TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information.
TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information.
复制标题
以TF为中心的下游基因集富集分析：通过整合TF-DNA相互作用和蛋白质翻译后修饰信息来推断因果调节因子
DOI：
10.1186/1471-2105-11-s11-s5
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发表时间：
2010-12-14
影响因子：
3
通讯作者：
Xie L
中科院分区：
文献类型：
--
作者：
Liu Q;Tan Y;Huang T;Ding G;Tu Z;Liu L;Li Y;Dai H;Xie L
背景不同条件下基因表达变化的因果调节因子的推断是非常重要的，但仍然相当具有挑战性。迄今为止，大多数方法使用转录因子（TF）的直接结合靶标来将TF与表达谱相关联。然而，TF和TF敲除受影响的基因的结合目标之间的低重叠限制了这些方法的力量。ResultsWe开发了一个TF为中心的下游基因集富集分析方法，以确定潜在的因果调节器负责表达的变化。我们构建了层次和多层调控模型，以获得可能的下游基因集的TF不仅使用TF-DNA相互作用，而且，第一次，翻译后修饰（PTM）的信息。我们在一个大规模TF敲除的表达数据集和另一个涉及TF敲除和TF过表达的数据集上验证了我们的方法。与单独使用TF-DNA相互作用的平面模型相比，我们的方法正确地识别了大规模TF敲除数据中的五个实际扰动TF和过表达数据中的六个扰动TF。给出了SNF 1、AFT 1和SUT 1这三个受干扰调节子下游的潜在调节途径，以证明整合TF-DNA相互作用和PTM信息的多层调节模型的能力。此外，我们的方法成功地确定了已知的重要TF，并推断出一些新的潜在TF参与从发酵到甘油为基础的呼吸生长和信息素反应的过渡。SUT 1和AFT 1的下游调控途径也得到了其介导的TF和/或“调节剂”蛋白的mRNA和/或磷酸化变化的支持。结论结果表明，除了直接转录外，间接转录和翻译后调节也是TF扰动的影响，特别是TF过表达的原因。我们的方法推断的许多TF文献支持。多种TF调节模型可能会为未来的实验带来新的假设。我们的方法提供了一个有价值的框架，用于分析基因表达数据，以确定在TF-DNA相互作用和PTM信息的背景下的因果调节器。
BackgroundInference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.ResultsWe developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators— SNF1, AFT1 and SUT1 —were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or “modulator” proteins.ConclusionsThe results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.