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Roy TA, Bubier JA, Dickson PE, Wilcox TD, Ndukum J, Clark JW, Sukoff Rizzo SJ, Crabbe JC, Denegre JM, Svenson KL, Braun RE, Kumar V, Murray SA, White JK, Philip VM, Chesler EJ. Discovery and validation of genes driving drug-intake and related behavioral traits in mice. Genes Brain Behav 2024; 23:e12875. [PMID: 38164795 PMCID: PMC10780947 DOI: 10.1111/gbb.12875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/08/2023] [Accepted: 11/12/2023] [Indexed: 01/03/2024]
Abstract
Substance use disorders are heritable disorders characterized by compulsive drug use, the biological mechanisms for which remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference and sensation seeking, are predictive of drug-use phenotypes, thereby implicating shared genetic mechanisms. High-throughput behavioral screening in knockout (KO) mice allows efficient discovery of the function of genes. We used this strategy in two rounds of candidate prioritization in which we identified 33 drug-use candidate genes based upon predisposing drug-naïve phenotypes and ultimately validated the perturbation of 22 genes as causal drivers of substance intake. We selected 19/221 KO strains (8.5%) that had a difference from control on at least one drug-naïve predictive behavioral phenotype and determined that 15/19 (~80%) affected the consumption or preference for alcohol, methamphetamine or both. No mutant exhibited a difference in nicotine consumption or preference which was possibly confounded with saccharin. In the second round of prioritization, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15/401 KO strains (3.7%, which included one gene from the first cohort) that differed most from controls for the predisposing phenotypes. 8 of 15 gene deletions (53%) affected intake or preference for alcohol, methamphetamine or both. Using multivariate and bioinformatic analyses, we observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.
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Affiliation(s)
- Tyler A. Roy
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Jason A. Bubier
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Price E. Dickson
- Joan C Edwards School of MedicineMarshall UniversityHuntingtonWest VirginiaUSA
| | - Troy D. Wilcox
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Juliet Ndukum
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - James W. Clark
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Stacey J. Sukoff Rizzo
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
- School of MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - John C. Crabbe
- VA Portland Health Care SystemOregon Health & Science UniversityPortlandOregonUSA
| | - James M. Denegre
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Karen L. Svenson
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Robert E. Braun
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Vivek Kumar
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Stephen A. Murray
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | | | - Vivek M. Philip
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
| | - Elissa J. Chesler
- Center for Addiction BiologyThe Jackson LaboratoryBar HarborMaineUSA
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Roy TA, Bubier JA, Dickson PE, Wilcox TD, Ndukum J, Clark JW, Rizzo SJS, Crabbe JC, Denegre JM, Svenson KL, Braun RE, Kumar V, Murray SA, White JK, Philip VM, Chesler EJ. DISCOVERY AND VALIDATION OF GENES DRIVING DRUG-INTAKE AND RELATED BEHAVIORAL TRAITS IN MICE. bioRxiv 2023:2023.07.09.548280. [PMID: 37503148 PMCID: PMC10369854 DOI: 10.1101/2023.07.09.548280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Substance use disorders (SUDs) are heritable disorders characterized by compulsive drug use, but the biological mechanisms driving addiction remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference, and sensation seeking, are predictive of drug-use phenotypes, implicating shared genetic mechanisms. Because of this relationship, high-throughput behavioral screening of predictive phenotypes in knockout (KO) mice allows efficient discovery of genes likely to be involved in drug use. We used this strategy in two rounds of screening in which we identified 33 drug-use candidate genes and ultimately validated the perturbation of 22 of these genes as causal drivers of substance intake. In our initial round of screening, we employed the two-bottle-choice paradigms to assess alcohol, methamphetamine, and nicotine intake. We identified 19 KO strains that were extreme responders on at least one predictive phenotype. Thirteen of the 19 gene deletions (68%) significantly affected alcohol use three methamphetamine use, and two both. In the second round of screening, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15 KO strains that were extreme responders across the predisposing drug-naïve phenotypes. Eight of the 15 gene deletions (53%) significantly affected intake or preference for three alcohol, eight methamphetamine or three both (3). We observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.
