Neavin, Drew

Objective CYP2C9 and CYP2C19 are highly polymorphic pharmacogenes that encodes enzymes that metabolize a large number of clinically relevant drugs. While the majority of variability in drug metabolism can be explained by a few common alleles, identification of gene sequence variation that alters enzyme activity and incorporation of that information into pharmacogenomic rules for clinical decision support might lead to improved therapy. Using sequence data derived from the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT Protocol), we sought to identify variants of unknown significance (VUSs) for CYP2C9 and CYP2C19, and to characterize the function of the encoded enzymes. Methods Targeted next‐generation sequencing for 84 pharmacogenes using the PGRN‐Seq v1.0 capture reagent was performed with DNA samples from 1013 RIGHT Protocol participants. Plasmids containing VUSs were created using site directed mutagenesis and were transiently expressed in COS‐1 cells. Initial determination of enzyme activity was performed using fluorogenic substrates. Enzyme kinetic parameters (Km and Vmax) for each of the variant allozymes were determined using liquid chromatography mass spectrometry with tolbutamide (CYP2C9) and mephenytoin (CYP2C19) as prototypic substrates. We also compared our results with in silico functional predictions by SIFT and Polyphen. Results Seven VUSs were identified for each gene, none of which had been functionally characterized. There were significant positive correlations between protein content and fluorometric enzyme activity for all variants for both CYP2C9 (P<0.05) and CYP2C19 (P<0.005). Notably, CYP2C9 V237L and CYP2C19 A173V activity was much lower than predicted based on protein content. The substrate intrinsic clearance of CYP2C9 variants P73L , S115R , V237L [6.23E‐05±1.08E‐05; 2.55E‐05±4.1E‐06; 7.28E‐05±2.4E‐05 (uL/mg protein/min)], and CYP2C19 variants V113I , A173V , R186C and R186H [2.49E‐04±2.78E‐05; 1.80E‐05±2.19E‐06; 2.33E‐05±9.2E‐06;1.45E‐04±1.2E‐05 (uL/mg protein/min)] were less than 25% of their respective WT enzyme . Conclusion Sequencing of CYP2C9 and CYP2C19 from 1013 subjects identified low frequency variants that had not been functionally characterized. In silico predictions which showed variable results, can be helpful but are not always accurate, hence functional validation is necessary for VUSs requiring protein expression and determination of kinetic parameters. Efforts to identify variation in diverse populations is resulting in a very large number of VUSs, so there is a need for the application of high throughput mutagenesis approaches and functional characterization methods to help pharmacogenomics contribute to precision medicine. Support or Funding Information NIH RO1 GM28157 and the Mayo Center for Individualized Medicine Pharmacogenomics Program.

Major depressive disorder (MDD), the number one psychiatric disease and a major cause of medical disability worldwide, is a heterogeneous disease. Efforts to identify biomarkers for antidepressant therapy response by the use of genome‐wide association studies (GWAS) have generally yielded disappointing results, e.g. only a few markers have been replicated across studies. By applying a “pharmacometabolomics‐informed pharmacogenomic” research strategy, we previously identified a genome‐wide significant single‐nucleotide polymorphism (SNP) signal in the glutamate‐rich 3 ( ERICH3 ) gene that was associated with plasma serotonin concentrations which were themselves associated with selective serotonin reuptake inhibitor (SSRI) response in MDD patients enrolled in the Mayo Clinic PGRN‐AMPS SSRI trial. The same ERICH3 SNPs were associated with SSRI response in other GWAS, including the STAR*D and ISPC studies. However, the function of ERICH3 and the molecular mechanism(s) by which it might be associated with plasma serotonin concentrations and with SSRI response remain unknown. Functional characterization of ERICH3 and its encoded protein might make it possible to identify novel mechanisms of antidepressant treatment response. In the present study, ERICH3 mRNA and protein were studied in cell lines from human peripheral blood and brain. ERICH3 cDNA constructs and specific antibodies were generated to make it possible to study its function. Two ERICH3 mRNA splice variants and protein isoforms, one “full‐length” and the other encoding a C‐terminal protein, were identified in a variety of human cell lines and in human brain tissue. ERICH3 mRNA expression and immunofluorescent staining suggested both cytosolic and vesicular localizations for ERICH3 as well as co‐localization with serotonin and dopamine in both mouse and human neurons. Co‐immunoprecipitation studies identified several ERICH3 interacting proteins, including PIK3C2A, CLTC and AP2A2, proteins known to play a role in vesicular trafficking, as well as ALK and CUX1, both of which display genetic polymorphisms that have been associated with SSRI response in GWAS studies of antidepressant treatment response in MDD patients. This series of experiments suggests that ERICH3 may play a role in vesicular trafficking and, as a result, may influence neurotransmitter actions in the brain that are associated with antidepressant treatment response. Support or Funding Information This work was supported by the NIH grants U19 GM61388, R01 GM28157 and T32 GM008685. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

