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SCIENCE CHINA Information Sciences, Volume 59, Issue 7: 070102(2016) https://doi.org/10.1007/s11432-016-5580-2

Identifying disease modules and components of viral infections based on multi-layer networks

Yuanyuan LI1,2,3, Xiufen ZOU1,2,*
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  • ReceivedMar 31, 2016
  • AcceptedApr 18, 2016
  • PublishedJun 7, 2016

Abstract

With the emergence of multi-dimensional data, we can more comprehensively analyze the pathogenic mechanisms of complex diseases, and thereby improve the diagnosis, treatment and prevention of these diseases. This study presents a novel multi-layer network-based strategy that integrates multi-dimensional data, and identifies disease-related modules and components of viral infections. We first propose a systematic method that constructs a virus-host interaction network with three layers: a viral protein layer, a host protein layer and a host gene layer. This method combines the data of high-throughput gene expression, viral protein interactions, virus-host interactions, protein-protein interactions and transcriptional regulatory relationships. To extract the underlying mechanisms of viral infections from the multi-layer networks, we identify the intra-layer and cross-layer modules, and investigate the conserved modules across multiple datasets. The essential components in the multi-layer networks are detected by singular-value decomposition. The identified conserved modules and essential components are combined into a functional enrichment analysis that reveals their contributions to influenza virus replication. By this analysis, we elucidate the common and specific mechanisms of the replication cycles of two influenza strains. By combining the different layers of information, we can comprehensively understand pathogenic mechanisms of complex diseases.


Funded by

Major Research Plan of the National Natural Science Foundation of China(91530320)

Research Foundation of Hubei Province Department of Education(Q20151505)

Major Research Plan of the National Natural Science Foundation of China(91230118)


Acknowledgment

Acknowledgments

This work was supported by Major Research Plan of the National Natural Science Foundation of China (Grant Nos. 91530320, 91230118), and Research Foundation of Hubei Province Department of Education (Grant No. Q20151505).


References

[1] Hsu N Y, Ilnytska O, Belov G, et al. Viral reorganization of the secretory pathway generates distinct organelles for RNA replication. Cell, 2010, 141: 799-811 CrossRef Google Scholar

[2] Jin S Q, Zou X F. Construction of the influenza A virus infection-induced cell-specific inflammatory regulatory network based on mutual information and optimization. BMC Syst Biol, 2013, 7: 105-811 CrossRef Google Scholar

[3] Jin S Q, Li Y Y, Pan R G, et al. Characterizing and controlling the inflammatory network during influenza A virus infection. Sci Rep, 2014, 4: 3799-811 Google Scholar

[4] Li Y Y, Jin S Q, Lei L, et al. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis. Sci Rep, 2015, 5: 9283-811 CrossRef Google Scholar

[5] Barabasi A L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet, 2011, 12: 56-68 CrossRef Google Scholar

[6] Cantini L, Medico E, Fortunato S, et al. Detection of gene communities in multi-networks reveals cancer drivers. Sci Rep, 2015, 5: 17386-68 CrossRef Google Scholar

[7] Li W, Dai C, Liu C C, et al. Algorithm to identify frequent coupled modules from two-layered network series: application to study transcription and splicing coupling. J Comput Biol, 2012, 19: 710-730 CrossRef Google Scholar

[8] Shapira S D, Gat-Viks I, Shum B O, et al. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell, 2009, 139: 1255-1267 CrossRef Google Scholar

[9] Watanabe T, Watanabe S, Kawaoka Y. Cellular networks involved in the influenza virus life cycle. Cell Host Microbe, 2010, 7: 427-439 CrossRef Google Scholar

[10] Wang Y C, Chen B S. Integrated cellular network of transcription regulations and protein-protein interactions. BMC Syst Biol, 2010, 4: 20-439 CrossRef Google Scholar

[11] Srivastava A, Kumar S, Ramaswamy R. Two-layer modular analysis of gene and protein networks in breast cancer. BMC Syst Biol, 2014, 8: 81-439 CrossRef Google Scholar

[12] Barrett T, Suzek T O, Troup D B, et al. NCBI GEO: mining millions of expression profiles---database and tools. Nucl Acids Res, 2005, 33: D562-566 Google Scholar

[13] Hsu A C Y, Barr I, Hansbro P M, et al. Human influenza is more effective than avian influenza at antiviral suppression in airway cells. Amer J Resp Cell Mol Biol, 2011, 44: 906-913 CrossRef Google Scholar

[14] Josset L, Zeng H, Kelly S M, et al. Transcriptomic characterization of the novel avian-origin influenza A (H7N9) virus: specific host response and responses intermediate between avian (H5N1 and H7N7) and human (H3N2) viruses and implications for treatment options. mBio, 2014, 5: e01102-01113 Google Scholar

[15] Ozawa M, Kawaoka Y. Taming influenza viruses. Virus Res, 2011, 162: 8-11 CrossRef Google Scholar

