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.

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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Exploratory/Developmental Grants Phase II (R33)
Project #
5R33DA027592-04
Application #
8320136
Study Section
Special Emphasis Panel (NSS)
Program Officer
Wu, Da-Yu
Project Start
2009-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2014-08-31
Support Year
4
Fiscal Year
2012
Total Cost
$311,627
Indirect Cost
$83,674
Name
University of Texas Sw Medical Center Dallas
Department
Other Clinical Sciences
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Wang, Tao; Xie, Yang; Xiao, Guanghua (2014) dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol 15:R11
Yang, Jichen; Wang, Xinlei; Kim, Minsoo et al. (2014) Detection of candidate tumor driver genes using a fully integrated Bayesian approach. Stat Med 33:1784-800
Zhong, Rui; Kim, Jimi; Kim, Hyun Seok et al. (2014) Computational detection and suppression of sequence-specific off-target phenotypes from whole genome RNAi screens. Nucleic Acids Res 42:8214-22
Xiao, Guanghua; Ma, Shuangge; Minna, John et al. (2014) Adaptive prediction model in prospective molecular signature-based clinical studies. Clin Cancer Res 20:531-9
Yun, Jonghyun; Wang, Tao; Xiao, Guanghua (2014) Bayesian hidden Markov models to identify RNA-protein interaction sites in PAR-CLIP. Biometrics 70:430-40
Wang, Xinlei; Zang, Miao; Xiao, Guanghua (2013) Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models. Stat Med 32:2292-307
Chen, Min; Zang, Miao; Wang, Xinlei et al. (2013) A powerful Bayesian meta-analysis method to integrate multiple gene set enrichment studies. Bioinformatics 29:862-9
Tang, Hao; Xiao, Guanghua; Behrens, Carmen et al. (2013) A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res 19:1577-86