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      Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

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          Abstract

          Background

          Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol.

          Results

          Acute ethanol altered the expression of ∼2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity.

          Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.

          Conclusions

          The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders.

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          Most cited references78

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Genetic dissection of transcriptional regulation in budding yeast.

            To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae. Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.
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              Genetics of gene expression surveyed in maize, mouse and man.

              Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                12 April 2012
                : 7
                : 4
                : e33575
                Affiliations
                [1 ]Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [2 ]Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [3 ]Department of Neurology, Virginia Commonwealth University, Richmond, Virginia, United States of America
                [4 ]Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, United States of America
                [5 ]Department of Anatomy and Neurobiology, University of Tennessee Health Sciences, Memphis, Tennessee, United States of America
                University of Chicago, United States of America
                Author notes

                Conceived and designed the experiments: ARW MFM AHP. Performed the experiments: ARW AHP NAB PJV. Analyzed the data: ARW MFM AHP NAB PJV CAP MAL TPY. Contributed reagents/materials/analysis tools: RWW CAP MAL MFM. Wrote the paper: ARW MFM.

                Article
                PONE-D-11-17703
                10.1371/journal.pone.0033575
                3325236
                22511924
                38728898-d2d7-4fed-8ad7-84e9a5b4c7ea
                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                History
                : 8 September 2011
                : 15 February 2012
                Page count
                Pages: 16
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Functional Genomics
                Genome Expression Analysis
                Microarrays
                Systems Biology
                Genetics
                Animal Genetics
                Gene Networks
                Genomics
                Functional Genomics
                Model Organisms
                Animal Models
                Mouse
                Neuroscience
                Molecular Neuroscience
                Medicine
                Drugs and Devices
                Behavioral Pharmacology
                Drug Dependence
                Public Health
                Alcohol

                Uncategorized
                Uncategorized

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