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Su G, Chen Y, Li X, Shao JW. Virus versus host: influenza A virus circumvents the immune responses. Front Microbiol 2024; 15:1394510. [PMID: 38817972 PMCID: PMC11137263 DOI: 10.3389/fmicb.2024.1394510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/23/2024] [Indexed: 06/01/2024] Open
Abstract
Influenza A virus (IAV) is a highly contagious pathogen causing dreadful losses to humans and animals around the globe. As is known, immune escape is a strategy that benefits the proliferation of IAVs by antagonizing, blocking, and suppressing immune surveillance. The HA protein binds to the sialic acid (SA) receptor to enter the cytoplasm and initiate viral infection. The conserved components of the viral genome produced during replication, known as the pathogen-associated molecular patterns (PAMPs), are thought to be critical factors for the activation of effective innate immunity by triggering dependent signaling pathways after recognition by pattern recognition receptors (PRRs), followed by a cascade of adaptive immunity. Viral infection-induced immune responses establish an antiviral state in the host to effectively inhibit virus replication and enhance viral clearance. However, IAV has evolved multiple mechanisms that allow it to synthesize and transport viral components by "playing games" with the host. At its heart, this review will describe how host and viral factors interact to facilitate the viral evasion of host immune responses.
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Affiliation(s)
- Guanming Su
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Veterinary Pharmaceutics Development and Safety Evaluation, Guangzhou, China
| | - Yiqun Chen
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Xiaowen Li
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Key Laboratory of Zoonosis Prevention and Control of Guangdong Province, Guangzhou, China
| | - Jian-Wei Shao
- School of Life Science and Engineering, Foshan University, Foshan, China
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2
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Zhang L, Zhang W, Xiao C, Wu X, Cui H, Yan P, Yang C, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Alfredsson L, Klareskog L, Yang Y, Yao Y, Li J, Liu Z, Yang C, Jiang X, Zhang B. Using human genetics to understand the epidemiological association between obesity, serum urate, and gout. Rheumatology (Oxford) 2023; 62:3280-3290. [PMID: 36734534 DOI: 10.1093/rheumatology/kead054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/31/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout. METHODS We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N = 806 834), waist-to-hip ratio (WHR; N = 697 734), WHR adjusted for BMI (WHRadjBMI; N = 694 649), serum urate (N = 288 649), and gout (Ncases = 13 179 and Ncontrols = 750 634). RESULTS Positive overall genetic correlations were observed for BMI (rg = 0.27, P = 6.62 × 10-7), WHR (rg = 0.22, P = 6.26 × 10-7) and WHRadjBMI (rg = 0.07, P = 6.08 × 10-3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene-tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI-gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR-gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance. CONCLUSION Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits.
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Affiliation(s)
- Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine and Center for Molecular Medicine, Karolinska Institutet at Karolinska University Hospital (Solna), Stockholm, Sweden
| | - Yanfang Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhenmi Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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Chan K, Farias AG, Lee H, Guvenc F, Mero P, Brown KR, Ward H, Billmann M, Aulakh K, Astori A, Haider S, Marcon E, Braunschweig U, Pu S, Habsid A, Yan Tong AH, Christie-Holmes N, Budylowski P, Ghalami A, Mubareka S, Maguire F, Banerjee A, Mossman KL, Greenblatt J, Gray-Owen SD, Raught B, Blencowe BJ, Taipale M, Myers C, Moffat J. Survival-based CRISPR genetic screens across a panel of permissive cell lines identify common and cell-specific SARS-CoV-2 host factors. Heliyon 2023; 9:e12744. [PMID: 36597481 PMCID: PMC9800021 DOI: 10.1016/j.heliyon.2022.e12744] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 12/15/2022] [Accepted: 12/22/2022] [Indexed: 12/31/2022] Open
Abstract
SARS-CoV-2 depends on host cell components for infection and replication. Identification of virus-host dependencies offers an effective way to elucidate mechanisms involved in viral infection and replication. If druggable, host factor dependencies may present an attractive strategy for anti-viral therapy. In this study, we performed genome wide CRISPR knockout screens in Vero E6 cells and four human cell lines including Calu-3, UM-UC-4, HEK-293 and HuH-7 to identify genetic regulators of SARS-CoV-2 infection. Our findings identified only ACE2, the cognate SARS-CoV-2 entry receptor, as a common host dependency factor across all cell lines, while other host genes identified were largely cell line specific, including known factors TMPRSS2 and CTSL. Several of the discovered host-dependency factors converged on pathways involved in cell signalling, immune-related pathways, and chromatin modification. Notably, the chromatin modifier gene KMT2C in Calu-3 cells had the strongest impact in preventing SARS-CoV-2 infection when perturbed.
