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      RNA‐Seq Analysis of Genetic and Transcriptome Network Effects of Dual‐Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models

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          Abstract

          Background

          Genetic factors significantly affect alcohol consumption and vulnerability to withdrawal. Furthermore, some genetic models showing predisposition to severe withdrawal are also predisposed to low ethanol (EtOH) consumption and vice versa, even when tested independently in naïve animals.

          Methods

          Beginning with a C57BL/6J × DBA/2J F2 intercross founder population, animals were simultaneously selectively bred for both high alcohol consumption and low acute withdrawal (SOT line), or vice versa (NOT line). Using randomly chosen fourth selected generation (S4) mice ( N = 18‐22/sex/line), RNA‐Seq was employed to assess genome‐wide gene expression in ventral striatum. The MegaMUGA array was used to detect genome‐wide genotypic differences. Differential gene expression and the weighted gene co‐expression network analysis were implemented as described elsewhere (Genes Brain Behav 16, 2017, 462).

          Results

          The new selection of the SOT and NOT lines was similar to that reported previously (Alcohol Clin Exp Res 38, 2014, 2915). One thousand eight hundred and sixteen transcripts were detected as differentially expressed between the lines. For genes more highly expressed in the SOT line, there was enrichment in genes associated with cell adhesion, synapse organization, and postsynaptic membrane. The genes with a cell adhesion annotation included 23 protocadherins, Mpdz and Dlg2. Genes with a postsynaptic membrane annotation included Gabrb3, Gphn, Grid1, Grin2b, Grin2c, and Grm3. The genes more highly expressed in the NOT line were enriched in a network module (red) with annotations associated with mitochondrial function. Several of these genes were module hub nodes, and these included Nedd8, Guk1, Elof1, Ndufa8, and Atp6v1f.

          Conclusions

          Marked effects of selection on gene expression were detected. The NOT line was characterized by higher expression of hub nodes associated with mitochondrial function. Genes more highly expressed in the SOT aligned with previous findings, for example, Colville and colleagues (Genes Brain Behav 16, 2017, 462) that both high EtOH preference and consumption are associated with effects on cell adhesion and glutamate synaptic plasticity.

          Abstract

          SOT (Old English for drunkard) mice were selected for high alcohol preference/low withdrawal, with NOTs selected for the opposite phenotype. A cluster analysis showed genetic separation of the lines with SOTs genetically closer to the C57BL/6 founder, and NOTs genetically closer to the DBA/2 founder. The effects of selection on gene expression in SOTs and NOTs implicates genes involved in cell and synaptic interactions, energy metabolism and oxidative stress in alcohol preference and withdrawal.

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          Most cited references34

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics

            Mouse genetic resources include inbred strains, recombinant inbred lines, chromosome substitution strains, heterogeneous stocks, and the Collaborative Cross (CC). These resources were generated through various breeding designs that potentially produce different genetic architectures, including the level of diversity represented, the spatial distribution of the variation, and the allele frequencies within the resource. By combining sequencing data for 16 inbred strains and the recorded history of related strains, the architecture of genetic variation in mouse resources was determined. The most commonly used resources harbor only a fraction of the genetic diversity of Mus musculus, which is not uniformly distributed thus resulting in many blind spots. Only resources that include wild-derived inbred strains from subspecies other than M. m. domesticus have no blind spots and a uniform distribution of the variation. Unlike other resources that are primarily suited for gene discovery, the CC is the only resource that can support genome-wide network analysis, which is the foundation of systems genetics. The CC captures significantly more genetic diversity with no blind spots and has a more uniform distribution of the variation than all other resources. Furthermore, the distribution of allele frequencies in the CC resembles that seen in natural populations like humans in which many variants are found at low frequencies and only a minority of variants are common. We conclude that the CC represents a dramatic improvement over existing genetic resources for mammalian systems biology applications.
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              GeneNetwork: A Toolbox for Systems Genetics.

              The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.
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                Author and article information

                Contributors
                hitzeman@ohsu.edu
                Journal
                Alcohol Clin Exp Res
                Alcohol. Clin. Exp. Res
                10.1111/(ISSN)1530-0277
                ACER
                Alcoholism, Clinical and Experimental Research
                John Wiley and Sons Inc. (Hoboken )
                0145-6008
                1530-0277
                16 March 2020
                April 2020
                : 44
                : 4 ( doiID: 10.1111/acer.v44.4 )
                : 820-830
                Affiliations
                [ 1 ] Department of Behavioral Neuroscience VA Portland Health Care System Oregon Health & Science University Portland Oregon
                Author notes
                [*] [* ] Reprint requests: Robert Hitzemann, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, 3710 SW US Veterans Hospital Road, mail code RD40, Portland, OR 97239‐3098; Tel.: 503-273-5130; Fax: 503-494-6877; E‐mail: hitzeman@ 123456ohsu.edu

                Author information
                https://orcid.org/0000-0003-3059-2046
                https://orcid.org/0000-0002-6406-2783
                Article
                ACER14312
                10.1111/acer.14312
                7169974
                32090358
                0845e3f4-2bbe-42d9-9f39-ac5acb5ad4fc
                © 2020 The Authors. Alcoholism: Clinical & Experimental Research published by Wiley Periodicals, Inc. on behalf of Research Society on Alcoholism

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 05 June 2019
                : 13 February 2020
                Page count
                Figures: 5, Tables: 1, Pages: 11, Words: 8379
                Funding
                Funded by: National Institute on Alcohol Abuse and Alcoholism , open-funder-registry 10.13039/100000027;
                Award ID: P60 AA 010760
                Award ID: R01AA011114
                Award ID: R24AA020245
                Award ID: U01 AA013484
                Funded by: U.S. Department of Veterans Affairs , open-funder-registry 10.13039/100000738;
                Award ID: BX00022
                Categories
                Original Article
                Human and Animal Genetics
                Custom metadata
                2.0
                April 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.1 mode:remove_FC converted:12.05.2020

                Health & Social care
                alcohol,genetics,mouse,rna‐seq,transcriptome sequencing,weighted gene co‐expression network analysis,ventral striatum,dual‐trait selective breeding

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