Gene Variants for Nicotine Withdrawal Deficits in Learning

 描述(由申请者提供)：尼古丁成瘾在美国仍然是一个主要问题，吸烟每年导致44.3万人死亡，估计造成1930亿美元的经济负担。这些令人震惊的统计数据表明，识别导致尼古丁成瘾的因素非常重要。学习障碍等认知缺陷是尼古丁戒断的主要症状，但人们对导致尼古丁戒断相关认知变化的遗传因素知之甚少。识别尼古丁戒断认知缺陷的神经和遗传基础将为促进药物发现和识别高危人群的标志物提供数据。使用遗传数据来识别高危人群可能 也促进了治疗方法的开发，这些治疗方法可能会增加保持禁欲的可能性。最近一项研究检测了小鼠海马区依赖学习中尼古丁戒断相关的变化，数据表明，遗传学对这种认知戒断表型有贡献。由于这种戒断表型涉及的特定基因尚不清楚，本应用程序的目标是使用下一代测序来识别与学习中尼古丁戒断缺陷相关的基因变异。建议中的研究将使用重组近交系(RI)BXD品系来表征BXD品系之间的表型差异(目标1)，并使用该数据进行数量性状基因座(QTL)分析，以确定与尼古丁戒断学习缺陷表型相关的基因(目标2)。此外，还将检查BXD数据库，以确定与认知撤退表型遗传相关的其他表型(目标2)。最后，BXD品系中表现出极端表型变异的RNA测序将被用来识别与学习表型中尼古丁戒断缺陷相关的转录组的变化。这些研究应该会大大促进对尼古丁成瘾的理解和治疗。识别与小鼠学习中尼古丁戒断缺陷相关的基因和基因表达变化，将有助于识别人类是否存在与认知尼古丁戒断缺陷相关的同源基因。这可以帮助从业者为患者量身定做治疗方案。此外，识别与学习中尼古丁戒断缺陷相关的转录组的特定变化将提供有关导致学习中尼古丁戒断缺陷的细胞信号变化的有价值的信息，这将有助于确定药物治疗开发的潜在靶点。

 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.