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Affiliation(s)
- Katherine Chan
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Corresponding author
| | - Adrian Granda Farias
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Hunsang Lee
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Furkan Guvenc
- Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Patricia Mero
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Kevin R. Brown
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Henry Ward
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Kamaldeep Aulakh
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Audrey Astori
- Princess Margaret Cancer Center, Toronto, Ontario, Canada
| | - Shahan Haider
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Edyta Marcon
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Ulrich Braunschweig
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Shuye Pu
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Andrea Habsid
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Amy Hin Yan Tong
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
| | - Natasha Christie-Holmes
- Combined Containment Level 3 Unit, Temerty Faculty of Medicine, University of Toronto Toronto, Ontario, Canada, M5S3E1
| | - Patrick Budylowski
- Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Ayoob Ghalami
- Office of Environmental Health & Safety, University of Toronto, Toronto, Ontario, Canada
| | - Samira Mubareka
- Sunnybrook Research Institute, Toronto, Ontario, Canada, M5S3E1,Department of Laboratory Medicine and Pathobiology, University of Toronto, Ontario, Canada
| | - Finlay Maguire
- Department of Community Health and Epidemiology, Faculty of Medicine Dalhousie University, Halifax, Nova Scotia, Canada,Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Arinjay Banerjee
- Vaccine and Infectious Disease Organization, Department of Veterinary Microbiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Karen L. Mossman
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jack Greenblatt
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Scott D. Gray-Owen
- Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Brian Raught
- Princess Margaret Cancer Center, Toronto, Ontario, Canada
| | - Benjamin J. Blencowe
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Mikko Taipale
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8
| | - Chad Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Jason Moffat
- Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1,Department of Molecular Genetics, 1 King's College Circle, University of Toronto, Toronto, Ontario, Canada, M5S1A8,Institute for Biomedical Engineering, Rosebrugh Building, 164 College Street, Room 407, University of Toronto, Toronto, Ontario, Canada, M5S3G9,Corresponding author. Donnelly Center, 160 College Street, University of Toronto, Toronto, Ontario, Canada, M5S3E1
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4
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Rossin F, Ciccosanti F, D'Eletto M, Occhigrossi L, Fimia GM, Piacentini M. Type 2 transglutaminase in the nucleus: the new epigenetic face of a cytoplasmic enzyme. Cell Mol Life Sci 2023; 80:52. [PMID: 36695883 PMCID: PMC9874183 DOI: 10.1007/s00018-023-04698-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/26/2023]
Abstract
One of the major mysteries in science is how it is possible to pack the cellular chromatin with a total length of over 1 m, into a small sphere with a diameter of 5 mm "the nucleus", and even more difficult to envisage how to make it functional. Although we know that compaction is achieved through the histones, however, the DNA needs to be accessible to the transcription machinery and this is allowed thanks to a variety of very complex epigenetic mechanisms. Either DNA (methylation) or post-translational modifications of histone proteins (acetylation, methylation, ubiquitination and sumoylation) play a crucial role in chromatin remodelling and consequently on gene expression. Recently the serotonylation and dopaminylation of the histone 3, catalyzed by the Transglutaminase type 2 (TG2), has been reported. These novel post-translational modifications catalyzed by a predominantly cytoplasmic enzyme opens a new avenue for future investigations on the enzyme function itself and for the possibility that other biological amines, substrate of TG2, can influence the genome regulation under peculiar cellular conditions. In this review we analyzed the nuclear TG2's biology by discussing both its post-translational modification of various transcription factors and the implications of its epigenetic new face. Finally, we will focus on the potential impact of these events in human diseases.
