Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression

Abstract

Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large‐scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi‐class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene‐network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

“…Genotyping for rs2032583 and rs2235015 (TT/GG) may be used for clinical application to optimize antidepressant treatment [19]. Antidepressant treatment outcome was significantly associated with rs203258, rs2235015 [17], rs2032588 [13], and C3435T [83] Encodes a glutamate-gated ionic channel family protein that collectively functions as excitatory neurotransmitters in the CNS [121] The GRIK4 SNPs rs12800734 [72] and rs1954787 showed a strong association with antidepressant treatment outcome (remission and/or response) [85] GNB3 G protein subunit beta 3…”

“…Encodes one of the receptors for serotonin Polymorphisms in 5HT-2A receptor rs17288723 [72], rs7997012 [102,103], rs9534505 [93], rs6311, rs6313, rs7997012, and rs1928040 [91,102] were significantly associated with therapeutic response or remission to antidepressants [102] SLC6A4 Solute carrier family 6 member 4…”

“…Supervised machine learning approaches are often suggested as a powerful tool in medicine. Several methods in machine learning use a multivariate approach to the entire dataset and are able to handle interactions [60,72].…”

Transcriptomic Studies of Antidepressant Action in Rodent Models of Depression: A First Meta-Analysis

“…Sex: Strikingly, most of the studies were conducted on male animals since out of 107 cohorts only 8 involved females [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], and, in fact, RNA profiling on males and females subjected to the same paradigm was performed only four times [ 32 , 33 , 34 , 36 , 37 , 38 ]. In contrast, the proportions are much more balanced between studies in mice and rats or between studies conducted in naïve or stressed animals ( Figure 2 A–C).…”

Association study of polymorphisms within inflammatory genes and methylation status in treatment response in major depression

“…[23] Other inflammatory candidate genes such as IL2 , IL10 , IL18 , tumor necrosis factor alpha ( TNF-α ) and interferon-gamma ( IFN-γ ) have received less attention, although having been previously related with major depressive disorder. [24, 25] Taking into account that response to medication may be a polygenic and complex trait than previously hypothesized and distinct molecular mechanisms seem to modulate inflammatory pathways as stated above [26], studies should address the role of different cytokine genetic variants, as response to medication may be as complex as the pathology. Besides, recent studies have also shown that alterations in the methylation status that regulate gene expression of these polymorphisms may contribute to disease risk and prognosis [27, 28], but few studies have examined whether inflammatory genes are differentially methylated in depression (Ryan’s study in late life depression [29]), or the association of methylation status of such polymorphisms with treatment outcomes.…”