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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., (© 2023 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.)

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.

Rationale: Few effective treatments exist for cocaine use disorders due to gaps in knowledge about its complex etiology. Genetically defined animal models provide a useful tool for advancing our understanding of the biological and genetic underpinnings of addiction-related behavior and evaluating potential treatments. However, many attempts at developing mouse models of behavioral disorders were based on overly simplified single gene perturbations, often leading to inconsistent and misleading results in pre-clinical pharmacology studies. A genetically complex mouse model may better reflect disease-related behaviors., Objectives: Screening defined, yet genetically complex, intercrosses of the Collaborative Cross (CC) mice revealed two lines, RIX04/17 and RIX41/51, with extreme high and low behavioral responses to cocaine. We characterized these lines as well as their CC parents, CC004/TauUnc and CC041/TauUnc, to evaluate their utility as novel model systems for studying the biological and genetic mechanisms underlying behavioral responses to cocaine., Methods: Behavioral responses to acute (initial locomotor sensitivity) and repeated (behavioral sensitization, conditioned place preference, intravenous self-administration) exposures to cocaine were assessed. We also examined the monoaminergic system (striatal tissue content and in vivo fast scan cyclic voltammetry), HPA axis reactivity, and circadian rhythms as potential mechanisms for the divergent phenotypic behaviors observed in the two strains, as these systems have a previously known role in mediating addiction-related behaviors., Results: RIX04/17 and 41/51 show strikingly divergent initial locomotor sensitivity to cocaine with RIX04/17 exhibiting very high and RIX41/51 almost no response. The lines also differ in the emergence of behavioral sensitization with RIX41/51 requiring more exposures to exhibit a sensitized response. Both lines show conditioned place preference for cocaine. We determined that the cocaine sensitivity phenotype in each RIX line was largely driven by the genetic influence of one CC parental strain, CC004/TauUnc and CC041/TauUnc. CC004 demonstrates active operant cocaine self-administration and CC041 is unable to acquire under the same testing conditions, a deficit which is specific to cocaine as both strains show operant response for a natural food reward. Examination of potential mechanisms driving differential responses to cocaine show strain differences in molecular and behavioral circadian rhythms. Additionally, while there is no difference in striatal dopamine tissue content or dynamics, there are selective differences in striatal norepinephrine and serotonergic tissue content., Conclusions: These CC strains offer a complex polygenic model system to study underlying mechanisms of cocaine response. We propose that CC041/TauUnc and CC004/TauUnc will be useful for studying genetic and biological mechanisms underlying resistance or vulnerability to the stimulatory and reinforcing effects of cocaine.

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., (© 2018 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.)

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.