During the past 5 years of support, we have identified chromosomal regions (called quantitative trait loci, QTLs) that contain genes involved in ethanol responses including preference/consumption and withdrawal. We have progressed to fine map several of these QTLs to ~1 megabase (Mb) intervals. For a QTL on chromosome 4, we have progressed to identify the causative gene or quantitative trait gene (QTG) as Mpdz, and have identified high-quality QTG candidates for other ethanol response QTLs as well as candidates and gene networks implicated by knockout and microarray analyses. These gene candidates and gene networks now require rigorous testing to be accepted as having causal roles in ethanol response. An important goal of behavioral genomics is to identify individual genes and gene networks underlying phenotypic variation, and to elucidate the mechanism by which the gene or gene network affects behavior. In the next 5 years of support, the role of the Core will continue to provide expertise for both candidate gene hypothesis-driven (e.g., RNA interference [RNAi]) and hypothesis-generating (e.g., weighted gene co-expression networks) analyses in mice and nonhuman primates. Complementary strategies will emphasize identification and definitive proof of genes and gene networks involved in ethanol preference/consumption, withdrawal, and genetically correlated traits (including impulsivity). Genes will be tested for differential expression and/or sequence (coding and regulatory) using appropriate animal models. Priority for expression and sequence comparisons will be determined based on several criteria, including putative biological role and likely relevance to ethanol action. Database sequence information will also be used to design oligonucleotide primers that flank genes of interest for real-time quantitative PCR (QPCR) to test for genotype-differences in expression. In some cases, PCR amplification of the coding and regulatory regions from appropriate strains will be needed for DNA sequencing of PCR products to identify sequence differences. Both units of Core Component #3 (the Molecular Genetics Unit [MGU] and the Bioinformatics &Biostatistics Unit [BBU]) will be active in all years of requested Center support. The BBU will focus on statistical, computational and bioinformatics support, especially microarray data analysis, QTL analysis, gene network analyses and further central database development. Both units will support all five Center research components, as well as pilot projects in Component #10 and several other NIH (ROI, R37, UOI, KOI, F31 and F32) and VA grants.
This Core will facilitate hypothesis-driven (candidate gene) and hypothesis-generating (network) analyses to elucidate the genetic determinants of alcohol preference/consumption, withdrawal and impulsivity. In some cases the underlying gene will be the same in animal models (mice or nonhuman primates) and humans. In other cases animal model research will identify a gene network relevant in humans. Analyses of both candidate genes and networks will be more powerful than either alone to provide the interlocking levels of proof to move from gene/network to mechanism, and better prevent and treat alcohol abuse/dependence.
|Colville, Alexandre M; Iancu, Ovidiu D; Lockwood, Denesa R et al. (2018) Regional Differences and Similarities in the Brain Transcriptome for Mice Selected for Ethanol Preference From HS-CC Founders. Front Genet 9:300|
|Xu, Ting; Falchier, Arnaud; Sullivan, Elinor L et al. (2018) Delineating the Macroscale Areal Organization of the Macaque Cortex In Vivo. Cell Rep 23:429-441|
|Iancu, Ovidiu D; Colville, Alexander; Walter, Nicole A R et al. (2018) On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome. Addict Biol 23:196-205|
|Morales, Angelica M; Jones, Scott A; Ehlers, Alissa et al. (2018) Ventral striatal response during decision making involving risk and reward is associated with future binge drinking in adolescents. Neuropsychopharmacology 43:1884-1890|
|Gavin, David P; Hashimoto, Joel G; Lazar, Nathan H et al. (2018) Stable Histone Methylation Changes at Proteoglycan Network Genes Following Ethanol Exposure. Front Genet 9:346|
|Purohit, Kush; Parekh, Puja K; Kern, Joseph et al. (2018) Pharmacogenetic Manipulation of the Nucleus Accumbens Alters Binge-Like Alcohol Drinking in Mice. Alcohol Clin Exp Res 42:879-888|
|Müller-Oehring, Eva M; Kwon, Dongjin; Nagel, Bonnie J et al. (2018) Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains. Cereb Cortex 28:1049-1063|
|Iancu, Ovidiu Dan; Colville, Alex M; Wilmot, Beth et al. (2018) Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs. Alcohol Clin Exp Res :|
|Kafkafi, Neri; Agassi, Joseph; Chesler, Elissa J et al. (2018) Reproducibility and replicability of rodent phenotyping in preclinical studies. Neurosci Biobehav Rev 87:218-232|
|Qiu, J; Wagner, E J; Rønnekleiv, O K et al. (2018) Insulin and leptin excite anorexigenic pro-opiomelanocortin neurones via activation of TRPC5 channels. J Neuroendocrinol 30:|
Showing the most recent 10 out of 291 publications