Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets Peter B. McGarvey1*, Hongzhan Huang 1 , Raja Mazumder1, Jian Zhang 1 , Yongxing Chen1, Chengdong Zhang2, Stephen Cammer2, Rebecca Will2, Margie Odle3, Bruno Sobral2, Margaret Moore3, Cathy H. Wu1,4* 1 Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D.C., United States of America, 2 Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America, 3 Social & Scientific Systems, Inc., Silver Spring, Maryland, United States of America, 4 Departments of Computer & Information Sciences and Biological Sciences, Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, United States of America Abstract The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification. Citation: McGarvey PB, Huang H, Mazumder R, Zhang J, Chen Y, et al. (2009) Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets. PLoS ONE 4(9): e7162. doi:10.1371/journal.pone.0007162 Editor: Jo¨ rg Hoheisel, Deutsches Krebsforschungszentrum, Germany Received March 16, 2009; Accepted August 14, 2009; Published September 25, 2009 Copyright: 2009 McGarvey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Supported by National Institutes of Allergy and Infectious Disease NIH Contract: HHSN266200400061C The funders had no role in study design, analysis, decision to publish, or preparation of the manuscript. The funders, NIAID, did play some role in data collection as they had contracts with the Proteomics Research Centers that provided the data. The contracts provided guidelines on data collection and submission to the Resource Center and require release to the public. Social and Scientific Systems is an IT contractor and has no interest, finacial or otherwise, in the data, software or conclusions in this manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: pbm9@georgetown.edu (PBM); wuc@georgetown.edu (CHW) Introduction The NIAID (National Institute of Allergy and Infectious Diseases) Biodefense Proteomics program, established in 2004, aims to characterize the pathogen and host cell proteome by identifying proteins associated with the biology of microbes, mechanisms of microbial pathogenesis and host responses to infection, thereby facilitating the discovery of target genes or proteins as potential candidates for the next generation of vaccines, therapeutics, and diagnostics [1]. The program includes seven Proteomics Research Centers (PRCs) conducting state-of-the-art high-throughput re- search on pathogens of concern in biodefense and emerging/ reemerging infectious diseases, as well as a Biodefense Resource Center for public dissemination of the pathogen and host data, biological reagents, protocols, and other project deliverables. The PRCs work on many different organisms, covering bacterial pathogens (Bacillus anthracis, Brucella abortus, Francisella tularensis, Salmonella typhi, S. typhimurium, Vibrio cholerae, Yersinia pestis), Eukaryotic parasites (Cryptosporidium parvum, Toxoplasma gondii), and viral pathogens (Monkeypox, SARS-CoV, Vaccinia). The centers have generated a heterogeneous set of experimental data using various technologies loosely defined as proteomic, but encompassing genomic, structural, immunology and protein interaction technologies, as well as more standard cell and molecular biology techniques used to validate PLoS ONE | www.plosone.org 1 September 2009 | Volume 4 | Issue 9 | e7162