1
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He J, Liu Z, Cao Y, Zhang X, Yi C, Zhou Y, Yang C, Guo Z, Zheng Q, Huang J. Single-cell landscape of peripheral immune response in patients with anti-melanoma differentiation-associated gene 5 dermatomyositis. Rheumatology (Oxford) 2024; 63:2284-2294. [PMID: 37941459 DOI: 10.1093/rheumatology/kead597] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVE Anti-melanoma differentiation-associated gene 5 (Anti-MDA5)-positive DM is a rare but life-threatening autoimmune disorder that is associated with a high risk of developing rapidly progressive interstitial lung disease. Current empirical therapies offer limited benefit in terms of patient survival, as little is known about the aetiology of anti-MDA5 DM. To best understand its immune landscape, we applied single-cell RNA sequencing (scRNA-seq) to peripheral blood samples from DM patients and healthy controls. METHODS Peripheral blood mononuclear cells (PBMCs) from eight DM patients (including three distinct subtypes of DM) and two healthy donors were sequenced using the 10X Genomics platform. Additional scRNA-seq data for four healthy donors were incorporated for further bioinformatic analysis. RESULTS Aberrantly increased proportions of CD14+ monocytes and plasma cells were observed in anti-MDA5 DM PBMC samples. Moreover, we found an overactivated type I IFN response and antiviral immunity in both innate and adaptive immune cells derived from anti-MDA5 DM patients that was positively correlated with disease severity. Importantly, a unique subset of CD14+ monocytes that highly expressed IFN alpha-inducible protein 27 (IFI27), a biomarker for viral infection, and IFN induced with helicase C domain 1 (IFIH1, which encodes MDA5) was specifically identified in anti-MDA5 DM samples for the first time. CONCLUSION Our study has illustrated the peripheral immune cell atlas of a number of DM subtypes, has provided compelling evidence for a viral infection-derived origin for anti-MDA5 DM, and has indicated potential targets for innovative therapeutic interventions.
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Affiliation(s)
- Jiangping He
- Department of Rheumatology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China, Hangzhou, China
| | - Zhicheng Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Cao
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaofang Zhang
- Department of Rheumatology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China, Hangzhou, China
| | - Caihong Yi
- Department of Rheumatology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China, Hangzhou, China
| | - Yanzi Zhou
- Department of Rheumatology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China, Hangzhou, China
| | - Chen Yang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenyang Guo
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Quan Zheng
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiao Huang
- Department of Rheumatology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China, Hangzhou, China
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2
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Li Y, Tao X, Ye S, Tai Q, You YA, Huang X, Liang M, Wang K, Wen H, You C, Zhang Y, Zhou X. A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses. Viruses 2024; 16:1029. [PMID: 39066192 PMCID: PMC11281602 DOI: 10.3390/v16071029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/06/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.
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Affiliation(s)
- Yang Li
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;
- Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China; (X.T.); (X.H.)
| | - Xinya Tao
- Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China; (X.T.); (X.H.)
| | - Sheng Ye
- Chongqing Center for Disease Control and Prevention, Chongqing 400707, China;
| | - Qianchen Tai
- Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100091, China;
| | - Yu-Ang You
- Institute of Pharmaceutical Science, King’s College London, London WC2R 2LS, UK;
| | - Xinting Huang
- Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China; (X.T.); (X.H.)
| | - Mifang Liang
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
| | - Kai Wang
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China;
| | - Haiyan Wen
- Chongqing International Travel Health Care Center, Chongqing 401120, China;
| | - Chong You
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;
- Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China; (X.T.); (X.H.)
- Shanghai Institute for Mathematics and Interdisciplinary Sciences, Fudan University, Shanghai 200433, China
| | - Yan Zhang
- Sports & Medicine Integration Research Center (SMIRC), Capital University of Physical Education and Sports, Beijing 100088, China
| | - Xiaohua Zhou
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;
- Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China; (X.T.); (X.H.)
- Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100091, China;
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3
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Schlapbach LJ, Ganesamoorthy D, Wilson C, Raman S, George S, Snelling PJ, Phillips N, Irwin A, Sharp N, Le Marsney R, Chavan A, Hempenstall A, Bialasiewicz S, MacDonald AD, Grimwood K, Kling JC, McPherson SJ, Blumenthal A, Kaforou M, Levin M, Herberg JA, Gibbons KS, Coin LJM. Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study. THE LANCET. CHILD & ADOLESCENT HEALTH 2024; 8:325-338. [PMID: 38513681 DOI: 10.1016/s2352-4642(24)00017-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection. METHODS This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI. FINDINGS Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection. INTERPRETATION In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction. FUNDING Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.
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Affiliation(s)
- Luregn J Schlapbach
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Department of Intensive Care and Neonatology, and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia.
| | - Devika Ganesamoorthy
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Clare Wilson
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Sainath Raman
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Shane George
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Department of Emergency Medicine, Gold Coast University Hospital, Southport, QLD, Australia; School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia
| | - Peter J Snelling
- Department of Emergency Medicine, Gold Coast University Hospital, Southport, QLD, Australia; School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia
| | - Natalie Phillips
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Emergency Department, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Adam Irwin
- Faculty of Medicine, UQ Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia; Infection Management and Prevention Services, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Natalie Sharp
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia; Paediatric Intensive Care Unit, Queensland Children's Hospital, Children's Health Queensland, Brisbane, QLD, Australia
| | - Renate Le Marsney
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Arjun Chavan
- Paediatric Intensive Care Unit, Townsville University Hospital, Townsville, QLD, Australia
| | | | - Seweryn Bialasiewicz
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, and Queensland Paediatric Infectious Diseases Laboratory, The University of Queensland, Brisbane, QLD, Australia
| | - Anna D MacDonald
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Keith Grimwood
- School of Medicine and Dentistry and the Menzies Health Institute Queensland, Griffith University, Southport, QLD, Australia; Department of Infectious Disease and Paediatrics, Gold Coast Health, Southport, QLD, Australia
| | - Jessica C Kling
- Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | | | - Antje Blumenthal
- Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Myrsini Kaforou
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Michael Levin
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Jethro A Herberg
- Section of Paediatric Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Kristen S Gibbons
- Children's Intensive Care Research Program, Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Lachlan J M Coin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
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4
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Zhang Z. The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications. BIOLOGY 2024; 13:245. [PMID: 38666857 PMCID: PMC11048280 DOI: 10.3390/biology13040245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87-100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.
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Affiliation(s)
- Zhengjun Zhang
- School of Computer, Data and Information Sciences, University of Wisconsin, Madison, WI 53706, USA
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5
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Mehta P, Swaminathan A, Yadav A, Chattopadhyay P, Shamim U, Pandey R. Integrative genomics important to understand host-pathogen interactions. Brief Funct Genomics 2024; 23:1-14. [PMID: 35909219 DOI: 10.1093/bfgp/elac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2024] Open
Abstract
Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.
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6
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Zhai Z, Lin Z, Meng X, Zheng X, Du Y, Li Z, Zhang X, Liu C, Zhou L, Zhang X, Tian Z, Ma Q, Li J, Li Q, Pan J. DiSignAtlas: an atlas of human and mouse disease signatures based on bulk and single-cell transcriptomics. Nucleic Acids Res 2024; 52:D1236-D1245. [PMID: 37930831 PMCID: PMC10767933 DOI: 10.1093/nar/gkad961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 11/08/2023] Open
Abstract
Molecular signatures are usually sets of biomolecules that can serve as diagnostic, prognostic, predictive, or therapeutic markers for a specific disease. Omics data derived from various high-throughput molecular biology technologies offer global, unbiased and appropriately comparable data, which can be used to identify such molecular signatures. To address the need for comprehensive disease signatures, DiSignAtlas (http://www.inbirg.com/disignatlas/) was developed to provide transcriptomics-based signatures for a wide range of diseases. A total of 181 434 transcriptome profiles were manually curated from studies involving 1836 nonredundant disease types in humans and mice. Then, 10 306 comparison datasets comprising both disease and control samples, including 328 single-cell RNA sequencing datasets, were established. Furthermore, a total of 3 775 317 differentially expressed genes in humans and 1 723 674 in mice were identified as disease signatures by analysing transcriptome profiles using commonly used pipelines. In addition to providing multiple methods for the retrieval of disease signatures, DiSignAtlas provides downstream functional enrichment analysis, cell type analysis and signature correlation analysis between diseases or species when available. Moreover, multiple analytical and comparison tools for disease signatures are available. DiSignAtlas is expected to become a valuable resource for both bioscientists and bioinformaticians engaged in translational research.
