de Duve Institute, Université Catholique de Louvain, MEXP74.30, avenue Hippocrates 74-75, B-1200 Brussels, Belgium.
Nucleic Acids Res. 2010 Jun;38(11):e120. doi: 10.1093/nar/gkq149. Epub 2010 Mar 9.
Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining.
从基因芯片数据中破译转录因子网络仍然具有挑战性。本研究提出了一种简单的方法，基于特征明确的靶基因从基因芯片数据中推断转录因子的调控。我们生成了一个包含与 2720 个靶基因和 6401 个实验验证的调控相关的转录因子的目录。对于每个调控，我们都包含了转录激活和抑制的区分。接下来，我们构建了一个工具（www.tfacts.org），该工具将提交的基因列表与目录中的靶基因进行比较，以检测受调控的转录因子。TFactS 在各种模型中使用已发表的受调控基因列表进行了验证，并与基于计算机启动子分析的工具进行了比较。接下来，我们分析了 NCI60 癌症基因芯片数据集，并显示了黑色素瘤中 SOX10、MITF 和 JUN 的调控。然后，我们进行了基因表达实验，比较了不同生长因子刺激的人成纤维细胞的基因表达反应。TFactS 预测了 PDGF-BB 对信号转导和转录因子的特异性激活，这一结果在实验中得到了证实。我们的结果表明，转录因子靶基因的表达水平构成了转录因子调控的稳健特征，可以有效地用于基因芯片数据挖掘。
