Regulation of gene expression is considered a plausible mechanism of drug addiction given the stability of behavioral abnormalities that define an addicted state. Numerous transcription factors, proteins that bind to regulatory regions of specific genes and thereby control levels of their expression, have been implicated in the addiction process over the past decade or two. Here we review the growing evidence for the role played by several prominent transcription factors, including a Fos family protein (DeltaFosB), cAMP response element binding protein (CREB), and nuclear factor kappa B (NFkappaB), among several others, in drug addiction. As will be seen, each factor displays very different regulation by drugs of abuse within the brain's reward circuitry, and in turn mediates distinct aspects of the addiction phenotype. Current efforts are geared toward understanding the range of target genes through which these transcription factors produce their functional effects and the underlying molecular mechanisms involved. This work promises to reveal fundamentally new insight into the molecular basis of addiction, which will contribute to improved diagnostic tests and therapeutics for addictive disorders.
Chromatin Assembly and Disassembly ; Cyclic AMP Response Element-Binding Protein ; Diagnostic Tests, Routine ; Epigenomics ; Gene Expression Regulation ; Humans ; NF-kappa B ; Nucleus Accumbens ; Phenotype ; Proteins ; Regulatory Sequences, Nucleic Acid ; Reward ; Street Drugs ; Substance-Related Disorders ; Transcription Factors ; Ventral Tegmental Area

A decade-long project, led by several international research groups, called the Encyclopedia of DNA Elements (ENCODE), recently released an unprecedented amount of data. The ambitious project covers transcriptome, cistrome, epigenome, and interactome data from more than 1,600 sets of experiments in human. To make use of this valuable resource, it is important to understand the information it represents and the techniques that were used to generate these data. In this review, we introduce the data that ENCODE generated, summarize the observations from the data analysis, and revisit a computational approach that ENCODE used to predict gene expression, with a focus on the human transcriptome and its association with chromatin modifications.
Chromatin ; DNA ; Gene Expression ; Humans ; Statistics as Topic ; Transcriptome