Whole Brain and Brain Regional Coexpression Network Interactions Associated with Predisposition to Alcohol Consumption

Abstract

To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA). Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL) with a genomic region that regulates alcohol consumption (bQTL). To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories) and from gene expression data from 6 brain regions (nucleus accumbens (NA); prefrontal cortex (PFC); ventral tegmental area (VTA); striatum (ST); hippocampus (HP); cerebellum (CB)) available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three “meta-modules”, composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA) and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits.

Influence of sex on genetic regulation of “drinking in the dark” alcohol consumption

“…Expression analysis was performed with Affymetrix mouse whole genome exon arrays (Mouse Exon 1.0 ST, Affymetrix, Santa Clara CA) as previously described (Tabakoff et al 2008; Vanderlinden et al 2013) and according to the manufacturer’s protocols. RNA from the brain of each mouse was hybridized to a separate array.…”

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

“…Weighted gene coexpression network analysis (WGCNA) (Zhang and Horvath, 2005) is one such approach that has been utilized successfully to provide new biological insight into gene networks involved in several CNS disorders, including autism (Parikshak et al, 2013), Alzheimer's disease (Miller et al, 2013; Zhang et al, 2013), schizophrenia (Maschietto et al, 2015) and alcoholism (Vanderlinden et al, 2013). Previous studies have utilized coexpression analyses in depressed human post-mortem tissue or mouse stress models to describe interesting network level changes in single brain regions, but the mechanistic role of such changes has not been examined (Chang et al, 2014; Gaiteri and Sibille, 2011; Malki et al, 2015; Malki et al, 2013).…”

Voluntary wheel running reduces voluntary consumption of ethanol in mice: identification of candidate genes through striatal gene expression profiling

“…Weighted Gene Co-expression Network Analysis (WGCNA), an agnostic network analysis tool, was used to identify biologically relevant co-expression networks (Langfelder and Horvath, 2008; Zhao et al, 2010). Expression data produced from RNA-Seq and analyzed using WGCNA have been shown to improve network characteristics relative to microarray expression data (Iancu et al, 2012), and both microarrays and RNA-Seq have been used successfully to characterize gene co-expression networks related to ethanol behaviors (Contet, 2012; Darlington et al, 2013; Farris et al, 2014; Iancu et al, 2013; Marballi et al, 2015; McBride et al, 2010, 2013; Mulligan et al, 2011; Vanderlinden et al, 2013, 2015; Zhang et al, 2014). A recent review by Farris et al (2015) summarizes findings across multiple omics studies.…”