(591a) Systems Analysis of Cellular Response to Alcohol Withdrawal

Alcoholism, a disease that develops as individuals adapt to the effects of alcohol to the point of requiring it for normal function, affects 14 million Americans and accounts for an estimated annual cost of $100 billion in healthcare and related productivity losses [1]. Once individuals are alcohol-adapted, serious consequences arise upon the cessation of alcohol intake, characterized by alcohol withdrawal syndrome (AWS). The symptoms of AWS involve dysregulation of the body's internal equilibrium, or homeostasis, and include increased blood pressure, increased heart rate, disturbances in the respiratory rhythm, anxiety, seizures, and in some cases cardiac arrhythmias or sudden death [2]. The nucleus tractus solitarius (NTS) is a key brain region in the regulation of homeostasis [3]. It integrates visceral afferents such as blood pressure and heart rate to regulate homeostasis, and it is directly and reciprocally connected to the central nucleus of the amygdala (CeA) [4], which has been identified as a nexus for the regulation of emotions such as fear and anxiety [5]. The NTS and the CeA must therefore be studied jointly for any truly systematic understanding of alcohol withdrawal. Previous studies of alcoholism and withdrawal have identified localized gene expression changes in the liver [6-9] and brain [10-15], including the NTS [10, 16-17] and CeA [18, 19]. From these studies, it is clear that a large number of genes are involved in the response to alcohol consumption. However, the response of this system to alcohol withdrawal, particularly the coordinated response of the genes underlying the functional disturbances of AWS, is largely unknown. To this end, our strategy is to study global gene expression in the NTS and CeA as observable indicators of the cellular system response to alcohol withdrawal, using microarray technology for the simultaneous collection of expression data for thousands of genes. This allows us to adopt a systems approach, where the network of genes can be studied for interacting responses instead of studying an individual gene. Our primary objective is to identify from such data, the regulatory network involved in withdrawal adaptation and AWS using this systems approach. In contrast to other systems, physiological responses in the brain typically involve small levels of differential gene expression. These small changes challenge the detectability limits of microarray technologies. Standard microarrays reliably measure two-fold or greater expression differentials, but for gene products with a functional role in the brain, a two-fold change is titanic. Therefore, we have developed and employ in-house technologies that provide the needed precision to detect more subtle gene expression changes [20] reliably. Our microarrays currently include over 8,800 annotated rat genes, and we apply these technologies to detect and quantify the differential gene expression of each of these genes in the NTS and the CeA. We obtain tissue samples from rat triplets?control, alcohol-adapted, and withdrawal?at various time points following the alcohol withdrawal. This experimental design, along with five biological replicates of each treatment to increase the statistical power of the analysis, provides us with data that adequately captures the gene expression dynamics following alcohol withdrawal. Our presentation will include the development and implications of this microarray time series experimental design. We will also present results for the 24 hour post-withdrawal time point and contrast the gene expression profiles for each region. We have previously shown that chronic alcoholism in rats produces differential expression in the NTS [10], but here we observe that these changes involve fewer genes and are generally smaller in magnitude than the gene expression changes produced upon alcohol withdrawal. This indicates that the alcohol-adapted state in the NTS and the CeA is quite distinct from the control state, more so than the gene expression profiles suggest. In our presentation, we will discuss these differences and a hypothesis regarding the transcriptional regulation underlying the observed differential expression. [1] Rich, B., Ed., The Dana Brain Daybook: What's New in Neuroscience 2(1), 1998. [2] K_hk_nen, S. Progress in Neuro-Psychopharm & Biological Psych 2004; 28: 937-41. [3] Doyle, FJ et al., Neural Processes for Control, Omidvar OM and DL Elliot, Eds. 1997, 89-123. [4] Loewy, AD. Central Regulation of Autonomic Functions, Loewy, AD and KM Spyer, Eds. Oxford University Press, 1990, 97. 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Accepted, BMC Genomics.