School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
BMC Bioinformatics. 2010 Dec 14;11 Suppl 11(Suppl 11):S5. doi: 10.1186/1471-2105-11-S11-S5.
Inference 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.
We 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.
The 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.
推断不同条件下导致基因表达变化的因果调控因子非常重要，但仍然颇具挑战性。迄今为止，大多数方法都使用转录因子（TF）的直接结合靶标将 TF 与表达谱联系起来。然而，TF 结合靶标与 TF 敲除受影响基因之间的低重叠限制了这些方法的效力。
我们开发了一种以 TF 为中心的下游基因集富集分析方法，以鉴定负责表达变化的潜在因果调控因子。我们构建了分层和多层调控模型，不仅使用 TF-DNA 相互作用，而且首次使用翻译后修饰（PTM）信息，推导出 TF 的可能下游基因集。我们在大规模 TF 敲除的一个表达数据集和另一个同时涉及 TF 敲除和 TF 过表达的数据集上验证了我们的方法。与仅使用 TF-DNA 相互作用的平面模型相比，我们的方法在大规模 TF 敲除数据中正确识别了 5 个更多实际受扰的 TF，在过表达数据中正确识别了 6 个更多受扰的 TF。还提供了三个受扰调控因子（SNF1、AFT1 和 SUT1）下游的潜在调控途径，以展示整合 TF-DNA 相互作用和 PTM 信息的多层调控模型的威力。此外，我们的方法成功识别了已知的重要 TF，并推断了一些参与从发酵到甘油基呼吸生长转变以及激素反应的新的潜在 TF。SUT1 和 AFT1 的下游调控途径也得到了其介导 TF 和/或“调节剂”蛋白的 mRNA 和/或磷酸化变化的支持。
结果表明，除了直接转录外，间接转录和翻译后调控也对 TF 扰动的影响负责，尤其是对 TF 过表达的影响。我们的方法推断出的许多 TF 都得到了文献的支持。多个 TF 调控模型可以为未来的实验提供新的假设。我们的方法为分析基因表达数据提供了一个有价值的框架，以在 TF-DNA 相互作用和 PTM 信息的背景下识别因果调控因子。
