Kerns Robnet T, Miles Michael F
Department of Pharmacology/Toxicology, Virginia Commonwealth University, Richmond, VA, USA.
Methods Mol Biol. 2008;447:395-410. doi: 10.1007/978-1-59745-242-7_26.
DNA microarray studies offer a robust method for nonbiased analysis of whole genome messenger ribonucleic acid expression patterns. A growing number of studies have applied this experimental approach to studies on ethanol either in cell culture of animal models of ethanol exposure or self-administration. Expression profiling has identified novel gene networks responding to ethanol or differing across animal strains with differing responses to ethanol. Recent studies have shown benefit for meta-analysis of microarray data across different laboratories. Gene network analysis offers unique opportunities for understanding the molecular mechanisms of ethanol responses, toxicity and addiction. Eventually, such work may generate novel targets for future pharmacotherapy. To fully capitalize on the prom ise alluded to above, particularly in regard to meta-analysis of microarray data, it is critical that high quality standards are followed in the generation and analysis of microarray studies. This chapter will discuss experience of our laboratory in performing and analyzing microarray studies on ethanol, focusing discussion mainly on short oligonucleotide microarrays (Affymetrix). However, the general principals of technique and analysis that are discussed have broad applicability to other types of microarray platforms and experimental designs.
DNA微阵列研究为全基因组信使核糖核酸表达模式的无偏分析提供了一种强大的方法。越来越多的研究已将这种实验方法应用于乙醇研究,这些研究要么在乙醇暴露的动物模型的细胞培养中进行,要么在自我给药实验中开展。表达谱分析已经确定了对乙醇有反应或在对乙醇反应不同的动物品系之间存在差异的新型基因网络。最近的研究表明,对不同实验室的微阵列数据进行荟萃分析是有益的。基因网络分析为理解乙醇反应、毒性和成瘾的分子机制提供了独特的机会。最终,此类工作可能会为未来的药物治疗产生新的靶点。为了充分利用上述前景,特别是在微阵列数据的荟萃分析方面,在微阵列研究的生成和分析过程中遵循高质量标准至关重要。本章将讨论我们实验室在进行和分析乙醇相关微阵列研究方面的经验,主要围绕短寡核苷酸微阵列(Affymetrix)展开讨论。然而,所讨论的技术和分析的一般原则广泛适用于其他类型的微阵列平台和实验设计。