Modeling Gene Networks in Neuronal Adaptation to Alcohol

本提案通过提供对神经元的理解而具有公共卫生服务相关性。 酒精戒断引起的体内平衡失调和焦虑作用的潜在过程。这些 这种影响使酗酒者无法戒酒，事实上，突然戒酒不仅 令人不快，但本身就有很大的健康风险。我们将发现新的过程， 探索作为潜在的新目标，以改善酒精戒断的负面影响。 该提案是对PA-03 <$162“寻找与酒精相关的行为和风险的基因”的回应。 酒精中毒”，并寻求进行动物研究，以确定和表征与酒精有关的基因 戒断酒精对中枢神经系统的影响是由基因和基因产物介导的 在多种区域和特定神经元类型中差异表达。这些变化可以是不同的， 在特定的细胞类型中相反，或者可以仅在特定的细胞类型中发生，并且可以相对较大或非常小。 微妙。此外，很明显，对酒精戒断的反应并不局限于一个或几个基因， 而是一个涉及大量基因协调调控的复杂动态过程 随着时间我们建议收集一个全球基因表达数据集的时间序列， 神经元酒精戒断的基因组过程和机制，以发现和模拟 大脑中酒精戒断的基因网络和细胞控制。该方法的主要优点 我们提出的是新的和强大的微阵列技术和先进的生物信息学/数学 我们已经开发和使用的分析。我们提出了两个具体目标：（1）建立统计模型， 急性酒精戒断过程中的转录反应模式与急性酒精戒断过程中的转录反应模式相比， 慢性酒精状态和正常状态以及两个关键神经元群体内（2））识别进化的 基因调控网络活动和机制，以及衍生的信号传导途径活动， 酒精戒断使用计算方法来开发酒精过程的模型 撤回，并进行适当的实验验证研究，导致未来的研究。

The present proposal has public health service relevance by providing understanding of the neuronal processes underlying the dysregulation of homeostasis and anxiogenic effects of alcohol withdrawal. These effects keep alcoholics from quitting drinking, and in fact make sudden cessation of drinking not just unpleasant but a significant health risk itself. We will discover new processes involved in this that can be explored as potential new targets for amelioration of the negative consequences of alcohol withdrawal. This proposal is in response to PA-03¿162 "Finding Genes for Alcohol-Related Behaviors and Risk for Alcoholism" and seeks to perform animal studies to identify and characterize genes associated with alcohol withdrawal. Alcohol's effects on the central nervous system (CNS) are mediated by genes and gene products differentially expressed in a variety of regions and specific neuron types. These changes can be different or opposite in specific cell types, or may occur in only particular cell types, and may be relatively large or very subtle. Furthermore, it is clear that the response to alcohol withdrawal is not confined to one or a few genes, but rather is a complex, dynamical process involving the coordinate regulation of large numbers of genes over time. We propose to collect a time series of global gene expression datasets that will reveal the genomic processes and mechanisms of the alcohol withdrawal of neurons in order to discover and model the gene networks and cellular controls of alcohol withdrawal in the brain. The major strengths of the approach we propose are the novel and robust microarray technology and advanced bioinformatics/mathematical analyses we have developed and employed. We propose two specific aims: (1) develop statistical models of the patterns of transcriptional response during acute alcohol withdrawal across time as compared to the chronic alcohol state and the normal state and within two key neuronal populations (2)) Identify the evolving gene regulatory network activities and mechanisms, and derived signaling pathway activities by which a alcohol withdrawal develops using computational approaches to develop models of the process of alcohol withdrawal, and perform appropriate experimental validation studies leading to future studies.