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Affiliation(s)
- Federica Rossin
- Department of Biology, University of Rome 'Tor Vergata', Via Della Ricerca Scientifica 1, 00133, Rome, Italy
| | - Fabiola Ciccosanti
- Department of Epidemiology, Preclinical Research and Advanced Diagnostics, National Institute for Infectious Diseases IRCCS 'L. Spallanzani', Rome, Italy
| | - Manuela D'Eletto
- Department of Biology, University of Rome 'Tor Vergata', Via Della Ricerca Scientifica 1, 00133, Rome, Italy
| | - Luca Occhigrossi
- Department of Molecular Medicine, University of Rome "La Sapienza", Rome, Italy
| | - Gian Maria Fimia
- Department of Epidemiology, Preclinical Research and Advanced Diagnostics, National Institute for Infectious Diseases IRCCS 'L. Spallanzani', Rome, Italy
- Department of Molecular Medicine, University of Rome "La Sapienza", Rome, Italy
| | - Mauro Piacentini
- Department of Biology, University of Rome 'Tor Vergata', Via Della Ricerca Scientifica 1, 00133, Rome, Italy.
- Department of Epidemiology, Preclinical Research and Advanced Diagnostics, National Institute for Infectious Diseases IRCCS 'L. Spallanzani', Rome, Italy.
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5
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Su W, Lin XT, Zhao S, Zheng XQ, Zhou YQ, Xiao LL, Chen H, Zhang ZY, Zhang LJ, Wu XX. Tripartite motif-containing protein 46 accelerates influenza A H7N9 virus infection by promoting K48-linked ubiquitination of TBK1. Virol J 2022; 19:176. [PMID: 36329446 PMCID: PMC9632593 DOI: 10.1186/s12985-022-01907-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Avian influenza A H7N9 emerged in 2013, threatening public health and causing acute respiratory distress syndrome, and even death, in the human population. However, the underlying mechanism by which H7N9 virus causes human infection remains elusive. METHODS Herein, we infected A549 cells with H7N9 virus for different times and assessed tripartite motif-containing protein 46 (TRIM46) expression. To determine the role of TRIM46 in H7N9 infection, we applied lentivirus-based TRIM46 short hairpin RNA sequences and overexpression plasmids to explore virus replication, and changes in type I interferons and interferon regulatory factor 3 (IRF3) phosphorylation levels in response to silencing and overexpression of TRIM46. Finally, we used Co-immunoprecipitation and ubiquitination assays to examine the mechanism by which TRIM46 mediated the activity of TANK-binding kinase 1 (TBK1). RESULTS Type I interferons play an important role in defending virus infection. Here, we found that TRIM46 levels were significantly increased during H7N9 virus infection. Furthermore, TRIM46 knockdown inhibited H7N9 virus replication compared to that in the control group, while the production of type I interferons increased. Meanwhile, overexpression of TRIM46 promoted H7N9 virus replication and decrease the production of type I interferons. In addition, the level of phosphorylated IRF3, an important interferon regulatory factor, was increased in TRIM46-silenced cells, but decreased in TRIM46 overexpressing cells. Mechanistically, we observed that TRIM46 could interact with TBK1 to induce its K48-linked ubiquitination, which promoted H7N9 virus infection. CONCLUSION Our results suggest that TRIM46 negatively regulates the human innate immune response against H7N9 virus infection.
