Elsevier

Neurobiology of Disease

Volume 45, Issue 1, January 2012, Pages 115-121
Neurobiology of Disease

Review
Ethanol modulation of gene networks: Implications for alcoholism

https://doi.org/10.1016/j.nbd.2011.04.013Get rights and content

Abstract

Alcoholism is a complex disease caused by a confluence of environmental and genetic factors influencing multiple brain pathways to produce a variety of behavioral sequelae, including addiction. Genetic factors contribute to over 50% of the risk for alcoholism and recent evidence points to a large number of genes with small effect sizes as the likely molecular basis for this disease. Recent progress in genomics (microarrays or RNA-Seq) and genetics has led to the identification of a large number of potential candidate genes influencing ethanol behaviors or alcoholism itself. To organize this complex information, investigators have begun to focus on the contribution of gene networks, rather than individual genes, for various ethanol-induced behaviors in animal models or behavioral endophenotypes comprising alcoholism. This chapter reviews some of the methods used for constructing gene networks from genomic data and some of the recent progress made in applying such approaches to the study of the neurobiology of ethanol. We show that rapid technology development in gathering genomic data, together with sophisticated experimental design and a growing collection of analysis tools are producing novel insights for understanding the molecular basis of alcoholism and that such approaches promise new opportunities for therapeutic development.

Research highlights

► Alcoholism is caused by gene networks influencing signaling in neural pathways affecting component behavioral vectors of alcoholism. ► Study of gene networks in ethanol behaviors entails combined use of genomics, genetics, and bioinformatics. ► Over-representation analysis of ethanol gene sets with existing biological pathways or forming novel gene-gene correlation networks can be used to identify ethanol networks. ► Ethanol might "induce" or increase interactions within a novel gene network or destabilize interactions in an existing network. ► Identifying central modulators of ethanol gene networks might provide novel approaches for intervention in alcoholism.

Introduction

Alcoholism is a prevalent and serious behavioral disease characterized by progression from intermittent social use of ethanol to abusive and uncontrolled consumption. As with other drug abuse disorders, the underlying neurobiological etiology of alcohol abuse and alcoholism is thought to involve long-lasting aberrant molecular plasticity in the central nervous system. Although multiple biological and environmental factors may converge en route to the manifestation and sustainability of this disease, altered function or expression of genes and gene networks are considered major factors contributing to long-lasting CNS changes causing the behavioral phenotype of alcoholism. The advent of high-throughput, unbiased approaches to studying genomic structure and expression, such as proteomics, DNA microarrays, whole genome SNP analysis and Next-Gen sequencing technologies, are providing new insights into gene sets involved in complex diseases. However, long lists of genes do not in themselves provide improved understanding of diseases such as alcoholism. New experimental approaches, combined with advanced statistical and bioinformatics support, have recently allowed organization of results from whole-genome studies into novel functional networks of genes related to the trait under study. Rather than focusing on an individual gene, the investigator can now simultaneously probe the entire genome to assess the interaction among individual genes. Provided with enough information a causal network may be constructed to predict functional mechanisms related to complex phenotypes (Zhu et al., 2008).

The overall framework leading to the full onset of alcohol dependence involves the progression from initial acute exposure toward compulsive drug use with frequent intermixed reoccurring bouts of tolerance and withdrawal (Koob and Volkow, 2010). The disease involves reward seeking, compulsivity and habit formation, aversive stimuli (e.g. withdrawal) and many other behavioral facets. There is likely no single causative factor in alcoholism, and thus each facet of this disorder may provide an important area of scientific inquiry. For example, interpreting the gene network structure of an organism undergoing withdrawal may impart novel mechanistic information contributing to the neuroadaptations driving relapse behavior. The overall phenotype of alcoholism could thus be considered a “behavioral vector” that is made up of multiple component vectors subserving endophenotypes as mentioned above (Fig. 1). Vectors of interacting neuronal/glial networks across multiple brain regions, in turn, likely control each of these endophenotype vectors. Drilling down yet further, these neural networks are ultimately controlled by regulation/function of multiple gene networks expressed within individual neurons or glial cells. As depicted in Fig. 1, this hierarchy of nested response vectors, extending from the molecular to the behavioral, likely explains the tremendous difficulty encountered in studying mechanisms of complex traits such as alcoholism. This degree of complexity also explains why efforts to correlate function/expression of single genes to a complex disease are exceedingly difficult. When considered in this light, it becomes apparent that progress in studying the mechanisms of complex disease requires a combined distillation of traits into endophenotypes and the amalgamation of brain regional gene expression/function into networks relevant to the trait vectors. Once we have mapped the network structure of these varying endophenotypes, we may be able to identify major genetic hubs for developing more rational pharmacotherapies in the treatment of alcoholism. This manuscript will review the process of using whole genome expression analysis to define gene networks that contribute to the complex nature of alcoholism.

Section snippets

Experimental design

Although this topic cannot be discussed in detail here, a variety of platforms exists for detecting differential gene expression. Under certain circumstances array platforms with a limited or more focused set of genes (e.g. arrays targeted against cytokine mRNA or protein) may be advantageous. However, the construction of gene networks as discussed in this chapter, generally requires a more inclusive approach that utilizes unbiased whole-genome arrays. In any case, the construction and analysis

Transcriptional networks of alcohol abuse and alcoholism

Genetic predisposition contributes an underlying vulnerability to the risk of developing alcohol dependence (Goodwin et al., 1974, Prescott and Kendler, 1999) as well as other substance abuse disorders. However, limited success has been achieved in the identification of candidate genes that contribute to the variable occurrence of alcohol dependence through linkage studies, single gene association or, more recently, genome-wide association studies (GWAS) (Johnson et al., 2006). This difficulty

Conclusion and closing remarks

Neuropsychiatric conditions including alcoholism are multifaceted diseases of complex origin. Extensive efforts across multiple fields of scientific research have actively sought to explain the origins of alcoholism; however, no solitary molecular mechanism has yet been established. Emerging evidence from a trove of genome-wide association and differential gene expression studies have illustrated that variants and expression differences in multiple genes can account for the manifestation of

Acknowledgments

The authors would like to thank Nate Bruce, Paul Vorster, and Alexander Putman for their contributions in generating some of the discussed BXD data, and Robert Williams at University of Tennessee Health Science Center for collaboration in the use of GeneNetwork. This work was supported in part by grants from the National Institute on Alcohol Abuse and Alcoholism (F31AA018615 to SPF; U01AA016662, U01AA016667, RO1AA014717, and P20AA017828 to MFM).

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