Microarrays do not yield direct evidence for functional connections between genes. However, transcription factors (TFs) and their binding sites (TFBSs) in promoters are important for inducing and coordinating changes in RNA levels, and thus represent the first layer of functional interaction. Similar to genes, TFs act only in context, which is why a TF/TFBS-based promoter analysis of genes needs to be done in the form of gene(TF)-gene networks, not individual TFs or TFBSs. In addition, integration of the literature and various databases (e.g. GO, MeSH, etc) allows the adding of genes relevant for the functional context of the data even if they were initially missed by the microarray as their RNA levels did not change significantly. Here, we outline a TF-TFBSs network-based strategy to assess the involvement of transcription factors in agonist signaling and demonstrate its utility in deciphering the response of human microvascular endothelial cells (HMEC-1) to leukemia inhibitory factor (LIF). Our strategy identified a central core of eight TFs, of which only STAT3 had previously been definitively linked to LIF in endothelial cells. We also found potential molecular mechanisms of gene regulation in HMEC-1 upon stimulation with LIF that allow for the prediction of changes of genes not used in the analysis. Our approach, which is readily applicable to a wide variety of expression microarray and next generation sequencing RNA-seq results, illustrates the power of a TF-gene networking approach for elucidation of the underlying biology.

中文翻译：

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通过整合转录因子聚焦基因相互作用和调控网络分析阐明微阵列数据中的功能背景。

微阵列不能为基因之间的功能联系提供直接证据。然而，启动子中的转录因子 (TF) 及其结合位点 (TFBS) 对于诱导和协调 RNA 水平的变化很重要，因此代表了功能相互作用的第一层。与基因类似，TF 仅在上下文中起作用，这就是为什么需要以基因 (TF)-基因网络的形式进行基于 TF/TFBS​​ 的基因启动子分析，而不是单个 TF 或 TFBS。此外，文献和各种数据库（例如 GO、MeSH 等）的整合允许添加与数据功能背景相关的基因，即使它们最初被微阵列遗漏，因为它们的 RNA 水平没有显着变化。这里，我们概述了一种基于 TF-TFBSs 网络的策略，以评估转录因子在激动剂信号传导中的参与，并证明其在破译人类微血管内皮细胞 (HMEC-1) 对白血病抑制因子 (LIF) 的反应中的效用。我们的策略确定了八个 TF 的中心核心，其中只有 STAT3 以前与内皮细胞中的 LIF 明确相关。我们还发现了 HMEC-1 在 LIF 刺激下基因调控的潜在分子机制，可以预测分析中未使用的基因的变化。我们的方法很容易适用于各种表达微阵列和下一代测序 RNA-seq 结果，说明了 TF 基因网络方法在阐明潜在生物学方面的力量。