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Affiliation(s)
- Zhaoyu Zhai
- Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Zhewei Lin
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xuehang Meng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xiao Zheng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Yujia Du
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Zhi Li
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xuelu Zhang
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Chang Liu
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Lu Zhou
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xu Zhang
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Zhihao Tian
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qinfeng Ma
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jinhao Li
- Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Qiang Li
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Pan
- Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
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7
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Wimmers F, Burrell AR, Feng Y, Zheng H, Arunachalam PS, Hu M, Spranger S, Nyhoff LE, Joshi D, Trisal M, Awasthi M, Bellusci L, Ashraf U, Kowli S, Konvinse KC, Yang E, Blanco M, Pellegrini K, Tharp G, Hagan T, Chinthrajah RS, Nguyen TT, Grifoni A, Sette A, Nadeau KC, Haslam DB, Bosinger SE, Wrammert J, Maecker HT, Utz PJ, Wang TT, Khurana S, Khatri P, Staat MA, Pulendran B. Multi-omics analysis of mucosal and systemic immunity to SARS-CoV-2 after birth. Cell 2023; 186:4632-4651.e23. [PMID: 37776858 PMCID: PMC10724861 DOI: 10.1016/j.cell.2023.08.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 06/18/2023] [Accepted: 08/31/2023] [Indexed: 10/02/2023]
Abstract
The dynamics of immunity to infection in infants remain obscure. Here, we used a multi-omics approach to perform a longitudinal analysis of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in infants and young children by analyzing blood samples and weekly nasal swabs collected before, during, and after infection with Omicron and non-Omicron variants. Infection stimulated robust antibody titers that, unlike in adults, showed no sign of decay for up to 300 days. Infants mounted a robust mucosal immune response characterized by inflammatory cytokines, interferon (IFN) α, and T helper (Th) 17 and neutrophil markers (interleukin [IL]-17, IL-8, and CXCL1). The immune response in blood was characterized by upregulation of activation markers on innate cells, no inflammatory cytokines, but several chemokines and IFNα. The latter correlated with viral load and expression of interferon-stimulated genes (ISGs) in myeloid cells measured by single-cell multi-omics. Together, these data provide a snapshot of immunity to infection during the initial weeks and months of life.
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Affiliation(s)
- Florian Wimmers
- Department of Molecular Medicine, Interfaculty Institute for Biochemistry, University of Tuebingen, 72076 Tuebingen, Baden-Wuerttemberg, Germany; DFG Cluster of Excellence 2180 "Image-guided and Functional Instructed Tumor Therapy" (iFIT), University of Tuebingen, 72076 Tuebingen, Baden-Wuerttemberg, Germany; German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Baden-Wuerttemberg, Germany
| | - Allison R Burrell
- Department of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Environmental and Public Health Sciences, Division of Epidemiology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Yupeng Feng
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Hong Zheng
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Prabhu S Arunachalam
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Mengyun Hu
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Sara Spranger
- Department of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lindsay E Nyhoff
- Department of Pediatrics, Division of Infectious Disease, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Devyani Joshi
- Department of Pediatrics, Division of Infectious Disease, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Meera Trisal
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Mayanka Awasthi
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Lorenza Bellusci
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Usama Ashraf
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA 94305, USA
| | - Sangeeta Kowli
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Katherine C Konvinse
- Department of Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Emily Yang
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael Blanco
- Stanford Genomics Service Center, Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Gregory Tharp
- Yerkes National Primate Research Center, Atlanta, GA 30024, USA
| | - Thomas Hagan
- Department of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - R Sharon Chinthrajah
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford, CA 94305, USA
| | - Tran T Nguyen
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Alba Grifoni
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA 92037, USA
| | - Kari C Nadeau
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford, CA 94305, USA
| | - David B Haslam
- Department of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Steven E Bosinger
- Yerkes National Primate Research Center, Atlanta, GA 30024, USA; Department of Pathology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jens Wrammert
- Department of Pediatrics, Division of Infectious Disease, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Holden T Maecker
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Paul J Utz
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Taia T Wang
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Surender Khurana
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Mary A Staat
- Department of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Bali Pulendran
- Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA.
