than 4 weeks post baseline. Within the tryptophan pathway , we noted significant reductions in serotonin (5HT) and increases in indoles that are known to be influenced by human gut microbial cometabolism. 5HT, 5-hydroxyindoleacetate (5HIAA), and the ratio of 5HIAA/5HT showed significant correlations to temporal changes in HRSD 17 scores. In the tyrosine pathway , changes were observed in the end products of the catecholamines, 3-methoxy-4-hydroxyphenylethyleneglycol and vinylmandelic acid. Furthermore, two phenolic acids, 4-hydroxyphenylacetic acid and 4-hydroxybenzoic acid, produced through noncanconical pathways, were increased with drug exposure. In the purine pathway , significant reductions in hypoxanthine and xanthine levels were observed. Examination of metabolite interactions through differential partial correlation networks revealed changes in guanosine–homogentisic acid and methionine–tyrosine interactions associated with HRSD 17 . Genetic association studies using the ratios of these interacting pairs of metabolites highlighted two genetic loci harboring genes previously linked to depression, neurotransmission, or neurodegeneration. Overall, exposure to escitalopram/citalopram results in shifts in metabolism through noncanonical pathways, which suggest possible roles for the gut microbiome, oxidative stress, and inflammation-related mechanisms.

Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. The functional characterization of variants of unknown significance within ORFs remains a major challenge for pharmacogenomics. Deep mutational scanning (DMS) is a high-throughput technique that makes it possible to analyze the functional effect of hundreds of variants in a parallel and scalable fashion. We adapted a "landing pad" DMS system to study the function of missense variants in the ORFs of cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily C member 19 (CYP2C19). We studied 230 observed missense variants in the CYP2C9 and CYP2C19 ORFs and found that 19 of 109 CYP2C9 and 36 of 121 CYP2C19 variants displayed less than ~ 25% of the wild-type protein expression, a level that may have clinical relevance. Our results support DMS as an efficient method for the identification of damaging ORF variants that might have potential clinical pharmacogenomic application. Genetic polymorphisms in or near pharmacogenes are a major cause of individual variation in drug response phenotypes. 1 Cytochrome P450 family 2 subfamily C member 9 (CYP2C9) and cytochrome P450 family 2 subfamily C member 19 (CYP2C19) are genes that encode important cytochrome P450 enzymes that catalyze the phase I biotransformation of a variety of therapeutic drugs, including antiplatelet agents, selective serotonin reuptake inhibitors, and proton pump inhibitors. 2-5 Several years ago, the Mayo Clinic launched the RIGHT 1K study, in which next generation DNA sequencing (NGS) was performed with DNA from 1013 Mayo Biobank participants to identify variants in 84 pharmacogenes, including CYP2C9 and CYP2C19. 1,6 However, many of the polymorphisms observed in those patients were variants of unknown significance (VUS). 1,7,8 In a recent publication, we functionally characterized six novel nonsynonymous open reading frame (ORF) variants in the CYP2C9 gene and seven nonsynonymous ORF variants in the CYP2C19 gene observed in DNA from participants in the Right 1K study. 9 Conventional methods for the characterization of individual sequence variants "one-at-a-time" are reliable, but they are also time-consuming, labor-intense, and not easily scalable. As a result, only a limited number of variants can practically be investigated in that fashion. To help address this challenge, predictive algorithms, such as Polyphen-2, SIFT, and PROVEAN, among others, represent efforts to help identify deleterious variants, but their reliability is variable and inadequate for clinical application. 10-12

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