Secondary Data Analyses for Substance Abuse Research

描述（由申请人提供）：反复接触滥用药物会导致大脑奖赏回路的稳定变化，从而进一步导致依赖、敏感和渴望等行为异常。了解药物成瘾的分子生物学将为治疗药物成瘾和防止新成瘾者的流行提供改进的治疗方法。研究表明，大脑中基因表达的变化有助于稳定调节与药物成瘾有关的大脑奖赏回路。然而，这种调控背后的特定基因和转录机制仍然知之甚少。该应用程序的总体目标是通过对与药物成瘾相关的丰富生物学数据集的综合分析，为药物成瘾的分子机制提供更深入的见解。目前已经积累了大量的全基因组分子图谱数据集来研究药物成瘾。这些大规模的数据为产生重大的科学发现提供了巨大的机会，但也给数据分析带来了巨大的挑战。来自其他研究领域，特别是癌症研究的研究表明，有效整合各种分子分析数据集不仅可以提高数据分析的能力，而且可以更全面地了解生物过程。本研究的中心假设是，对药物成瘾相关分子图谱数据集的综合分析将导致对药物成瘾分子机制的系统理解和重要基因和途径的新发现。本研究将侧重于可卡因成瘾，因为可卡因是最突出的非法滥用药物之一，并且在可卡因成瘾中积累了相当多的分子分析数据。然而，所提出的方法将是通用的，适用于研究其他药物成瘾。总之，该项目将提供：(1)更好地理解可卡因成瘾的机制；(2)强大的统计/计算工具，用于综合分析吸毒成瘾数据；(3)建立全面的数据库，加强药物成瘾研究的广泛知识基础设施。

DESCRIPTION (provided by applicant): Repeated exposure to a drug of abuse causes stable changes in the reward circuitry of the brain, which will further lead to behavioral abnormalities such as dependence, sensitization and craving. Understanding the molecular biology of drug addiction will provide improved therapies to treat drug addiction and prevent the epidemic of new addicts. It has been shown that gene expression changes in brain contribute to the stable regulation of the brain's reward circuitry involved in drug addiction. However, the specific genes and the transcriptional mechanisms underlying such regulation remain poorly understood. The overall goal of this application is to provide deeper insights into the molecular mechanisms of drug addiction by integrated analysis of rich biological data sets related to drug addiction. A large amount of genome-wide molecular profiling datasets have been accumulated to study drug addiction. These large scale data provide great opportunities to generate significant scientific findings, but also great challenges for data analysis. Studies from other research areas, especially cancer research, have shown that integrating the various molecular profiling datasets effectively can not only increase the power of data analysis, but also give more comprehensive knowledge of the biological process. The central hypothesis of this study is that integrated analysis of drug addiction related molecular profiling datasets will lead to a systematic understanding of the molecular mechanisms and novel findings of important genes and pathways involved in drug addiction. This study will focus on cocaine addiction because cocaine is among the most prominent illicit drugs of abuse, and considerable molecular profiling data have been accumulated in cocaine addiction. The proposed methods, however, will be general and applicable to study other drugs of addiction. In summary, this project will provide: (1) better mechanistic understanding of cocaine addiction; (2) powerful statistical/computational tools for the integrated analysis of drug addiction data; and (3) a comprehensive database for strengthening the broader knowledge infrastructure for drug addiction research. PUBLIC HEALTH RELEVANCE: Understanding the molecular biology of drug addiction will provide improved therapies to treat drug addiction and prevent the epidemic of new addicts. This project will provide better mechanistic understanding of cocaine addiction, and a comprehensive database for strengthening the broader knowledge infrastructure for drug addiction research.