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8
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Milicevic O, Loncar A, Abazovic D, Vukcevic M, Despot D, Djukic T, Djukic V, Milovanovic A, Panic N, Plecic N, Banko A. Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization. Int J Mol Sci 2023; 24:13125. [PMID: 37685932 PMCID: PMC10487753 DOI: 10.3390/ijms241713125] [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: 07/27/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.
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Affiliation(s)
- Ognjen Milicevic
- Institute for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia;
| | - Ana Loncar
- Institute for Biocides and Medical Ecology, 11000 Belgrade, Serbia; (A.L.); (M.V.); (D.D.)
| | | | - Marija Vukcevic
- Institute for Biocides and Medical Ecology, 11000 Belgrade, Serbia; (A.L.); (M.V.); (D.D.)
| | - Dragana Despot
- Institute for Biocides and Medical Ecology, 11000 Belgrade, Serbia; (A.L.); (M.V.); (D.D.)
| | - Tatjana Djukic
- Institute of Medical and Clinical Biochemistry, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia;
| | - Vladimir Djukic
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia; (V.D.); (A.M.); (N.P.)
- University Clinic “Dr Dragisa Misovic”, 11000 Belgrade, Serbia;
| | - Andjela Milovanovic
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia; (V.D.); (A.M.); (N.P.)
- Clinic for Medical Rehabilitation, Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Nikola Panic
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia; (V.D.); (A.M.); (N.P.)
- University Clinic “Dr Dragisa Misovic”, 11000 Belgrade, Serbia;
| | - Nemanja Plecic
- University Clinic “Dr Dragisa Misovic”, 11000 Belgrade, Serbia;
| | - Ana Banko
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
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9
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Sofia de Olazarra A, Chen FE, Wang TH, Wang SX. Rapid, Point-of-Care Host-Based Gene Expression Diagnostics Using Giant Magnetoresistive Biosensors. ACS Sens 2023; 8:2780-2790. [PMID: 37368357 DOI: 10.1021/acssensors.3c00696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Host-based gene expression analysis is a promising tool for a broad range of clinical applications, including rapid infectious disease diagnostics and real-time disease monitoring. However, the complex instrumentation requirements and slow turnaround-times associated with traditional gene expression analysis methods have hampered their widespread adoption at the point-of-care (POC). To overcome these challenges, we have developed an automated and portable platform that utilizes polymerase chain reaction (PCR) and giant magnetoresistive (GMR) biosensors to perform rapid multiplexed, targeted gene expression analysis at the POC. As proof-of-concept, we utilized our platform to amplify and measure the expression of four genes (HERC5, HERC6, IFI27, and IFIH1) that were previously shown to be upregulated in hosts infected with influenza viruses. The compact instrument conducted highly automated PCR amplification and GMR detection to measure the expression of the four genes in multiplex, then utilized Bluetooth communication to relay results to users on a smartphone application. To validate the platform, we tested 20 cDNA samples from symptomatic patients that had been previously diagnosed as either influenza-positive or influenza-negative using a RT-PCR virology panel. A non-parametric Mann-Whitney test revealed that day 0 (day of symptom onset) gene expression was significantly different between the two groups (p < 0.0001, n = 20). Hence, we preliminarily demonstrated that our platform could accurately discriminate between symptomatic influenza and non-influenza populations based on host gene expression in ∼30 min. This study not only establishes the potential clinical utility of our proposed assay and device for influenza diagnostics but it also paves the way for broadscale and decentralized implementation of host-based gene expression diagnostics at the POC.