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Affiliation(s)
- Wei Su
- Department of Intensive Care Unit, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China.
| | - Xian-Tian Lin
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Shuai Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Xiao-Qin Zheng
- Department of Lung Transplant, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China
| | - Yu-Qing Zhou
- Department of Respiratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, Zhejiang, China
| | - Lan-Lan Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Hui Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Zheng-Yu Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Li-Jun Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China
| | - Xiao-Xin Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, 310003, Zhejiang, China.
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6
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Host factors involved in influenza virus infection. Emerg Top Life Sci 2020; 4:389-398. [PMID: 33210707 DOI: 10.1042/etls20200232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/14/2020] [Accepted: 10/30/2020] [Indexed: 12/17/2022]
Abstract
Influenza virus causes an acute febrile respiratory disease in humans that is commonly known as 'flu'. Influenza virus has been around for centuries and is one of the most successful, and consequently most studied human viruses. This has generated tremendous amount of data and information, thus it is pertinent to summarise these for, particularly interdisciplinary readers. Viruses are acellular organisms and exist at the interface of living and non-living. Due to this unique characteristic, viruses require another organism, i.e. host to survive. Viruses multiply inside the host cell and are obligate intracellular pathogens, because their relationship with the host is almost always harmful to host. In mammalian cells, the life cycle of a virus, including influenza is divided into five main steps: attachment, entry, synthesis, assembly and release. To complete these steps, some viruses, e.g. influenza utilise all three parts - plasma membrane, cytoplasm and nucleus, of the cell; whereas others, e.g. SARS-CoV-2 utilise only plasma membrane and cytoplasm. Hence, viruses interact with numerous host factors to complete their life cycle, and these interactions are either exploitative or antagonistic in nature. The host factors involved in the life cycle of a virus could be divided in two broad categories - proviral and antiviral. This perspective has endeavoured to assimilate the information about the host factors which promote and suppress influenza virus infection. Furthermore, an insight into host factors that play a dual role during infection or contribute to influenza virus-host adaptation and disease severity has also been provided.
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7
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Guo S, Bao L, Li C, Sun J, Zhao R, Cui X. Antiviral activity of iridoid glycosides extracted from Fructus Gardeniae against influenza A virus by PACT-dependent suppression of viral RNA replication. Sci Rep 2020; 10:1897. [PMID: 32024921 PMCID: PMC7002373 DOI: 10.1038/s41598-020-58443-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 01/15/2020] [Indexed: 11/08/2022] Open
Abstract
Epidemic and pandemic influenza A virus (IAV) poses a significant threat to human populations worldwide. Iridoid glycosides are principal bioactive components from the Gardenia jasminoides J. Ellis fruit that exhibit antiviral activity against several strains of IAV. In the present study, we evaluated the protective effect of Fructus Gardeniae iridoid glycoside extracts (IGEs) against IAV by cytopathogenic effect(CPE), MTT and a plaque formation assay in vitro and examined the reduction in the pulmonary index (PI), restoration of body weight, reduction in mortality and increases in survival time in vivo. As a host factor, PACT provides protection against the pathogenic influenza A virus by interacting with IAV polymerase and activating the IFN-I response. To verify the whether IGEs suppress IAV replication in a PACT-dependent manner, IAV RNA replication, expression of PACT and the phosphorylation of eIF2α in A549 cells were detected; the levels of IFNβ, PACT and PKR in mouse lung tissues were determined; and the activity of IAV polymerase was evaluated in PACT-compromised cells. The results indicated that IGEs sufficiently alleviated cell damage and suppressed IAV replication in vitro, protecting mice from IAV-induced injury and lethal IAV infection. These anti-IAV effects might be related to disrupted interplay between IVA polymerase and PACT and/or prevention of a PACT-dependent overactivated IFN-I antiviral response. Taken together, our findings reveal a new facet of the mechanisms by which IGEs fight the influenza A virus in a PACT-dependent manner.
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Affiliation(s)
- Shanshan Guo
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China
| | - Lei Bao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China
| | - Chun Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China
| | - Jing Sun
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China
| | - Ronghua Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China
| | - Xiaolan Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, No.4 Yinghua East Road, Chaoyang District, Beijing, 100029, China.
