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通过结合两种微阵列分析方法的结果对低辐射剂量进行生物学检测。

Biological detection of low radiation doses by combining results of two microarray analysis methods.

作者信息

Mercier G, Berthault N, Mary J, Peyre J, Antoniadis A, Comet J-P, Cornuejols A, Froidevaux C, Dutreix M

机构信息

CNRS-UMR 2027, Institut Curie, Bâtiment 110, Centre Universitaire, F-91405 Orsay, France.

出版信息

Nucleic Acids Res. 2004 Jan 13;32(1):e12. doi: 10.1093/nar/gnh002.

Abstract

The accurate determination of the biological effects of low doses of pollutants is a major public health challenge. DNA microarrays are a powerful tool for investigating small intracellular changes. However, the inherent low reliability of this technique, the small number of replicates and the lack of suitable statistical methods for the analysis of such a large number of attributes (genes) impair accurate data interpretation. To overcome this problem, we combined results of two independent analysis methods (ANOVA and RELIEF). We applied this analysis protocol to compare gene expression patterns in Saccharomyces cerevisiae growing in the absence and continuous presence of varying low doses of radiation. Global distribution analysis highlights the importance of mitochondrial membrane functions in the response. We demonstrate that microarrays detect cellular changes induced by irradiation at doses that are 1000-fold lower than the minimal dose associated with mutagenic effects.

摘要

准确测定低剂量污染物的生物学效应是一项重大的公共卫生挑战。DNA微阵列是研究细胞内微小变化的有力工具。然而,该技术固有的低可靠性、重复样本数量少以及缺乏适用于分析如此大量属性(基因)的统计方法,妨碍了对数据的准确解读。为克服这一问题,我们结合了两种独立分析方法(方差分析和RELIEF)的结果。我们应用此分析方案比较了酿酒酵母在不存在和持续存在不同低剂量辐射情况下的基因表达模式。全局分布分析突出了线粒体膜功能在该反应中的重要性。我们证明,微阵列能够检测到比与诱变效应相关的最小剂量低1000倍的辐射剂量所诱导的细胞变化。

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