Secondary Data Analyses for Substance Abuse Research

项目总结 反复接触滥用药物会导致奖赏回路的稳定变化 大脑，这将进一步导致行为异常，如依赖，敏感化 和渴望。了解药物成瘾的分子生物学将提供更好的 治疗药物成瘾和防止新成瘾者流行的治疗方法。它已经被证明了 大脑中基因表达的变化有助于大脑奖赏的稳定调节 与药物成瘾有关的回路。然而，特定的基因和转录 这种监管背后的机制仍然知之甚少。这个项目的总体目标是 该提案旨在通过以下方式为药物成瘾的分子机制提供更深入的见解 与药物成瘾相关的丰富生物数据集的综合分析。 大量的全基因组分子图谱数据集已经积累到 研究毒瘾。这些大规模数据提供了巨大的机会来产生显著的 科学发现，但也给数据分析带来了巨大的挑战。来自其他研究领域的研究， 尤其是癌症研究表明，整合各种分子图谱数据集 有效的不仅可以增加数据分析的能力，还可以给出更全面的 关于生物过程的知识。这项研究的中心假设是，综合 分析与药物成瘾相关的分子图谱数据集将导致系统性 了解重要基因和基因的分子机制和新发现 与药物成瘾有关的途径。这项研究将集中在可卡因成瘾上，因为可卡因 是最突出的非法滥用药物之一，以及大量的分子图谱数据 都是在可卡因成瘾中积累起来的。然而，拟议的方法将是一般性的。 并适用于其他成瘾药物的研究。总而言之，这个项目将提供：(1)更好 对可卡因成瘾的机理理解；(2)强大的统计/计算工具 药物成瘾数据的综合分析；以及(3)一个全面的数据库，用于 加强毒瘾研究的更广泛的知识基础设施。

Project summary 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 proposal 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.