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8
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Rogers LRK, de Los Campos G, Mias GI. Microarray Gene Expression Dataset Re-analysis Reveals Variability in Influenza Infection and Vaccination. Front Immunol 2019; 10:2616. [PMID: 31787983 PMCID: PMC6854009 DOI: 10.3389/fimmu.2019.02616] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/21/2019] [Indexed: 12/18/2022] Open
Abstract
Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.
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Affiliation(s)
- Lavida R K Rogers
- Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, United States.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Gustavo de Los Campos
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States.,Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States
| | - George I Mias
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States.,Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
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9
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Chan CP, Yuen CK, Cheung PHH, Fung SY, Lui PY, Chen H, Kok KH, Jin DY. Antiviral activity of double-stranded RNA-binding protein PACT against influenza A virus mediated via suppression of viral RNA polymerase. FASEB J 2018. [PMID: 29513570 DOI: 10.1096/fj.201701361r] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PACT is a double-stranded RNA-binding protein that has been implicated in host-influenza A virus (IAV) interaction. PACT facilitates the action of RIG-I in the activation of the type I IFN response, which is suppressed by the viral nonstructural protein NS1. PACT is also known to interact with the IAV RNA polymerase subunit PA. Exactly how PACT exerts its antiviral activity during IAV infection remains to be elucidated. In the current study, we demonstrated the interplay between PACT and IAV polymerase. Induction of IFN-β by the IAV RNP complex was most robust when both RIG-I and PACT were expressed. PACT-dependent activation of IFN-β production was suppressed by the IAV polymerase subunits, polymerase acidic protein, polymerase basic protein 1 (PB1), and PB2. PACT associated with PA, PB1, and PB2. Compromising PACT in IAV-infected A549 cells resulted in the augmentation of viral RNA (vRNA) transcription and replication and IFN-β production. Furthermore, vRNA replication was boosted by knockdown of PACT in both A549 cells and IFN-deficient Vero cells. Thus, the antiviral activity of PACT is mediated primarily via its interaction with and inhibition of IAV polymerase. Taken together, our findings reveal a new facet of the host-IAV interaction in which the interplay between PACT and IAV polymerase affects the outcome of viral infection and antiviral response.-Chan, C.-P., Yuen, C.-K., Cheung, P.-H. H., Fung, S.-Y., Lui, P.-Y., Chen, H., Kok, K.-H., Jin, D.-Y. Antiviral activity of double-stranded RNA-binding protein PACT against influenza A virus mediated via suppression of viral RNA polymerase.
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Affiliation(s)
- Chi-Ping Chan
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Chun-Kit Yuen
- Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong
| | | | - Sin-Yee Fung
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Pak-Yin Lui
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
| | - Honglin Chen
- Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kin-Hang Kok
- Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Dong-Yan Jin
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong
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10
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Zheng J, Perlman S. Immune responses in influenza A virus and human coronavirus infections: an ongoing battle between the virus and host. Curr Opin Virol 2018; 28:43-52. [PMID: 29172107 PMCID: PMC5835172 DOI: 10.1016/j.coviro.2017.11.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 11/02/2017] [Indexed: 12/25/2022]
Abstract
Respiratory viruses, especially influenza A viruses and coronaviruses such as MERS-CoV, represent continuing global threats to human health. Despite significant advances, much needs to be learned. Recent studies in virology and immunology have improved our understanding of the role of the immune system in protection and in the pathogenesis of these infections and of co-evolution of viruses and their hosts. These findings, together with sophisticated molecular structure analyses, omics tools and computer-based models, have helped delineate the interaction between respiratory viruses and the host immune system, which will facilitate the development of novel treatment strategies and vaccines with enhanced efficacy.
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Affiliation(s)
- Jian Zheng
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, IA 52242, United States
| | - Stanley Perlman
- Department of Microbiology and Immunology, The University of Iowa, Iowa City, IA 52242, United States.
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