Gene Center Munich and Center for integrated Protein Science CiPSM, Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany.
PLoS Comput Biol. 2012;8(6):e1002568. doi: 10.1371/journal.pcbi.1002568. Epub 2012 Jun 21.
The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors.
中介体是一个高度保守的、大型的多蛋白复合物，主要参与真核生物 mRNA 转录的调控。它作为一个通用转录因子，通过整合来自基因特异性激活物或抑制剂的调节信号，作用于 RNA 聚合酶 II。中介体亚基之间的内部相互作用网络，传递这些信号，在很大程度上是未知的。在这里，我们引入了 MC EMiNEM，这是一种用于检索对 mRNA 转录具有多效性影响的蛋白质之间功能依赖性的新方法。MC EMiNEM 基于嵌套效应模型（NEMs），这是一类概率图形模型，扩展了层次聚类的思想。它将模式跳跃蒙特卡罗（MC）采样与 NEMs 的期望最大化（EM）算法相结合，与现有方法相比，提高了敏感性。对酿酒酵母中四个中介体扰动研究的荟萃分析，其中三个是未发表的，为中介体信号网络提供了新的见解。除了已知的中介体亚基的模块化组织，MC EMiNEM 还揭示了其内部信息流的分层排序，这可能是通过复合物内的结构变化传递的。我们确定 Med7 的 N 端为外围实体，在扰动时只涉及局部结构变化，而 Med7 和 Med19 的 C 端似乎起着核心作用。MC EMiNEM 将中介体亚基与受影响最直接的基因相关联，结合基因集富集分析，使我们能够构建中介体亚基和转录因子的相互作用图。
