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.

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BMC Bioinformatics

Tập 22

1-21

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