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Yu H, Li L, Huffman A, Beverley J, Hur J, Merrell E, Huang HH, Wang Y, Liu Y, Ong E, Cheng L, Zeng T, Zhang J, Li P, Liu Z, Wang Z, Zhang X, Ye X, Handelman SK, Sexton J, Eaton K, Higgins G, Omenn GS, Athey B, Smith B, Chen L, He Y. A new framework for host-pathogen interaction research. Front Immunol 2022; 13:1066733. [PMID: 36591248 PMCID: PMC9797517 DOI: 10.3389/fimmu.2022.1066733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
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Affiliation(s)
- Hong Yu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Li Li
- Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
- Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, United States
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Hsin-hui Huang
- University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yang Wang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Liang Cheng
- Department of Bioinformatics, Harbin Medical University, Harbin, Helongjian, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingsong Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pengpai Li
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhiping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhigang Wang
- Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangyan Zhang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Xianwei Ye
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | | | - Jonathan Sexton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathryn Eaton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gerry Higgins
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gilbert S. Omenn
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, United States
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Huffman A, Ong E, Hur J, D’Mello A, Tettelin H, He Y. COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. Brief Bioinform 2022; 23:bbac190. [PMID: 35649389 PMCID: PMC9294427 DOI: 10.1093/bib/bbac190] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 12/11/2022] Open
Abstract
Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
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Affiliation(s)
- Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota 58202, USA
| | - Adonis D’Mello
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Hervé Tettelin
- Department of Microbiology and Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
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Berke K, Sun P, Ong E, Sanati N, Huffman A, Brunson T, Loney F, Ostrow J, Racz R, Zhao B, Xiang Z, Masci AM, Zheng J, Wu G, He Y. VaximmutorDB: A Web-Based Vaccine Immune Factor Database and Its Application for Understanding Vaccine-Induced Immune Mechanisms. Front Immunol 2021; 12:639491. [PMID: 33777032 PMCID: PMC7994782 DOI: 10.3389/fimmu.2021.639491] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/18/2021] [Indexed: 01/07/2023] Open
Abstract
Vaccines stimulate various immune factors critical to protective immune responses. However, a comprehensive picture of vaccine-induced immune factors and pathways have not been systematically collected and analyzed. To address this issue, we developed VaximmutorDB, a web-based database system of vaccine immune factors (abbreviated as “vaximmutors”) manually curated from peer-reviewed articles. VaximmutorDB currently stores 1,740 vaccine immune factors from 13 host species (e.g., human, mouse, and pig). These vaximmutors were induced by 154 vaccines for 46 pathogens. Top 10 vaximmutors include three antibodies (IgG, IgG2a and IgG1), Th1 immune factors (IFN-γ and IL-2), Th2 immune factors (IL-4 and IL-6), TNF-α, CASP-1, and TLR8. Many enriched host processes (e.g., stimulatory C-type lectin receptor signaling pathway, SRP-dependent cotranslational protein targeting to membrane) and cellular components (e.g., extracellular exosome, nucleoplasm) by all the vaximmutors were identified. Using influenza as a model, live attenuated and killed inactivated influenza vaccines stimulate many shared pathways such as signaling of many interleukins (including IL-1, IL-4, IL-6, IL-13, IL-20, and IL-27), interferon signaling, MARK1 activation, and neutrophil degranulation. However, they also present their unique response patterns. While live attenuated influenza vaccine FluMist induced significant signal transduction responses, killed inactivated influenza vaccine Fluarix induced significant metabolism of protein responses. Two different Yellow Fever vaccine (YF-Vax) studies resulted in overlapping gene lists; however, they shared more portions of pathways than gene lists. Interestingly, live attenuated YF-Vax simulates significant metabolism of protein responses, which was similar to the pattern induced by killed inactivated Fluarix. A user-friendly web interface was generated to access, browse and search the VaximmutorDB database information. As the first web-based database of vaccine immune factors, VaximmutorDB provides systematical collection, standardization, storage, and analysis of experimentally verified vaccine immune factors, supporting better understanding of protective vaccine immunity.
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Affiliation(s)
- Kimberly Berke
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States.,Central Michigan College of Medicine, Mt. Pleasant, MI, United States
| | - Peter Sun
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Edison Ong
- Department of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, United States
| | - Nasim Sanati
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Anthony Huffman
- Department of Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, United States
| | - Timothy Brunson
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Fred Loney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Joseph Ostrow
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Rebecca Racz
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Bin Zhao
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Zuoshuang Xiang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, United States
| | - Anna Maria Masci
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
| | - Jie Zheng
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, United States
| | - Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.,Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States.,Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, United States
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Ong E, Sun P, Berke K, Zheng J, Wu G, He Y. VIO: ontology classification and study of vaccine responses given various experimental and analytical conditions. BMC Bioinformatics 2019; 20:704. [PMID: 31865910 PMCID: PMC6927110 DOI: 10.1186/s12859-019-3194-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Background Different human responses to the same vaccine were frequently observed. For example, independent studies identified overlapping but different transcriptomic gene expression profiles in Yellow Fever vaccine 17D (YF-17D) immunized human subjects. Different experimental and analysis conditions were likely contributed to the observed differences. To investigate this issue, we developed a Vaccine Investigation Ontology (VIO), and applied VIO to classify the different variables and relations among these variables systematically. We then evaluated whether the ontological VIO modeling and VIO-based statistical analysis would contribute to the enhanced vaccine investigation studies and a better understanding of vaccine response mechanisms. Results Our VIO modeling identified many variables related to data processing and analysis such as normalization method, cut-off criteria, software settings including software version. The datasets from two previous studies on human responses to YF-17D vaccine, reported by Gaucher et al. (2008) and Querec et al. (2009), were re-analyzed. We first applied the same LIMMA statistical method to re-analyze the Gaucher data set and identified a big difference in terms of significantly differentiated gene lists compared to the original study. The different results were likely due to the LIMMA version and software package differences. Our second study re-analyzed both Gaucher and Querec data sets but with the same data processing and analysis pipeline. Significant differences in differential gene lists were also identified. In both studies, we found that Gene Ontology (GO) enrichment results had more overlapping than the gene lists and enriched pathway lists. The visualization of the identified GO hierarchical structures among the enriched GO terms and their associated ancestor terms using GOfox allowed us to find more associations among enriched but often different GO terms, demonstrating the usage of GO hierarchical relations enhance data analysis. Conclusions The ontology-based analysis framework supports standardized representation, integration, and analysis of heterogeneous data of host responses to vaccines. Our study also showed that differences in specific variables might explain different results drawn from similar studies.
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Affiliation(s)
- Edison Ong
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Peter Sun
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
| | - Kimberly Berke
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA.,Central Michigan University College of Medicine, Mount Pleasant, MI, USA
| | - Jie Zheng
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Guanming Wu
- Oregon Health & Science University, Portland, OR, USA
| | - Yongqun He
- Unit of Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA. .,Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA. .,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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