Modeling Gene Networks in Neuronal Adaptation to Alcohol

本提案通过提供对神经元的理解，具有公共卫生服务的相关性。 酒精戒断的体内平衡失调和焦虑效应的潜在过程。这些 效果使酗酒者不会戒酒，而且实际上使突然停止饮酒不仅仅是 令人不快，但本身就是一个重大的健康风险。我们将发现这一过程中涉及的新流程 探讨作为改善戒酒负面后果的潜在新目标。 这项建议是对PA-03？162《发现与酒精相关的行为和风险的基因》的回应 并试图进行动物研究，以识别和表征与酒精有关的基因 戒烟。酒精对中枢神经系统(CNS)的影响是由基因和基因产物介导的 在不同的区域和特定的神经元类型中差异表达。这些更改可以不同，也可以 相反，在特定的细胞类型中，或者可能只出现在特定的细胞类型中，并且可以相对较大或非常 很微妙。此外，很明显对戒酒的反应并不局限于一个或几个基因， 而是一个复杂的、动态的过程，涉及大量基因的协调调节 随着时间的推移。我们建议收集全球基因表达数据集的时间序列，以揭示 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 我们提出了新颖和健壮的微阵列技术和先进的生物信息学/数学 我们开发和使用的分析。我们提出了两个具体的目标：(1)建立统计模型 急性酒精戒断期间随时间的转录反应模式与 慢性酒精状态和正常状态以及两个关键神经元群体(2))识别进化 基因调控网络活动和机制，以及衍生的信号通路活动，通过这些活动 戒酒发展使用计算方法来建立酒精过程的模型 停药，并进行适当的实验验证研究，从而进行未来的研究。

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.