Jin Suoqin, Li Yuanyuan, Pan Ruangang, Zou Xiufen
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, China.
Sci Rep. 2014 Jan 21;4:3799. doi: 10.1038/srep03799.
To gain insights into the pathogenesis of influenza A virus (IAV) infections, this study focused on characterizing the inflammatory network and identifying key proteins by combining high-throughput data and computational techniques. We constructed the cell-specific normal and inflammatory networks for H5N1 and H1N1 infections through integrating high-throughput data. We demonstrated that better discrimination between normal and inflammatory networks by network entropy than by other topological metrics. Moreover, we identified different dynamical interactions among TLR2, IL-1β, IL10 and NFκB between normal and inflammatory networks using optimization algorithm. In particular, good robustness and multistability of inflammatory sub-networks were discovered. Furthermore, we identified a complex, TNFSF10/HDAC4/HDAC5, which may play important roles in controlling inflammation, and demonstrated that changes in network entropy of this complex negatively correlated to those of three proteins: TNFα, NFκB and COX-2. These findings provide significant hypotheses for further exploring the molecular mechanisms of infectious diseases and developing control strategies.
为深入了解甲型流感病毒（IAV）感染的发病机制，本研究聚焦于通过结合高通量数据和计算技术来表征炎症网络并识别关键蛋白。我们通过整合高通量数据构建了H5N1和H1N1感染的细胞特异性正常网络和炎症网络。我们证明，与其他拓扑指标相比，网络熵在区分正常网络和炎症网络方面表现更佳。此外，我们使用优化算法确定了正常网络和炎症网络中TLR2、IL-1β、IL10和NFκB之间不同的动态相互作用。特别地，发现炎症子网具有良好的稳健性和多稳定性。此外，我们鉴定出一个复杂的TNFSF10/HDAC4/HDAC5，其可能在控制炎症中发挥重要作用，并证明该复合物的网络熵变化与三种蛋白TNFα、NFκB和COX-2的网络熵变化呈负相关。这些发现为进一步探索传染病的分子机制和制定控制策略提供了重要假设。