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Affiliation(s)
- Ana Sofia de Olazarra
- Department of Electrical Engineering, Stanford University, Stanford, California 94035, United States
| | - Fan-En Chen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Tza-Huei Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Shan X Wang
- Department of Electrical Engineering, Stanford University, Stanford, California 94035, United States
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
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10
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Kerslake R, Belay B, Panfilov S, Hall M, Kyrou I, Randeva HS, Hyttinen J, Karteris E, Sisu C. Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models. Cancers (Basel) 2023; 15:3350. [PMID: 37444459 DOI: 10.3390/cancers15133350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012-2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial-mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.
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Affiliation(s)
- Rachel Kerslake
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Birhanu Belay
- Computational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Suzana Panfilov
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Marcia Hall
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
- Mount Vernon Cancer Centre, Rickmansworth Road, Northwood HA6 2RN, UK
| | - Ioannis Kyrou
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Research Institute for Health & Wellbeing, Coventry University, Coventry CV1 5FB, UK
- Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
- Laboratory of Dietetics and Quality of Life, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece
| | - Harpal S Randeva
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Jari Hyttinen
- Computational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland
| | - Emmanouil Karteris
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| | - Cristina Sisu
- Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK
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11
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Momeni M, Rashidifar M, Balam FH, Roointan A, Gholaminejad A. A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures. Sci Rep 2023; 13:5599. [PMID: 37019895 PMCID: PMC10075178 DOI: 10.1038/s41598-023-32268-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/24/2023] [Indexed: 04/07/2023] Open
Abstract
COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.
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Affiliation(s)
- Maryam Momeni
- Department of Biotechnology, Faculty of Biological Science and Technology, The University of Isfahan, Isfahan, Iran
| | - Maryam Rashidifar
- Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Farinaz Hosseini Balam
- Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Roointan
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan Univerity of Medical Sciences, Hezar Jarib St, Isfahan, 81746-73461, Iran
| | - Alieh Gholaminejad
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan Univerity of Medical Sciences, Hezar Jarib St, Isfahan, 81746-73461, Iran.
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12
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Pelaia TM, Shojaei M, McLean AS. The Role of Transcriptomics in Redefining Critical Illness. Crit Care 2023; 27:89. [PMID: 36941625 PMCID: PMC10027592 DOI: 10.1186/s13054-023-04364-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023] Open
Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .
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Affiliation(s)
- Tiana M Pelaia
- Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia.
| | - Maryam Shojaei
- Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia
- Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
| | - Anthony S McLean
- Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia
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13
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Li J, Ren J, Liao H, Guo W, Feng K, Huang T, Cai YD. Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods. Front Microbiol 2023; 14:1138674. [PMID: 37007526 PMCID: PMC10063797 DOI: 10.3389/fmicb.2023.1138674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/01/2023] [Indexed: 03/19/2023] Open
Abstract
To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body’s immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2–4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1–4, and days 7–10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.
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Affiliation(s)
- Jing Li
- School of Computer Science, Baicheng Normal University, Baicheng, Jilin, China
| | - JingXin Ren
- School of Life Sciences, Shanghai University, Shanghai, China
| | | | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - Tao Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Tao Huang, ; Yu-Dong Cai,
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Tao Huang, ; Yu-Dong Cai,
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14
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Lim FY, Kim SY, Kulkarni KN, Blazevic RL, Kimball LE, Lea HG, Haack AJ, Gower MS, Stevens-Ayers T, Starita LM, Boeckh M, Schiffer JT, Hyrien O, Theberge AB, Waghmare A. Longitudinal home self-collection of capillary blood using homeRNA correlates interferon and innate viral defense pathways with SARS-CoV-2 viral clearance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.24.23284913. [PMID: 37034678 PMCID: PMC10081427 DOI: 10.1101/2023.01.24.23284913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Blood transcriptional profiling is a powerful tool to evaluate immune responses to infection; however, blood collection via traditional phlebotomy remains a barrier to precise characterization of the immune response in dynamic infections (e.g., respiratory viruses). Here we present an at-home self-collection methodology, homeRNA, to study the host transcriptional response during acute SARS-CoV-2 infections. This method uniquely enables high frequency measurement of the host immune kinetics in non-hospitalized adults during the acute and most dynamic stage of their infection. COVID-19+ and healthy participants self-collected blood every other day for two weeks with daily nasal swabs and symptom surveys to track viral load kinetics and symptom burden, respectively. While healthy uninfected participants showed remarkably stable immune kinetics with no significant dynamic genes, COVID-19+ participants, on the contrary, depicted a robust response with over 418 dynamic genes associated with interferon and innate viral defense pathways. When stratified by vaccination status, we detected distinct response signatures between unvaccinated and breakthrough (vaccinated) infection subgroups; unvaccinated individuals portrayed a response repertoire characterized by higher innate antiviral responses, interferon signaling, and cytotoxic lymphocyte responses while breakthrough infections portrayed lower levels of interferon signaling and enhanced early cell-mediated response. Leveraging cross-platform longitudinal sampling (nasal swabs and blood), we observed that IFI27, a key viral response gene, tracked closely with SARS-CoV-2 viral clearance in individual participants. Taken together, these results demonstrate that at-home sampling can capture key host antiviral responses and facilitate frequent longitudinal sampling to detect transient host immune kinetics during dynamic immune states.
