Abstract
Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a “multiple omics” research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to “inform” genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.
Highlights
PHARMACOGENOMICS TO PHARMACO-OMICSPharmacogenomics (PGx), the study of the role of inheritance in individual variation in drug response, has evolved from early “pharmacogenetic” studies of candidate genes, often genes encoding drug metabolizing enzymes, to become “pharmacogenomics” after it became possible to scan across the genome in an unbiased fashion to identify genes associated with variation in drug response (Wang et al, 2011; Weinshilboum and Wang, 2017)
When we describe the development of a machine learning-based, multiply replicated predictive algorithm for selective serotonin reuptake inhibitors (SSRIs) response in Major Depressive Disorder (MDD), it was found that single nucleotide polymorphism (SNP) from all four of the signals identified during these two genome-wide association study (GWAS), those for Tetraspanin 5 (TSPAN5), ERICH3, DEFB1 and aryl hydrocarbon receptor (AHR), all contributed to the predictive accuracy of the algorithm (Athreya et al, 2018; Athreya et al, 2019b)
The biological measures described in this review, i.e. the SNPs identified during the GWA studies, SNPs associated with metabolites which were themselves associated with clinical response, together with clinical variables such as baseline severity as evaluated by physicians, were incorporated into a supervised machine learning model to predict SSRI response
Summary
Pharmacogenomics (PGx), the study of the role of inheritance in individual variation in drug response, has evolved from early “pharmacogenetic” studies of candidate genes, often genes encoding drug metabolizing enzymes, to become “pharmacogenomics” after it became possible to scan across the genome in an unbiased fashion to identify genes associated with variation in drug response (Wang et al, 2011; Weinshilboum and Wang, 2017). Rapid advances in “-omic” technologies, e.g., metabolomics, transcriptomics and proteomics, coupled with a computational revolution that has made it possible to integrate and analyze large datasets, have enabled pharmacogenomics to expand beyond the genome to become “pharmaco-omics”—as will be illustrated by the subsequent description of SSRI pharmaco-omics (see Figure 1B). This brief review will describe the application of a multiple omics research strategy in an attempt to increase our understanding of and our ability to predict variation in clinical response for an extremely important class of drugs, the selective serotonin reuptake inhibitors (SSRIs)
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