Center for Genome Sciences and Systems Biology, Washington University, Saint Louis, Missouri 63108, USA.
Genome Res. 2013 Aug;23(8):1319-28. doi: 10.1101/gr.150904.112. Epub 2013 May 1.
A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.
理解基因组功能的一个关键步骤是确定哪些转录因子（TFs）调节每个基因。因此，人们投入了大量精力来绘制 TF 网络。在酿酒酵母中，大多数 TF 的蛋白-DNA 相互作用已经通过 ChIP-chip 被鉴定，并且大多数 TF 的缺失菌株的表达谱也已经完成。这些研究表明，在 TF 结合的启动子和 TF 缺失时表达变化的基因之间几乎没有重叠，这使得我们没有任何真核生物的明确 TF 网络，也没有有效地绘制功能 TF 网络的方法。本文描述了 NetProphet，这是一种从基因表达数据中提高网络映射效率的新算法。NetProphet 利用了 TF 网络本质的一个基本观察结果：破坏或过度表达 TF 时的反应在其直接靶标上最强，并随着网络的传播迅速消散。使用酿酒酵母数据，我们表明 NetProphet 仅使用基因表达数据就可以预测数千个直接的、功能的调节相互作用。NetProphet 为 TF 预测的靶标与 ChIP 暗示的靶标一样，至少与 TF 结合特异性匹配的位点一样可能。与大多数 ChIP 靶标不同，NetProphet 靶标也显示出功能调节的证据。这表明了一个惊人的结论：开始绘制直接的、功能的 TF-启动子相互作用的最佳方法可能不是通过测量结合。我们还表明，NetProphet 为几个酵母 TF 的功能提供了新的见解，包括一个研究得很好的 TF，Cbf1，和一个完全未被研究的 TF，Eds1。
