Department of Plant Biology and the Genome Center, College of Biological Sciences, University of California, Davis, Davis, California, United States of America.
PLoS Genet. 2013;9(10):e1003840. doi: 10.1371/journal.pgen.1003840. Epub 2013 Oct 3.
Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules.
理解基因调控网络需要在基因共表达网络中发现表达模块，并鉴定调控其表达的启动子基序和相应的转录因子。为此，一种常用的方法是基于聚类的自上而下的方法，即将网络聚类为一系列密集连接的片段，将这些片段视为表达模块，并从这些模块中提取启动子基序。在这里，我们描述了一种新的自下而上的方法，用于识别由基因启动子中已知顺式调控基序驱动的基因表达模块。对于特定的基序，根据其属于该基序调控的表达模块的概率对共表达网络中的基因进行排序。排序通过基序富集或基序位置偏倚分析进行。我们的结果表明，基序位置偏倚分析是一种用于全基因组基序分析的有效工具。提取包含排名最高基因的子网络，并对其进行固有基因表达模块的分析。该方法基于图形高斯模型，从拟南芥基因共表达网络中鉴定了 G-盒、W-盒、位点 II 和 MYB 基序的新型表达模块。新型表达模块包括参与维持功能、初级和次级代谢以及非生物和生物胁迫反应的模块。除了确认先前描述的模块外，我们还鉴定了包括新信号通路的模块。为了关联调节这些共表达模块中基因的转录因子，我们开发了一种新的报告系统。使用这种方法，我们评估了 MYB 转录因子-启动子相互作用在 MYB 基序模块内。
