Ethyl alcohol is a toxin that, when consumed at high levels, produces organ damage and death. One way to prevent or ameliorate this damage in humans is to reduce the exposure of organs to alcohol by reducing alcohol ingestion. Both the propensity to consume large volumes of alcohol and the susceptibility of human organs to alcohol-induced damage exhibit a strong genetic influence. We have developed an integrative genetic/genomic approach to identify transcriptional networks that predispose complex traits, including propensity for alcohol consumption and propensity for alcohol-induced organ damage. In our approach, the phenotype is assessed in a panel of recombinant inbred (RI) rat strains, and quantitative trait locus (QTL) analysis is performed. Transcriptome data from tissues/organs of naïve RI rat strains are used to identify transcriptional networks using Weighted Gene Coexpression Network Analysis (WGCNA). Correlation of the first principal component of transcriptional coexpression modules with the phenotype across the rat strains, and overlap of QTLs for the phenotype and the QTLs for the coexpression modules (module eigengene QTL) provide the criteria for identification of the functionally related groups of genes that contribute to the phenotype (candidate modules). While we previously identified a brain transcriptional module whose QTL overlapped with a QTL for levels of alcohol consumption in HXB/BXH RI rat strains and 12 selected rat lines, this module did not account for all of the genetic variation in alcohol consumption. Our search for QTL overlap and correlation of coexpression modules with phenotype can, however, be applied to any organ in which the transcriptome has been measured, and this represents a holistic approach in the search for genetic contributors to complex traits. Previous work has implicated liver/brain interactions, particularly involving inflammatory/immune processes, as influencing alcohol consumption levels. We have now analyzed the liver transcriptome of the HXB/BXH RI rat panel in relation to the behavioral trait of alcohol consumption. We used RNA-Seq and microarray data to construct liver transcriptional networks, and identified a liver candidate transcriptional coexpression module that explained 24% of the genetic variance in voluntary alcohol consumption. The transcripts in this module focus attention on liver secretory products that influence inflammatory and immune signaling pathways. We propose that these liver secretory products can interact with brain mechanisms that affect alcohol consumption, and targeting these pathways provides a potential approach to reducing high levels of alcohol intake and also protecting the integrity of the liver and other organs.
Mammalian Genome – Springer Journals
Published: Dec 1, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud