Hypergraph models of biological networks to identify genes critical to pathogenic viral response

BMC Bioinformatics - Tập 22 - Trang 1-21 - 2021
Song Feng1, Emily Heath2, Brett Jefferson3, Cliff Joslyn3,4, Henry Kvinge3, Hugh D. Mitchell1, Brenda Praggastis3, Amie J. Eisfeld5, Amy C. Sims6, Larissa B. Thackray7, Shufang Fan5, Kevin B. Walters5, Peter J. Halfmann5, Danielle Westhoff-Smith5, Qing Tan7, Vineet D. Menachery8,9, Timothy P. Sheahan8, Adam S. Cockrell10, Jacob F. Kocher8, Kelly G. Stratton1, Natalie C. Heller3, Lisa M. Bramer1, Michael S. Diamond7,11,12, Ralph S. Baric8, Katrina M. Waters1,13, Yoshihiro Kawaoka5,14,15,16, Jason E. McDermott1,17, Emilie Purvine3
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, USA
2Department of Mathematics, University of Illinois, Urbana-Champaign, USA
3Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, USA
4Systems Science Program, Portland State University, Portland, USA
5Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, Madison, USA
6Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, USA
7Department of Medicine, Washington University School of Medicine, Saint Louis, USA
8Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, USA
9Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, USA
10KNOWBIO LLC., Durham, USA
11Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, USA
12Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, USA
13Department of Comparative Medicine, University of Washington, Seattle, USA
14Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo, Japan
15ERATO Infection-Induced Host Responses Project, Saitama, Japan
16Department of Special Pathogens, International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, Tokyo, Japan
17Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, USA

Tóm tắt

Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Tài liệu tham khảo

McDermott JE, Mitchell HD, Gralinski LE, Eisfeld AJ, Josset L, Bankhead A, Neumann G, Tilton SC, Schäfer A, Li C, et al. The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus. BMC Syst Biol. 2016;10(1):93. https://doi.org/10.1186/s12918-016-0336-6. Mitchell HD, Eisfeld AJ, Stratton KG, Heller NC, Bramer LM, Wen J, McDermott JE, Gralinski LE, Sims AC, Le MQ, Baric RS, Kawaoka Y, Waters KM. The role of EGFR in influenza pathogenicity: multiple network-based approaches to identify a key regulator of non-lethal infections. Front Cell Dev Biol. 2019;7:200. https://doi.org/10.3389/fcell.2019.00200. Tran VD, Sperduti A, Backofen R, Costa F. Heterogeneous networks integration for disease gene prioritization with node kernels. Bioinformatics. 2020;36(9):2649–56. https://doi.org/10.1093/bioinformatics/btaa008. Adourian A, Jennings E, Balasubramanian R, Hines WM, Damian D, Plasterer TN, Clish CB, Stroobant P, McBurney R, Verheij ER, Bobeldijk I, van der Greef J, Lindberg J, Kenne K, Andersson U, Hellmold H, Nilsson K, Salter H, Schuppe-Koistinen I. Correlation network analysis for data integration and biomarker selection. Mol Biosyst. 2008;4(3):249–59. https://doi.org/10.1039/B708489G. Diamond DL, Syder AJ, Jacobs JM, Sorensen CM, Walters KA, Proll SC, McDermott JE, Gritsenko MA, Zhang Q, Zhao R, Metz TO, Camp DG, Waters KM, Smith RD, Rice CM, Katze MG. Temporal proteome and lipidome profiles reveal hepatitis C virus-associated reprogramming of hepatocellular metabolism and bioenergetics. PLoS Pathog. 2010;6(1):56. https://doi.org/10.1371/journal.ppat.1000719. Maier TV, Lucio M, Lee LH, VerBerkmoes NC, Brislawn CJ, Bernhardt J, Lamendella R, McDermott JE, Bergeron N, Heinzmann SS, Morton JT, Gonzalez A, Ackermann G, Knight R, Riedel K, Krauss RM, Schmitt-Kopplin P, Jansson JK. Impact of dietary resistant starch on the human gut microbiome, metaproteome, and metabolome. MBio. 2017. https://doi.org/10.1128/mBio.01343-17. McClure RS, Wendler JP, Adkins JN, Swanstrom J, Baric R, Kaiser BLD, Oxford KL, Waters KM, McDermott JE. Unified feature association networks through integration of transcriptomic and proteomic data. PLoS Comput Biol. 2019. https://doi.org/10.1371/journal.pcbi.1007241. Lempp M, Farke N, Kuntz M, Freibert SA, Lill R, Link H. Systematic identification of metabolites controlling gene expression in E. coli. Nat Commun. 2019;10(1):4463. https://doi.org/10.1038/s41467-019-12474-1. McDermott JE, Jarman K, Taylor R, Lancaster M, Shankaran H, Vartanian KB, Stevens SL, Stenzel-Poore MP, Sanfilippo A. Modeling dynamic regulatory processes in stroke. PLoS Comput Biol. 2012. https://doi.org/10.1371/journal.pcbi.1002722. McDermott JE, Oehmen CS, McCue LA, Hill E, Choi DM, Stockel J, Liberton M, Pakrasi HB, Sherman LA. A model of cyclic transcriptomic behavior in the cyanobacterium Cyanothece sp. ATCC 51142. Mol Biosyst. 2011;7(8):2407–18. https://doi.org/10.1039/c1mb05006k. Barabási A-L. Network science. UK: Cambridge University Press; 2016. Iacopini I, Petri G, Barrat A, Latora V. Simplicial models of social contagion. Nat Commun. 2019;10:2485. https://doi.org/10.1038/s41467-019-10431-6. Klamt S, Haus U-U, Theis F. Hypergraphs and cellular networks. PLoS Comput Biol. 2009;5(5):56. https://doi.org/10.1371/journal.pcbi.1000385. Patania A, Petri G, Vaccarino F. The shape of collaborations. EPJ Data Sci. 2017;6(1):18. https://doi.org/10.1140/epjds/s13688-017-0114-8. Javidian MA, Wang Z, Lu L, Valtorta M. On a hypergraph probabilistic graphical model. Ann Math Artif Intell. 2020. https://doi.org/10.1007/s10472-020-09701-7. Joslyn CA, Aksoy S, Callahan TJ, Hunter L, Jefferson B, Praggastis B, Purvine EA, Tripodi IJ. Hypernetwork science: from multidimensional networks to computational topology. In: International conference on complex systems (ICCS 2020). 2020. https://arxiv.org/abs/2003.11782. Leal W, Restrepo G. Formal structure of periodic system of elements. Proc R Soc A. 2019. https://doi.org/10.1098/rspa.2018.0581. Minas M. Hypergraphs as a uniform diagram representation model. In: Proceedings of the 6th international workshop on theory and applications of graph transformations. Berlin: Springer; 1998. p. 281–95. https://doi.org/10.1007/978-3-540-46464-8_20. Chitra U. Random walks on hypergraphs with applications to disease-gene prioritization. PhD thesis, Brown University. 2017. Tran L. Hypergraph and protein function prediction with gene expression data. 2012. arXiv preprint arXiv:1212.0388. Ramadan E, Tarafdar A, Pothen A. A hypergraph model for the yeast protein complex network. In: 18th International parallel and distributed processing symposium. 2004. p. 189. https://doi.org/10.1109/IPDPS.2004.1303205. Zhou W, Nakhleh L. Properties of metabolic graphs: biological organization or representation artifacts? BMC Bioinf. 2011. https://doi.org/10.1186/1471-2105-12-132. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Microbiol. 2010. https://doi.org/10.1038/nrmicro2419. McDermott JE, Vartanian KB, Mitchell H, Stevens SL, Sanfilippo A, Stenzel-Poore MP. Identification and validation of ifit1 as an important innate immune bottleneck. PLoS ONE. 2012;7(6):36465. Menachery VD, Eisfeld AJ, Schafer A, Josset L, Sims AC, Proll S, Fan S, Li C, Neumann G, Tilton SC, Chang J, Gralinski LE, Long C, Green R, Williams CM, Weiss J, Matzke MM, Webb-Robertson BJ, Schepmoes AA, Shukla AK, Metz TO, Smith RD, Waters KM, Katze MG, Kawaoka Y, Baric RS. Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses. MBio. 2014;5(3):01174–14. https://doi.org/10.1128/mBio.01174-14. Basters A, Knobeloch K-P, Fritz G. Usp18-a multifunctional component in the interferon response. Biosci Rep. 2018;38(6):56. Mitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson B-JM, Tilton SC, Tchitchek N, Josset L, Li C, et al. A network integration approach to predict conserved regulators related to pathogenicity of influenza and sars-cov respiratory viruses. PLoS ONE. 2013. https://doi.org/10.1371/journal.pone.0069374. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):56. https://doi.org/10.1371/journal.pbio.0050008. Hagberg A, Swart P, Chult DS. Exploring network structure, dynamics, and function using NetworkX. Technical report, Los Alamos National Lab.(LANL), Los Alamos. 2008. Dyer MD, Murali TM, Sobral BW. The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog. 2008;4(2):56. https://doi.org/10.1371/journal.ppat.0040032. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102. Kim EY, Ashlock D, Yoon SH. Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks. BMC Bioinf. 2019;20(1):328. https://doi.org/10.1186/s12859-019-2897-z. Mi W, Zhang Y, Lyu J, Wang X, Tong Q, Peng D, Xue Y, Tencer AH, Wen H, Li W, et al. The ZZ-type zinc finger of ZZZ3 modulates the ATAC complex-mediated histone acetylation and gene activation. Nat Commun. 2018. https://doi.org/10.1038/s41467-018-06247-5. Li C, Bankhead A, Eisfeld AJ, Hatta Y, Jeng S, Chang JH, Aicher LD, Proll S, Ellis AL, Law GL, et al. Host regulatory network response to infection with highly pathogenic h5n1 avian influenza virus. J Virol. 2011;85(21):10955–67. Berge C. Hypergraphs: combinatorics of finite sets, vol. 45. Elsevier; 1984. Aksoy SG, Joslyn C, Marrero CO, Praggastis B, Purvine E. Hypernetwork science via high-order hypergraph walks. EPJ Data Sci. 2020;9(1):16. https://doi.org/10.1140/epjds/s13688-020-00231-0. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007;3(4):59. McDermott JE, Taylor RC, Yoon H, Heffron F. Bottlenecks and hubs in inferred networks are important for virulence in salmonella typhimurium. J Comput Biol. 2009;16(2):169–80.
Thông tin
Thông tin xuất bản

BMC Bioinformatics

Tập 22

1-21

Thông tin tác giả