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Affiliation(s)
| | | | - Price E. Dickson
- Joan C Edwards School of Medicine, Marshall University Huntington, WV
| | | | | | | | - Stacey J. Sukoff Rizzo
- The Jackson Laboratory, Bar Harbor, ME
- University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - John C. Crabbe
- Oregon Health & Science University and VA Portland Health Care System, Portland, OR
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Dickson PE, Roy TA, McNaughton KA, Wilcox TD, Kumar P, Chesler EJ. Systems genetics of sensation seeking. Genes Brain Behav 2018; 18:e12519. [PMID: 30221471 PMCID: PMC6399063 DOI: 10.1111/gbb.12519] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 09/09/2018] [Accepted: 09/11/2018] [Indexed: 02/06/2023]
Abstract
Sensation seeking is a multifaceted, heritable trait which predicts the development of substance use and abuse in humans; similar phenomena have been observed in rodents. Genetic correlations among sensation seeking and substance use indicate shared biological mechanisms, but the genes and networks underlying these relationships remain elusive. Here, we used a systems genetics approach in the BXD recombinant inbred mouse panel to identify shared genetic mechanisms underlying substance use and preference for sensory stimuli, an intermediate phenotype of sensation seeking. Using the operant sensation seeking (OSS) paradigm, we quantified preference for sensory stimuli in 120 male and 127 female mice from 62 BXD strains and the C57BL/6J and DBA/2J founder strains. We used relative preference for the active and inactive levers to dissociate preference for sensory stimuli from locomotion and exploration phenotypes. We identified genomic regions on chromosome 4 (155.236‐155.742 Mb) and chromosome 13 (72.969‐89.423 Mb) associated with distinct behavioral components of OSS. Using publicly available behavioral data and mRNA expression data from brain regions involved in reward processing, we identified (a) genes within these behavioral QTL exhibiting genome‐wide significant cis‐eQTL and (b) genetic correlations among OSS phenotypes, ethanol phenotypes and mRNA expression. From these analyses, we nominated positional candidates for behavioral QTL associated with distinct OSS phenotypes including Gnb1 and Mef2c. Genetic covariation of Gnb1 expression, preference for sensory stimuli and multiple ethanol phenotypes suggest that heritable variation in Gnb1 expression in reward circuitry partially underlies the widely reported relationship between sensation seeking and substance use.
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Affiliation(s)
- Price E. Dickson
- Center for Systems Neurogenetics of AddictionThe Jackson LaboratoryBar HarborMaine
| | - Tyler A. Roy
- Center for Systems Neurogenetics of AddictionThe Jackson LaboratoryBar HarborMaine
| | | | - Troy D. Wilcox
- Center for Systems Neurogenetics of AddictionThe Jackson LaboratoryBar HarborMaine
| | - Padam Kumar
- Center for Systems Neurogenetics of AddictionThe Jackson LaboratoryBar HarborMaine
| | - Elissa J. Chesler
- Center for Systems Neurogenetics of AddictionThe Jackson LaboratoryBar HarborMaine
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Bubier JA, Wilcox TD, Jay JJ, Langston MA, Baker EJ, Chesler EJ. Cross-Species Integrative Functional Genomics in GeneWeaver Reveals a Role for Pafah1b1 in Altered Response to Alcohol. Front Behav Neurosci 2016; 10:1. [PMID: 26834590 PMCID: PMC4720795 DOI: 10.3389/fnbeh.2016.00001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 01/04/2016] [Indexed: 12/12/2022] Open
Abstract
Identifying the biological substrates of complex neurobehavioral traits such as alcohol dependency pose a tremendous challenge given the diverse model systems and phenotypic assessments used. To address this problem we have developed a platform for integrated analysis of high-throughput or genome-wide functional genomics studies. A wealth of such data exists, but it is often found in disparate, non-computable forms. Our interactive web-based software system, Gene Weaver (http://www.geneweaver.org), couples curated results from genomic studies to graph-theoretical tools for combinatorial analysis. Using this system we identified a gene underlying multiple alcohol-related phenotypes in four species. A search of over 60,000 gene sets in GeneWeaver's database revealed alcohol-related experimental results including genes identified in mouse genetic mapping studies, alcohol selected Drosophila lines, Rattus differential expression, and human alcoholic brains. We identified highly connected genes and compared these to genes currently annotated to alcohol-related behaviors and processes. The most highly connected gene not annotated to alcohol was Pafah1b1. Experimental validation using a Pafah1b1 conditional knock-out mouse confirmed that this gene is associated with an increased preference for alcohol and an altered thermoregulatory response to alcohol. Although this gene has not been previously implicated in alcohol-related behaviors, its function in various neural mechanisms makes a role in alcohol-related phenomena plausible. By making diverse cross-species functional genomics data readily computable, we were able to identify and confirm a novel alcohol-related gene that may have implications for alcohol use disorders and other effects of alcohol.
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Affiliation(s)
| | | | - Jeremy J Jay
- The Jackson LaboratoryBar Harbor, ME, USA; Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, North Carolina Research CampusKannapolis, NC, USA
| | - Michael A Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee Knoxville, TN, USA
| | - Erich J Baker
- School of Engineering and Department of Computer Science, Baylor University Waco, TX, USA
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