[16] Chen W, Calvo P A, Malide D, et al. A novel influenza A virus mitochondrial protein that induces cell death. Nat Med, 2001, 7: 1306-1312 CrossRef Google Scholar

[17] Wise H M, Foeglein A, Sun J, et al. A complicated message: identification of a novel PB1-related protein translated from influenza A virus segment 2 mRNA. J Virol, 2009, 83: 8021-8031 CrossRef Google Scholar

[18] Lamesch P, Li N, Milstein S, et al. hORFeome v3. 1: a resource of human open reading frames representing over 10, 000 human genes. Genomics, 2007, 89: 307-315 Google Scholar

[19] Schaefer M H, Fontaine J F, Vinayagam A, et al. HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS ONE, 2012, 7: e31826-315 CrossRef Google Scholar

[20] Zheng G, Qian Z, Yang Q, et al. The combination approach of SVM and ECOC for powerful identification and classification of transcription factor. BMC Bioinform, 2008, 9: 282-315 CrossRef Google Scholar

[21] Zheng G, Tu K, Yang Q, et al. ITFP: an integrated platform of mammalian transcription factors. Bioinformatics, 2008, 24: 2416-2417 CrossRef Google Scholar

[22] Sun N, Carroll R J, Zhao H. Bayesian error analysis model for reconstructing transcriptional regulatory networks. Proc Nat Acad Sci USA, 2006, 103: 7988-7993 CrossRef Google Scholar

[23] Wang R S, Jin G, Zhang X S, et al. Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli. BMC Bioinform, 2009, 10: S6-7993 Google Scholar

[24] Geeven G, van Kesteren R E, Smit A B, et al. Identification of context-specific gene regulatory networks with GEMULA-gene expression modeling using LAsso. Bioinformatics, 2012, 28: 214-221 CrossRef Google Scholar

[25] Saito S, Hirokawa T, Horimoto K. Discovery of chemical compound groups with common structures by a network analysis approach (affinity prediction method). J Chem Inf Model, 2011, 51: 61-68 CrossRef Google Scholar

[26] Brunel H, Gallardo-Chacon J J, Buil A, et al. MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis. Bioinformatics, 2010, 26: 1811-1818 CrossRef Google Scholar

[27] Zhang X, Zhao X M, He K, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 2012, 28: 98-104 CrossRef Google Scholar

[28] Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods, 2012, 9: 471-472 CrossRef Google Scholar

[29] Xiao X Y, Zhang W, Zou X F. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks. PLoS ONE, 2015, 10: e0119294-472 CrossRef Google Scholar

[30] Zhang W, Zou X F. A new method for detecting protein complexes based on the three node cliques. IEEE/ACM Trans Comput Biol Bioinform, 2015, 12: 879-886 CrossRef Google Scholar

[31] Huang D W, Sherman B T, Lempicki R A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009, 4: 44-57 Google Scholar

[32] Cao M M, Wei C D, Zhao L L, et al. DnaJA1/Hsp40 is co-opted by influenza A virus to enhance its viral RNA polymerase activity. J Virol, 2014, 88: 14078-14089 CrossRef Google Scholar

[33] Momose F, Naito T, Yano K, et al. Identification of Hsp90 as a stimulatory host factor involved in influenza virus RNA synthesis. J Biol Chem, 2002, 277: 45306-45314 CrossRef Google Scholar

[34] Menche J, Sharma A, Kitsak M, et al. Uncovering disease-disease relationships through the incomplete interactome. Science, 2015, 347: 1257601-45314 CrossRef Google Scholar

[35] de Domenico M, Sole-Ribalta A, Omodei E, et al. Ranking in interconnected multilayer networks reveals versatile nodes. Nat Commun, 2015, 6: 6868-45314 CrossRef Google Scholar

[36] Tan J Y, Zou X F. Complex dynamical analysis of a coupled network from innate immune responses. Int J Bifurcat Chaos, 2013, 23: 1350180-45314 CrossRef Google Scholar

[37] Tan J Y, Zou X F. Optimal control strategy for abnormal innate immune response. Comput Math Methods Med, 2015, 2015: 386235-45314 Google Scholar

[38] Wang D J, Jin S Q, Wu F X, et al. Estimation of control energy and control strategies for complex networks. Adv Complex Syst, 2015, 18: 1550018-45314 CrossRef Google Scholar

[39] Zou X F, Niu L L, Jin S Q. The mathematical modeling and analysis for S1PR1-mediated cytokine signaling pathway. J Jiangxi Norm Univ (Nat Sci Ed), 2015, 39: 7-14 Google Scholar

[40] Jin S Q, Niu L L, Wang G, et al. Mathematical modeling and nonlinear dynamical analysis of cell growth in response to antibiotics. Int J Bifurcat Chaos, 2015, 25: 1540007-14 CrossRef Google Scholar

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