Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems
Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems
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DOI：
10.1038/s41598-018-38224-9
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发表时间：
2019-02-14
影响因子：
4.6
通讯作者：
Robertson, David L.
中科院分区：
文献类型：
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作者：
Ravindran, Vandana;Nacher, Jose C.;Robertson, David L.
近年来，控制理论已被应用于生物系统，目的是识别最小的分子相互作用集，可以驱动网络到所需的状态。然而，在蜂窝内网络中，如何在实践中实现控制尚不清楚。为了解决这一限制，我们使用病毒感染，特别是人类免疫缺陷病毒1型（HIV-1）和丙型肝炎病毒（HCV），作为一个范例来模拟感染细胞的控制。利用由6000多种人类蛋白质和34000多种定向相互作用组成的大型人类信号网络，我们比较了两种状态：正常/未感染和感染。我们的网络可控性分析展示了病毒如何通过主要针对现有的关键控制节点有效地控制动态组织的主机系统，比未感染的网络需要更少的节点。控制节点数量较少可能是为了优化利用病毒复制和/或参与宿主对感染反应的特定子系统。人体系统的病毒感染也允许在可用的网络控制模型之间进行区分，这表明，与最大匹配（MM）方法相比，最小支配集（MDS）方法通过识别大多数病毒蛋白作为关键驱动节点，更好地解释了感染期间生物信息和信号的组织方式。此外，MDS识别的宿主驱动节点分布在整个通路中，通过病毒和靶向宿主节点的高“控制中心性”实现对细胞的有效控制。我们的研究结果表明，与以前仅限于静态单状态网络的分析相比，控制理论对病毒利用宿主系统提供了更完整和动态的理解。
In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high 'control centrality' of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.