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Affiliation(s)
- Fang Yun Lim
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
- Department of Chemistry, University of Washington; Seattle, Washington, U.S.A
| | - Soo-Young Kim
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Karisma N. Kulkarni
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Rachel L. Blazevic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Louise E. Kimball
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Hannah G. Lea
- Department of Chemistry, University of Washington; Seattle, Washington, U.S.A
| | - Amanda J. Haack
- Department of Chemistry, University of Washington; Seattle, Washington, U.S.A
| | - Maia. S. Gower
- Department of Chemistry, University of Washington; Seattle, Washington, U.S.A
| | - Terry Stevens-Ayers
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Lea M. Starita
- Brotman Baty Institute, University of Washington, Seattle
- Department of Genome Sciences, University of Washington, Seattle
| | - Michael Boeckh
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
- Department of Medicine, University of Washington; Seattle, Washington, U.S.A
| | - Joshua T. Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
- Department of Medicine, University of Washington; Seattle, Washington, U.S.A
| | - Ollivier Hyrien
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
| | - Ashleigh B. Theberge
- Department of Chemistry, University of Washington; Seattle, Washington, U.S.A
- Department of Urology, University of Washington; Seattle, Washington, U.S.A
| | - Alpana Waghmare
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, Washington, U.S.A
- Department of Pediatrics, University of Washington; Seattle, Washington, U.S.A
- Seattle Children’s Research Institute; Seattle, Washington, U.S.A
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15
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Shojaei M, Shamshirian A, Monkman J, Grice L, Tran M, Tan CW, Teo SM, Rodrigues Rossi G, McCulloch TR, Nalos M, Raei M, Razavi A, Ghasemian R, Gheibi M, Roozbeh F, Sly PD, Spann KM, Chew KY, Zhu Y, Xia Y, Wells TJ, Senegaglia AC, Kuniyoshi CL, Franck CL, dos Santos AFR, de Noronha L, Motamen S, Valadan R, Amjadi O, Gogna R, Madan E, Alizadeh-Navaei R, Lamperti L, Zuñiga F, Nova-Lamperti E, Labarca G, Knippenberg B, Herwanto V, Wang Y, Phu A, Chew T, Kwan T, Kim K, Teoh S, Pelaia TM, Kuan WS, Jee Y, Iredell J, O’Byrne K, Fraser JF, Davis MJ, Belz GT, Warkiani ME, Gallo CS, Souza-Fonseca-Guimaraes F, Nguyen Q, Mclean A, Kulasinghe A, Short KR, Tang B. IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study. Front Immunol 2023; 13:1060438. [PMID: 36685600 PMCID: PMC9850159 DOI: 10.3389/fimmu.2022.1060438] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/09/2022] [Indexed: 01/07/2023] Open
Abstract
Purpose Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. Methods We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients. Results We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients. Conclusion These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.
