Time-dependent changes in ARE-driven gene expression by use of a noise-filtering process for microarray data
Time-dependent changes in ARE-driven gene expression by use of a noise-filtering process for microarray data
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DOI：
10.1152/physiolgenomics.00003.2002
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发表时间：
2002-06-03
影响因子：
4.6
通讯作者：
Johnson, JA
中科院分区：
文献类型：
--
作者：
Li, J;Johnson, JA
本研究旨在鉴定叔丁基对苯二酚（tBHQ）诱导的抗氧化反应元件（ARE）驱动基因的时间依赖性基因表达谱。介绍了一组简单的噪声过滤方法来评估和最小化微阵列数据集的方差。通过大规模寡核苷酸微阵列分析由tBHQ（10 μ M）诱导的IMR-32人神经母细胞瘤细胞中的基因表达。秩分析用于确定从数据集中消除假阳性所需的独立样本的可接受数量。通过使用3x 3矩阵比较实现了通过秩分析的基因数量的显著减少。根据平均差异变化的变异系数评价重现性。这些分析的完成表明，在所检查的9，670个基因中，有101个在处理4 h至48 h期间显示出动态变化。由于某些ARE驱动的基因已经被确定，基因聚类可能会根据相似的调控将它们分组在一起。自组织图将tBHQ诱导的基因分为12个（4x 3）不同的簇。那些先前鉴定的ARE驱动的基因被证明分为不同的簇。由于所有潜在的ARE驱动的基因没有聚集在一起，我们推测，多种转录因子和/或多种信号转导途径有助于ARE的转录激活。总之，许多新的潜在ARE驱动的基因在这项研究中被确定。它们在解毒和抗氧化防御、神经元增殖和分化以及信号转导中起作用。应用于这些微阵列数据的噪声过滤过程，因此，已被证明是非常有用的识别ARE驱动基因表达的时间依赖性变化。
The current study was designed to identify the time-dependent gene expression profiles of antioxidant responsive element (ARE)- driven genes induced by tert-butylhydroquinone (tBHQ). A set of simple noise-filtering methods was introduced to evaluate and minimize the variance of microarray datasets. Gene expression induced by tBHQ (10 muM) in IMR-32 human neuroblastoma cells was analyzed by means of large-scale oligonucleotide microarray. Rank analysis was used to determine the acceptable number of independent samples necessary to eliminate false positives from the dataset. A dramatic reduction in the number of genes passing the rank analysis was achieved by using a 3 x 3 matrix comparison. Reproducibility was evaluated based on the coefficient of variation for average difference change. Completion of these analyses revealed that 101 of the 9,670 genes examined showed dynamic changes with treatment ranging from 4 h to 48 h. Since certain ARE-driven genes have been already identified, gene clustering would presumably group them together based on similar regulation. Self-organizing map grouped the genes induced by tBHQ into 12 (4x3) distinct clusters. Those previously identified ARE-driven genes were shown to group into different clusters. Since all potential ARE-driven genes did not cluster together, we speculate that multiple transcription factors and/or multiple signal transduction pathways contribute to transcriptional activation of the ARE. In conclusion, many novel potential ARE-driven genes were identified in this study. They function in detoxification and antioxidant defense, neuronal proliferation and differentiation, and signal transduction. The noise-filtering process applied to these microarray data, therefore, has proven to be very useful in identification of the time-dependent changes in ARE-drive gene expression.