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

BMC Bioinformatics

Kelly G. Stratton¹  Lisa M. Bramer¹  Hugh D. Mitchell¹  Song Feng¹  Katrina M. Waters²  Jason E. McDermott³  Natalie C. Heller⁴  Emilie Purvine⁴  Brenda Praggastis⁴  Henry Kvinge⁴  Brett Jefferson⁴  Cliff Joslyn⁵  Ralph S. Baric⁶  Jacob F. Kocher⁶  Timothy P. Sheahan⁶  Vineet D. Menachery⁷  Emily Heath⁸  Larissa B. Thackray⁹  Qing Tan⁹  Michael S. Diamond^1,10  Amie J. Eisfeld^1,11  Danielle Westhoff-Smith^1,11  Shufang Fan^1,11  Peter J. Halfmann^1,11  Kevin B. Walters^1,11  Yoshihiro Kawaoka^1,12  Adam S. Cockrell^1,13  Amy C. Sims^1,14 

[1]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

[2]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

[3]Department of Comparative Medicine, University of Washington, Seattle, WA, USA

[4]Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

[5]Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA

[6]Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA

[7]Computing and Analytics Division, Pacific Northwest National Laboratory, Seattle, WA, USA

[8]Systems Science Program, Portland State University, Portland, OR, USA

[9]Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[10]Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

[11]Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA

[12]Department of Mathematics, University of Illinois, Urbana-Champaign, IL, USA

[13]Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA

[14]Department of Medicine, Washington University School of Medicine, 63110, Saint Louis, MO, USA

[15]Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

[16]Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA

[17]Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA

[18]Department of Pathobiological Sciences, School of Veterinary Medicine, Influenza Research Institute, University of Wisconsin-Madison, 575 Science Drive, 53711, Madison, WI, USA

[19]Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, 108-8639, Tokyo, Japan

[20]ERATO Infection-Induced Host Responses Project, 332-0012, Saitama, Japan

[21]Department of Special Pathogens, International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, 108-8639, Tokyo, Japan

[22]KNOWBIO LLC., 27703, Durham, NC, USA

[23]Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, WA, USA

关键词: Systems biology;    Hypergraph;    Viral infection;    Biological networks;    SARS;    MERS;    Influenza;    West Nile Virus;    Host response;    Viral pathogenesis;   

DOI  :  10.1186/s12859-021-04197-2

来源: Springer

【 摘 要 】

BackgroundRepresenting 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.ResultsWe 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.ConclusionsOur 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.

【 授权许可】

CC BY