Gene Variants for Nicotine Withdrawal Deficits in Learning

尼古丁戒断学习障碍的基因变异

基本信息

批准号：
9981713
负责人：
Thomas J Gould
金额：
$ 35.37万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-15 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9981713
关键词：
Abstinence Accounting Alleles American Animal Model Cerebellum Cessation of life Chronic Cognition Cognitive Cognitive deficits Data Data Analyses Databases Development Dorsal Economic Burden Female Gene Expression Genes Genetic Genetic Variation Genotype Goals Health Heritability Hippocampus (Brain)Homologous Gene Human Inbred Mouse Inbred Strains Mice Inbreeding Individual Intervention Laboratories Learning Maps Measures Methods Molecular Mus Nicotine Nicotine Dependence Nicotine Withdrawal Outcome Patients Pattern Phenotype Pilot Projects Population Populations at Risk Predisposition Quantitative Trait Loci Recombinant Inbred Strain Recombinants Relapse Reporting Rodent Model Signal Transduction Site Smoker Smoking Surveys Symptoms Taxes Time Tissue Banks United States Variant Withdrawal Withdrawal Symptom Work base cigarette smoking conditioned fear drug discovery effective therapy genetic variant genome database individualized medicine mRNA Expression male next generation sequencing novel therapeutics premature public health relevance relapse prediction relating to nervous system response self-directed learning sex smoking cessation statistics therapy development trait transcriptome transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): Nicotine addiction continues to be a major problem in the US with smoking accounting for the loss of 443,000 lives and an estimated $193 billion economic burden each year. These staggering statistics speak to important of identifying factors that contribute to nicotine addiction. Cognitive deficits such as disrupted learning are a major symptom of nicotine withdrawal, yet little is understood about the genetic factors that contribute to nicotine withdrawal-related changes in cognition. Identifying the neural and genetic substrates of nicotine withdrawal deficits in cognition will provide data to facilitate drug discovery and identify markers for at risk populations. Use of genetic data to identify at risk populations could also facilitate development of treatments that could increase the likelihood of maintaining abstinence. Recent data from a study examining nicotine withdrawal-related changes in hippocampus-dependent learning in mice demonstrated that genetics contributes to this cognitive withdrawal phenotype. As the specific genes involved in this withdrawal phenotype are unknown, it is the goal of this application to use next-generation sequencing to identify gene variants related to nicotine withdrawal deficits in learning. Studies in the proposal will use the recombinant inbred (RI) BXD strain to characterize phenotypic differences across BXD lines (Aim 1) and use that data for quantitative trait loci (QTL) analysis to identify genes associated with the nicotine withdrawal learning deficit phenotype (Aim 2). In addition, BXD databases will be examined to identify other phenotypes that are genetically correlated with the cognitive withdrawal phenotype (Aim 2). Finally, RNA-sequencing in BXD lines that show extreme phenotypic variation will be used to identify changes in the transcriptome associated with the nicotine withdrawal deficit in learning phenotype. These studies should significantly advance understanding of and treatment of nicotine addiction. Identifying genes and gene expression changes that are associated with nicotine withdrawal deficits in learning in the mouse will facilitate identifying if homologous genes in humans that are associated with nicotine withdrawal deficits in cognition. This could help practitioners tailor treatments to patients. In addition, identifying specific changes in the transcriptome associated with nicotine withdrawal deficits in learning will provide valuable information on the changes in cell signaling that contribute to nicotine withdrawal deficits in learning and this should aid in identifying potential targets for pharmacotherapeutic development.

描述（由适用提供）：在美国，尼古丁成瘾仍然是一个主要问题，因为吸烟占443,000人的损失，估计每年耗资1930亿美元的经济燃烧。这些惊人的统计数据表明，重要的是确定导致尼古丁成瘾的因素。认知定义了诸如中断学习之类的是尼古丁戒断的主要症状，但是几乎没有什么理解的，这些因素导致认知与尼古丁戒断相关的变化。确定尼古丁戒断的神经和遗传底物在认知中定义的定义将提供数据以促进药物发现并确定在风险种群中的标志。使用遗传数据来识别在风险中的人群还促进了可能增加戒酒可能性的治疗方法的发展。一项研究研究尼古丁戒断相关的研究的最新数据在小鼠中依赖海马依赖性学习表明遗传学有助于这种认知戒断表型。由于该戒断表型中涉及的特定基因尚不清楚，因此该应用是使用下一代测序来识别与尼古丁戒断缺陷有关的基因变异的目标。该提案中的研究将使用重组近交（RI）BXD菌株来表征BXD线之间的表型差异（AIM 1），并将该数据用于定量性状基因座（QTL）分析来识别与尼古丁戒断学习缺陷相关的基因（AIM 2）。此外，将检查BXD数据库，以识别与认知戒断表型相关的其他表型（AIM 2）。最后，在BXD线中显示出极端表型变异的RNA序列将用于识别与尼古丁戒断表型相关的转录组的变化。这些研究应大大提高对尼古丁成瘾的理解和治疗。识别与尼古丁戒断缺陷相关的基因和基因表达变化，将促进与认知中尼古丁戒断定义相关的人类中同源基因。这可以帮助从业人员为患者量身定制治疗。此外，确定与尼古丁戒断定义相关的转录组中的特定变化将提供有关细胞信号传导变化的有价值信息，这些变化有助于学习中的尼古丁提取定义，这应该有助于确定药物治疗性开发的潜在目标。