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Affiliation(s)
- Maryam Shojaei
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia,Centre for Immunology and Allergy Research, the Westmead Institute for Medical Research, Westmead, NSW, Australia,Department of Medicine, Sydney Medical School Nepean, Nepean Hospital, University of Sydney, Penrith, NSW, Australia,*Correspondence: Arutha Kulasinghe, ; Kirsty R. Short, ; Maryam Shojaei,
| | - Amir Shamshirian
- Gastrointestinal Cancer Research Centre, Non-Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - James Monkman
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Laura Grice
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia,School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Minh Tran
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Chin Wee Tan
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, VIC, Australia,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Siok Min Teo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Gustavo Rodrigues Rossi
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Timothy R. McCulloch
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Marek Nalos
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia
| | - Maedeh Raei
- Gastrointestinal Cancer Research Centre, Non-Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Alireza Razavi
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Roya Ghasemian
- Antimicrobial Resistance Research Centre, Department of Infectious Diseases, Mazandaran University of Medical Sciences, Sari, Iran
| | - Mobina Gheibi
- Student Research Committee, School of Allied Medical Sciences, Mazandaran University of Medical Science, Sari, Iran
| | | | - Peter D. Sly
- Child Health Research Centre, The University of Queensland, South Brisbane, QLD, Australia
| | - Kirsten M. Spann
- Centre for Immunology and Infection Control, Faculty of Health, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Keng Yih Chew
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Yanshan Zhu
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Yao Xia
- School of Science, Edith Cowan University; School of Biomedical Science, University of Western Australia, Perth, WA, Australia
| | - Timothy J. Wells
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alexandra Cristina Senegaglia
- Complexo Hospital de Clinicas, Universidade Federal do Paraná, Curitiba, Brazil,Core for Cell Technology, School of Medicine, PontifìciaUniversidade Católica do Paraná, Curitiba, Brazil
| | - Carmen Lúcia Kuniyoshi
- Complexo Hospital de Clinicas, Universidade Federal do Paraná, Curitiba, Brazil,Core for Cell Technology, School of Medicine, PontifìciaUniversidade Católica do Paraná, Curitiba, Brazil
| | | | | | | | - Sepideh Motamen
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Reza Valadan
- Molecular and Cell Biology Research Centre, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran,Department of Immunology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Omolbanin Amjadi
- Gastrointestinal Cancer Research Centre, Non-Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Rajan Gogna
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark,Novo Nordisk Foundation centre for Stem Cell Biology, DanStem, Faculty of Health and Medical Sciences, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Esha Madan
- Campania Centre for the Unknown, Lisbon, Portugal
| | - Reza Alizadeh-Navaei
- Gastrointestinal Cancer Research Centre, Non-Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Liliana Lamperti
- Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile
| | - Felipe Zuñiga
- Molecular and Translational Immunology Laboratory, Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Universidad de Concepcion, Concepcion, Chile
| | - Estefania Nova-Lamperti
- Molecular and Translational Immunology Laboratory, Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Universidad de Concepcion, Concepcion, Chile
| | - Gonzalo Labarca
- Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile,Faculty of Medicine, Universidad de Concepcion, Concepcion, Chile
| | - Ben Knippenberg
- Infectious Diseases Department, Royal Darwin Hospital, Darwin, NT, Australia
| | - Velma Herwanto
- Faculty of Medicine, Universitas Tarumanagara, Jakarta, Indonesia
| | - Ya Wang
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia,Centre for Immunology and Allergy Research, the Westmead Institute for Medical Research, Westmead, NSW, Australia,Department of Medicine, Sydney Medical School Nepean, Nepean Hospital, University of Sydney, Penrith, NSW, Australia
| | - Amy Phu
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia,Westmead Clinical School, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Tracy Chew
- Sydney Informatics Hub, Core Research Facilities, University of Sydney, Sydney, NSW, Australia
| | - Timothy Kwan
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia
| | - Karan Kim
- Centre for Immunology and Allergy Research, the Westmead Institute for Medical Research, Westmead, NSW, Australia
| | - Sally Teoh
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia
| | - Tiana M. Pelaia
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia
| | - Win Sen Kuan
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore,Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yvette Jee
- Emergency Medicine Department, National University Hospital, National University Health System, Singapore, Singapore
| | - Jon Iredell
- Faculty of Medicine and Health, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia,Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, Sydney, NSW, Australia,Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
