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Coombs KM, Simon PF, McLeish NJ, Zahedi-Amiri A, Kobasa D. Aptamer Profiling of A549 Cells Infected with Low-Pathogenicity and High-Pathogenicity Influenza Viruses. Viruses 2019; 11:v11111028. [PMID: 31694171 PMCID: PMC6893437 DOI: 10.3390/v11111028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/30/2019] [Accepted: 11/04/2019] [Indexed: 12/17/2022] Open
Abstract
Influenza A viruses (IAVs) are important animal and human emerging and re-emerging pathogens that are responsible for yearly seasonal epidemics and sporadic pandemics. IAVs cause a wide range of clinical illnesses, from relatively mild infections by seasonal strains, to acute respiratory distress during infections with highly pathogenic avian IAVs (HPAI). For this study, we infected A549 human lung cells with lab prototype A/PR/8/34 (H1N1) (PR8), a seasonal H1N1 (RV733), the 2009 pandemic H1N1 (pdm09), or with two avian strains, an H5N1 HPAI strain or an H7N9 strain that has low pathogenicity in birds but high pathogenicity in humans. We used a newly-developed aptamer-based multiplexed technique (SOMAscan®) to examine >1300 human lung cell proteins affected by the different IAV strains, and identified more than 500 significantly dysregulated cellular proteins. Our analyses indicated that the avian strains induced more profound changes in the A549 global proteome compared to all tested low-pathogenicity H1N1 strains. The PR8 strain induced a general activation, primarily by upregulating many immune molecules, the seasonal RV733 and pdm09 strains had minimal effect upon assayed molecules, and the avian strains induced significant downregulation, primarily in antimicrobial response, cardiovascular and post-translational modification systems.
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Affiliation(s)
- Kevin M. Coombs
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada (A.Z.-A.); (D.K.)
- Manitoba Centre for Proteomics & Systems Biology, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Children’s Hospital Research Institute of Manitoba, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
- Correspondence: ; Tel.: +1-204-7893-976
| | - Philippe F. Simon
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada (A.Z.-A.); (D.K.)
- Special Pathogen Program, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada
| | - Nigel J. McLeish
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada (A.Z.-A.); (D.K.)
| | - Ali Zahedi-Amiri
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada (A.Z.-A.); (D.K.)
- Manitoba Centre for Proteomics & Systems Biology, University of Manitoba, Winnipeg, MB R3E 3P4, Canada
| | - Darwyn Kobasa
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada (A.Z.-A.); (D.K.)
- Special Pathogen Program, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada
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Liu R, Chen P, Chen L. Single-sample landscape entropy reveals the imminent phase transition during disease progression. Bioinformatics 2019; 36:1522-1532. [DOI: 10.1093/bioinformatics/btz758] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 07/05/2019] [Accepted: 10/05/2019] [Indexed: 12/13/2022] Open
Abstract
Abstract
Motivation
The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt, that is, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data, in particular when only a single sample is available.
Results
In this study, we developed a novel approach, i.e. single-sample landscape entropy (SLE) method, to identify the tipping point during disease progression with only one sample data. Specifically, by evaluating the disorder of a network projected from a single-sample data, SLE effectively characterizes the criticality of this single sample network in terms of network entropy, thereby capturing not only the signals of the impending transition but also its leading network, i.e. dynamic network biomarkers. Using this method, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects, including influenza virus infection, lung cancer metastasis, prostate cancer and acute lung injury.
Availability and implementation
https://github.com/rabbitpei/SLE.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Pei Chen
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
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