McDermott Jason E, Taylor Ronald C, Yoon Hyunjin, Heffron Fred
Computational Biology & Bioinformatics Group, Pacific Northwest National Laboratory, US Department of Energy, Richland, WA, USA.
J Comput Biol. 2009 Feb;16(2):169-80. doi: 10.1089/cmb.2008.04TT.
Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.
实验方法的最新进展提供了足够的数据，可将系统视为由相互连接的组件构成的大型网络。蛋白质 - 蛋白质相互作用网络的高通量测定已导致观察到拓扑瓶颈，即在网络中由高中心性定义的蛋白质，在具有诸如必需性等系统水平表型的蛋白质中富集。通过微阵列分析进行的全局转录谱分析已被广泛用于表征系统，例如，检查细胞对环境条件的反应以及基因突变的影响。这些转录组数据集已被用于基于共调控推断调控和功能关系网络。我们使用相关性上下文似然度（CLR）方法从鼠伤寒沙门氏菌病原体收集的两个数据集中推断网络：一个数据集来自一系列环境培养条件，另一个来自对15个在毒力方面至关重要的调节因子的缺失。从这些推断出的网络中识别出瓶颈基因和枢纽基因，并且我们首次表明这些基因比它们的非瓶颈或非枢纽对应基因在毒力方面更有可能是必需的。使用简单相似性度量（相关性和互信息）生成的网络并未表现出这种行为。总体而言，这项研究表明，从全局转录谱推断出的网络拓扑结构提供了有关瓶颈基因系统水平作用的信息。对两个CLR衍生网络之间差异的分析表明，瓶颈节点要么是系统状态之间转变的介质，要么是反映这些转变动态的哨兵。
