The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Abstract

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multiomic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder

“…In males, they were notably related to: immune responses, such as phagocytosis (NES = 2.14, FDR < 10 − 4 ), Toll-like receptor signaling (NES = 1.67, FDR = 1.8x10 − 2 ), leukocyte transendothelial migration, necroptosis (NES = 1.72, FDR = 1.19x10 − 2 ), IL-17 signaling pathway (NES = 1.76, FDR = 7.22 10 − 3 ); energy metabolism, including oxidative phosphorylation (NES = 1.81, FDR = 3.81x10 − 3 ) glycolysis (NES = 1.71, FDR = 1.37x10 − 2 ), pentose phosphate pathway (NES = 1.84, FDR = 2.96x10 − 3 ), carbon metabolism (NES = 1.82, FDR = 3.99x10 − 3 ); the proteasome (NES = 2.21, FDR < 10 − 4 ); mTOR signaling (NES = 1.56, FDR = 4.59x10 − 2 ); and the synaptic vesicle cycle (NES = 1.88, FDR = 1.61x10 − 3 ). These results are consistent with previous peripheral blood studies of MDD, which identi ed similar GO terms related to the stress axis, immunity and brain neuronal physiology [17,54]. They also further document signi cant sex differences in MDD, which we further characterized using RRHO2.…”

“…Trying to overcome this di culty, research on other medical conditions, such as cancer or chronic respiratory diseases [14,15], has suggested that integration of several molecular modalities represent a promising strategy for casecontrol classi cation. In the case of MDD, such efforts have mainly used "step-by-step" strategies, whereby sparse differences are identi ed individually at the level of each omic layer, using arbitrary thresholds, and then aggregated [16,17]. These previous studies, however, did not comprehensively leverage the genome-wide and multiomic nature of available data, nor did they quantify how combining multiple layers may generate more relevant MDD biomarkers.…”

Functional genomic mechanisms of opioid action and opioid use disorder: a systematic review of animal models and human studies

“…By combining WGBS with ChIP-Seq analysis of 6 histone marks, the 2 forms of plasticity were found to affect distinct genomic sites and features, and to be defined by different histones modifications and chromatin states. Similar multiomic investigations of opioid effects should be conducted and, importantly, will require dedicated bioinformatic frameworks, which are only emerging in psychiatry [ 182 ]. Only 2 studies reviewed here followed that path: Mendez et al performed transcriptomic and proteomic analyses, while Liu et al analyzed the transcriptome and methylome (see 4.Non-coding and epigenomic mechanisms of opioid plasticity ).…”