BACKGROUND Most functionally‐significant single nucleotide polymorphisms (SNPs) are outside of open reading frames. Mechanisms by which these SNPs contribute to gene regulation are poorly understood. Aryl hydrocarbon receptor (AHR) is a nuclear receptor that regulates drug metabolism, xenobiotic response and immune response. Our previous data indicate that SNPs outside response elements can impact transcription factor binding and gene regulation. We hypothesized that SNPs near but outside AHR response elements (AhREs) might impact AHR binding and gene regulation. METHODS We have collected dense SNP‐genotypes across 300 lymphoblastoid cell lines (LCLs) – a powerful tool for generating and testing pharmacogenomic hypotheses. Eight LCLs were used to generate hypotheses about AHR SNP‐dependent transcription utilizing RNA‐ and ChIP‐seq, which were tested in additional LCLs using qRT‐PCR and ChIP after AHR agonist or antagonist treatment. RESULTS 69 genes were highly significantly differentially expressed after agonist or antagonist treatment and 80% of those genes had an AhRE within 50kb. Five SNP‐gene pairs have been functionally validated in additional LCLs (Table 1). CONCLUSION SNPs that are outside of AhREs can alter AHR binding and gene transcription. This is a general phenomenon that may have large‐scale influence on gene transcriptional regulation and pharmacogenomics. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

We previously reported that testis‐specific Y‐encoded‐like protein (TSPYLs) are transcription regulators for CYP3A4, CYP2C9, and CYP2C19. Here, we observed dual roles for TSPYLs in mediating serotonin transport and the metabolism of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD). The widely prescribed SSRIs, citalopram, and escitalopram are metabolized mainly by CYP2C19. The TSPYL1 rs3828743 single nucleotide polymorphism (SNP), which decreases its suppression of CYP2C19 expression, was associated with rapid escitalopram metabolism and worse treatment response in the Mayo PGRN‐AMPS clinical trial. We also found that TSPYLs can regulate expression of the serotonin transporter protein, SLC6A4, and, in turn, serotonin transport into cells. The SNPs in tight linkage disequilibrium with the TSPYL1 rs10223646 SNP were significantly correlated with baseline severity of depression in patients with MDD in the Sequenced Treatment Alternatives to Relieve Depression and International SSRI Pharmacogenomics Consortium clinical trials. Our findings suggest that genetic variation in TSPYL genes may be novel indicators for baseline severity of depression and SSRI poor response.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

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.

Major depressive disorder (MDD) is a severe illness that afflicts about 16.6% of people over their lifetime. MDD is highly correlated with suicidality, and often first presents in adolescence. Unfortunately, many pediatric patients suffering from MDD go undiagnosed, and current evidence-based treatment options in the U.S. are limited to psychotherapy and two selective serotonin reuptake inhibitors approved by the United States Food and Drug Administration. Molecular mechanisms have been shown to play a role in MDD pathogenesis, progression, and response to medication, yet few studies have explored the role of these pathways in pediatric MDD. In this review, we outline the gravity and importance of MDD in pediatric patients, some challenges in diagnosis and treatment, current treatments available for pediatric patients, and research to investigate differences between pediatric and adult MDD. We hope that this review will provide an outline of the current understanding and treatment of MDD in pediatric patients, and provide thoughtful insights for future work that could advance our understanding of MDD in pediatric populations, and also identify new therapeutic strategies.