Substance use disorders (SUDs) are heritable psychiatric disorders with a significant genetic component. Opioid dependence, one of the most heritable SUDS, has reached epidemic proportions in the United States. Human genome-wide association studies (GWAS) are statistically underpowered to detect the majority of common genetic variation that contributes to opioid dependence. Discovery-based genetics in mammalian model organisms is a powerful complement to human GWAS and can uncover novel genetic factors, biological pathways, and gene networks underlying addiction traits. Mouse models are advantageous because they enable collection of the relevant brain tissue at the appropriate time points under controlled opioid dosing. Furthermore, gene editing permits the validation of functional variants in vivo within the same species on a controlled, genetic background. Reduced Complexity Crosses (RCCs) are genetic crosses between inbred mouse substrains that are nearly genetically identical and can vastly improve the speed at which causal genetic factors can be identified. Our primary objective is to use an RCC between BALB/c substrains to discover the genetic and molecular basis of opioid addiction-relevant traits at two stages of opioid dependence following repeated administration of the mu opioid receptor agonist oxycodone (OXY; the active ingredient of Oxycontin). We found robust differences between BALB/c substrains in opioid adaptive behaviors, including state-dependent learning of OXY-induced locomotor stimulation and reward following limited, low-dose administration (1.25 mg/kg, IP) as well as the emotional-affective component of opioid withdrawal and weight loss following repeated high-dose administration (40 mg/kg, IP).
In Aim 1, we will map quantitative trait loci (QTLs) underlying these OXY phenotypes in an RCC F2 cross.
In Aim 2, we will map QTLs controlling gene expression (eQTLs) in the relevant brain tissues of control F2 mice and in OXY-trained F2 mice. We will then nominate candidate causal genes and nucleotides underlying behavior by integrating eQTL with behavioral QTL analysis. To increase precision in assigning candidate variants with the regulation of gene expression and behavior and to identify biological pathways and opioid-adaptive gene networks in specific cell types, we will use single nucleus RNA- seq (snRNA-seq) of brain tissue following limited, low-dose OXY and repeated high-dose OXY.
In Aim 3, we will validate candidate functional variants underlying OXY phenotypes using CRISPR/Cas9 gene editing of each of the two alternate alleles onto each reciprocal substrain background. This approach will allow us to demonstrate both necessity and sufficiency of the quantitative trait nucleotides. The proposed studies will identify the genetic basis of unique opioid phenotypes across two stages of opioid dependence. Independent from gene discovery, these studies have broader application in revealing novel, actionable insight toward cellular adaptations at progressive stages of the opioid addiction process and potentially improving behavioral outcomes.
PROJECT RELEVANCE Opioid dependence is a heritable substance use disorder that is associated with tens of thousands of deaths each year in the United States. The proposed studies will use a mouse model of reduced genetic complexity combined with a treatment regimen that models the progressive stages of opioid addiction to rapidly discover novel genetic factors underlying initial drug reward and progressive drug withdrawal and weight loss. Combining genetic and genomic analysis at the cell type-specific level in the context of reduced genetic complexity will pinpoint specific downstream molecular adaptations that confer risk for opioid dependence and potentially inform actionable biological pathways and targets to improve behavioral outcomes.
