Heritable variation in locomotion, reward sensitivity and impulsive behaviors in a genetically diverse inbred mouse panel

Abstract

Drugs of abuse, including alcohol and stimulants like cocaine, produce effects that are subject to individual variability, and genetic variation accounts for at least a portion of those differences. Notably, research in both animal models and human subjects point towards reward sensitivity and impulsivity as being trait characteristics that predict relatively greater positive subjective responses to stimulant drugs. Here we describe use of the eight Collaborative Cross (CC) founder strains and multiple CC strains to examine the heritability of reward sensitivity and impulsivity traits, as well as genetic correlations between these measures and existing addiction-related phenotypes. Methods. Strains were all tested for activity in an open field and reward sensitivity (intake of chocolate BOOST). Mice were then divided into two counterbalanced groups and underwent reversal learning (impulsive action and waiting impulsivity) or delay discounting (impulsive choice). Results. CC and founder mice demonstrate significant heritability for impulsive action, impulsive choice, waiting impulsivity, locomotor activity, and reward sensitivity, with each impulsive phenotype determined to be non-correlating, independent traits. This research was conducted within the broader, inter-laboratory effort of the Center for Systems Neurogenetics of Addiction (CSNA) to characterize CC and DO mice for multiple, cocaine abuse related traits. These data will facilitate the discovery of genetic correlations between predictive traits, which will then guide discovery of genes and genetic variants that contribute to addictive behaviors.

Syn3 Gene Knockout Negatively Impacts Aspects of Reversal Learning Performance

“…At a minimum, the dissociation between behavioral flexibility assessed in reversal (impaired) and premature responding (reduced) in Syn3 null mice suggest that these two traits are influenced by separate genetic architectures, as existing evidence already suggested (Nautiyal et al 2017). That hypothesis is further supported by our recent observation that measures of behavioral flexibility and premature responding are not genetically correlated in the Collaborative Cross recombinant inbred panel and their inbred founder strains (Bailey et al, 2021). Moreover, Syn3 negatively affected the ability to maintain the variable duration OR.…”

Syn3Gene Knockout Negatively Impacts Aspects of Reversal Learning Performance

“…At a minimum, the dissociation between behavioral flexibility assessed in reversal (impaired) and premature responding (reduced) in Syn3 null mice suggest that these two traits are influenced by separate genetic architectures, as existing evidence already suggested (Nautiyal et al 2017). That hypothesis is further supported by recent observations that measures of behavioral flexibility and premature responding are not genetically correlated in the Collaborative Cross recombinant inbred panel and their inbred founder strains (Bailey et al, 2021). Moreover, Syn3 negatively affected the ability to maintain the variable duration OR.…”

Reversal Learning Phenotypes are Linked with Novel Genetic Loci in Diversity Outbred Mice

“…Reversal learning is influenced by genetic variation in rodent populations that can be utilized to map associated genetic loci (Bailey et al, 2021; Laughlin et al, 2011). Laboratory rodent populations offer some distinct advantages in forward genetic approaches.…”

“…High genetic diversity can expand phenotypic distributions and provide unique opportunities for discovery of variants that drive extreme phenotypes (Chesler, 2014). Reversal learning is heritable in CC strains and their founders (Bailey et al, 2021), indicating these populations may be suitable for genetic dissection of this trait. The DO mice may thus be utilized for relatively high-resolution QTL mapping studies.…”

“…Here, we describe QTL mapping for reversal learning using DO mice. We also advance positional candidate discovery using reversal learning data from the CC strains along with complementary whole-transcriptome gene expression measures generated from bulk RNA sequencing of striatal tissue (previously described (Bailey et al, 2021; Saul et al, 2020) to advance positional candidate discovery. The striatum is a key brain region of interest in reversal learning performance and SUDs (Bergstrom et al, 2020; Clarke et al, 2008; Cools et al, 2009; Everitt & Robbins, 2013).…”