Discovery and validation of genes driving drug-intake and related behavioral traits in mice.
Tyler A RoyJason A BubierPrice E DicksonTroy D WilcoxJuliet NdukumJames W ClarkStacey J Sukoff RizzoJohn C CrabbeJames M DenegreKaren L SvensonRobert E BraunVivek KumarStephen A MurrayJacqueline K WhiteVivek M PhilipElissa J CheslerPublished in: Genes, brain, and behavior (2024)
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.
Keyphrases
- genome wide
- high throughput
- dna methylation
- copy number
- genome wide identification
- small molecule
- escherichia coli
- drug induced
- weight gain
- mouse model
- emergency department
- alcohol consumption
- sleep quality
- depressive symptoms
- genome wide analysis
- metabolic syndrome
- transcription factor
- data analysis
- insulin resistance