A Systems Approach Identifies Networks and Genes Linking Sleep and Stress: Implications for Neuropsychiatric Disorders

Abstract

SUMMARY Sleep dysfunction and stress susceptibility are co-morbid complex traits, which often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multi-level organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J×A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests the interplay between sleep, stress, and neuropathology emerge from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework to interrogate the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders.

Sex-specific transcriptional signatures in human depression

“…Furthermore, consistent with previous network-based comparisons 67,69,71,73,77 , we identified several modules enriched for genes expressed predominantly in specific cell types such as neurons, microglia, oligodendrocytes, astrocytes or endothelial cells to be associated with a GOC or LOC in males or females with MDD (Extended Figure 6a,b). The results illustrate that MDD is associated with transcriptional alterations occurring within several cell types, which appear to be affected differently in males and females.…”

REM sleep: unique associations with behavior, corticosterone regulation and apoptotic pathways in chronic stress in mice

“…transcriptomic predictors identified using machine learning. Transcriptomic predictor sets were overall relatively small in accordance with a previous study in which phenotypes were grouped into categories (75). Application of a machine learning approach which controlled for the unspecific effects of UCMS demonstrated that NREMS and REMS shared very few predictors, further emphasizing the contrast between these two sleep states observed at the electrophysiological level in this study.…”

“…They included pathways playing a role in oxidative stress or apoptosis with the pathway involved in the transport of proapoptic proteins linking the Jun amino-terminal kinases (JNK) signal transduction pathway and the mitochondrial apoptotic machinery (86). A causal role for mitochondrial genes was recently proposed as part of the processes in the striatum linking REMS parameters and stressinduced anxiety-like phenotype (75) and the contribution of mitochondrial dysfunction in major depression is emerging (87).…”

Sleep Deficits and Cannabis Use Behaviors: An Analysis of Shared Genetics Using Linkage Disequilibrium Score Regression and Polygenic Risk Prediction

“…Along these lines, several of the genes and genetic pathways found to be significant in sleep relatedvariable GWAS 56,58-61 have been associated with cannabis use and cannabinoid activity [62][63][64][65]67,68,[109][110][111][112] . Likewise, GWAS for lifetime cannabis use and CUD 65,66,69,70 have found genes that have been linked to sleep behaviors and circadian rhythm [71][72][73][74]113 .…”