| | - Ken O’Byrne
- Queensland University of Technology, Centre for Genomics and PersonalisedHealth, School of Biomedical Sciences, Brisbane, QLD, Australia
| | - John F. Fraser
- Critical Care Research Group, The University of Queensland, Brisbane, QLD, Australia
| | - Melissa J. Davis
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, VIC, Australia,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Gabrielle T. Belz
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Majid E. Warkiani
- Australia Centre for Health Technologies (CHT) & Institute for Biomedical Materials & Devices (IBMD), School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, Australia
| | - Carlos Salomon Gallo
- Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, University of Concepcion, Concepcion, Chile,Exosome Biology Laboratory, Centre for Clinical Diagnostics, University of Queensland Centre for Clinical Research, Royal Brisbane and Women’s Hospital, The University of Queensland, Brisbane, QLD, Australia
| | | | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anthony Mclean
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia,*Correspondence: Arutha Kulasinghe, ; Kirsty R. Short, ; Maryam Shojaei,
| | - Kirsty R. Short
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia,*Correspondence: Arutha Kulasinghe, ; Kirsty R. Short, ; Maryam Shojaei,
| | - Benjamin Tang
- Department of Intensive Care Medicine, Nepean Hospital, Penrith, NSW, Australia,Centre for Immunology and Allergy Research, the Westmead Institute for Medical Research, Westmead, NSW, Australia
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16
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Villamayor L, López-García D, Rivero V, Martínez-Sobrido L, Nogales A, DeDiego ML. The IFN-stimulated gene IFI27 counteracts innate immune responses after viral infections by interfering with RIG-I signaling. Front Microbiol 2023; 14:1176177. [PMID: 37187533 PMCID: PMC10175689 DOI: 10.3389/fmicb.2023.1176177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
The recognition of viral nucleic acids by host pattern recognition receptors (PRRs) is critical for initiating innate immune responses against viral infections. These innate immune responses are mediated by the induction of interferons (IFNs), IFN-stimulated genes (ISGs) and pro-inflammatory cytokines. However, regulatory mechanisms are critical to avoid excessive or long-lasting innate immune responses that may cause detrimental hyperinflammation. Here, we identified a novel regulatory function of the ISG, IFN alpha inducible protein 27 (IFI27) in counteracting the innate immune responses triggered by cytoplasmic RNA recognition and binding. Our model systems included three unrelated viral infections caused by Influenza A virus (IAV), Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), and Sendai virus (SeV), and transfection with an analog of double-stranded (ds) RNA. Furthermore, we found that IFI27 has a positive effect on IAV and SARS-CoV-2 replication, most likely due to its ability to counteract host-induced antiviral responses, including in vivo. We also show that IFI27 interacts with nucleic acids and PRR retinoic acid-inducible gene I (RIG-I), being the interaction of IFI27 with RIG-I most likely mediated through RNA binding. Interestingly, our results indicate that interaction of IFI27 with RIG-I impairs RIG-I activation, providing a molecular mechanism for the effect of IFI27 on modulating innate immune responses. Our study identifies a molecular mechanism that may explain the effect of IFI27 in counterbalancing innate immune responses to RNA viral infections and preventing excessive innate immune responses. Therefore, this study will have important implications in drug design to control viral infections and viral-induced pathology.
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Affiliation(s)
- Laura Villamayor
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
| | - Darío López-García
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
| | - Vanessa Rivero
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
| | | | - Aitor Nogales
- Center for Animal Health Research, CISA-INIA-CSIC, Madrid, Spain
| | - Marta L. DeDiego
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
- *Correspondence: Marta L. DeDiego,
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17
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Sun Z, Ke L, Zhao Q, Qu J, Hu Y, Gao H, Peng Z. The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients. Front Immunol 2023; 14:1098688. [PMID: 36911695 PMCID: PMC9992716 DOI: 10.3389/fimmu.2023.1098688] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
Background SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported. Methods COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression. Results The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L. Conclusion We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.
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Affiliation(s)
- Zhongyi Sun
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
| | - Li Ke
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
| | - Qiuyue Zhao
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
| | - Jiachen Qu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
| | - Yanan Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
| | - Han Gao
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhiyong Peng
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.,Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China
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18
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Chawla DG, Cappuccio A, