Vivek Kumar, Ph.D - US grants

? DESCRIPTION (provided by applicant): The goal of this proposal is to discover novel genetic variants that regulate sensitivity to an addictive drug, cocaine. Discovery of novel genes and alleles that mediate addiction will lend itself to discovery of mechanistic components in addiction pathways that can be targeted for treatment and intervention. We have taken a forward genetic mutagenesis approach to discover variants that regulate acute response to cocaine, hyperactivity and anxiety, all predictors of addictive behavior. Mutagenesis based screening in model animals is completely unbiased and has been bedrock in biology for over 50 years. We have conducted one of the largest screens to date for cocaine response and open field behaviors. From this screen of over 17,000 mice we have a collection of ten mutants and have genetically mapped three of these mutants to large chromosomal intervals. Now, we propose to leverage next-generation sequencing technology to find the exact mutation responsible for the phenovariance in these mutants. We will also carry our RNA-Seq to understand the transcriptomic program that is perturbed in these mutants. We will carry out an integrated multistep next- generation sequencing approach utilizing exome, whole genome, and RNA sequencing to study these mutants. Successful completion of this project will lead to detection of viable causal variants for addiction liability and identification of the pathway perturbed by th variant. This project has significant potential for discovery of novel genes, alleles, and pathways that regulate cocaine responses. From this information we will be able to identify new neural substrates of addiction, discover novel molecular components and establish a better mechanistic understanding of cocaine response and its proximity to potential druggable targets. We will also provide the addiction research community with new mouse models for altered drug response.

PROJECT SUMMARY/ABSTRACT The long-term goal of this project is to develop a technology that will allow addiction researchers to measure the influence of social and sleep behaviors on drug intake behavior in large-scale genomic experiments to discover genes that regulate vulnerability to addiction. To accomplish this we will develop and make publicly available a highly innovative technology that, for the first time, will allow researchers to continuously monitor complex sleep and social behaviors in groups of mice, using low-cost and easily scaled video equipment. While drug-addiction behaviors have well-established bidirectional relationships with these behaviors, the genetic etiologies have not been fully elucidated. This critical gap is primarily due to technological barriers that prevent reliable phenotyping of large numbers of animals for specific sleep states and social behaviors. Currently available methods for assessing different sleep states are designed to be used with isolated animals and do not measure multiple individuals in a group. Although social context is known to be an influence on addiction vulnerability, it has been largely studied by changing the housing environment and testing isolated animals. We will exploit techniques of artificial intelligence to develop a neural network-based machine-vision method of video analysis of these behaviors, designed to be used in an ethologically-relevant group setting, over long periods of time. This non-invasive method will be a significant advance in behavioral phenotyping, fulfilling the demands of the high-throughput genetic studies necessary for optimizing the mouse as model of addiction. Our first specific aim is to develop a machine-vision method to measure rapid eye movement (REM) sleep, nonREM (NREM) sleep and waking behaviors of all individuals, for use in either single or group housed conditions. We will train and validate our machine-vision networks using EEG and EMG recordings. To demonstrate the utility and assess the performance of our method we will compare two different mouse lines (the control mouse C57Bl/6J and a genetically altered strain, B6.129P2-Nos2 tm1Lau/J) that are known to differ in these sleep parameters. We will compare the sleep of these two mouse strains in both group and single housing. Our second specific aim is to extend our method to enable assessment of group social dynamics and to record all social and active behaviors over multiple days. This will allow us to test the bidirectional effects of social and sleep behaviors with the consumption of self-administered methamphetamine. We will assess the effect of initial social status and sleep quality on drug consumption and also measure how the social interactions and sleep patterns are changed after the drug is available. Successful completion of these aims will yield a validated technology with the capacity to provide detailed measurements of sleep and social behaviors in mice for use in large scale genetic studies, thereby significantly enhancing researchers' ability to identify genes associated with addiction susceptibility.

PROJECT SUMMARY/ABSTRACT Addiction is an enormous economic, personal, and social burden, costing over $600 billion per year in the U.S. Understanding vulnerability to addiction, and developing effective therapies, requires identifying the genes and pathways that mediate the addiction process. Our long-term goal is to develop novel genetic models for addiction-relevant phenotypes, and use these models to characterize the genetic mechanisms of addiction. We propose to leverage the Jackson Laboratory Knockout Mouse Project 2 (JAX KOMP2) pipeline to prioritize addiction gene candidates, and then characterize the effects of candidate gene knockouts on addiction-related behaviors and on addiction-relevant tissues. The JAX KOMP2 Phenotyping Center performs high-throughput phenotyping of knockout mice across organ systems using an efficient, broad-based testing pipeline including behavioral assays for emotionality and sleep, both predictive of addiction phenotypes. Here we propose to exploit this rich KOMP2 dataset to select a subset of lines with emotionality and neuronal phenotypes (e.g. deviant open field, light dark, hole board, tail suspension, prepulse inhibition, rotarod, electroconvulsive seizure threshold, or sleep phenotypes) and lacking metabolism and physiology phenotypes. Our preliminary data provide compelling evidence that gene deletions leading to emotionality phenotypes in the KOMP pipeline have addiction phenotypes. We will subject these lines to deep drug abuse?relevant phenotyping, including drug self-administration, transcriptional profiling from key neuronal tissues, and whole brain imaging. The data from these will be integrated using systems analysis. The successful completion of this project will yield dozens of novel mouse models with detailed transcriptome, and neuroanatomical profile to establish mechanistic insight into this behavioral abnormality. These can serve are a resource for the research community for therapeutics development.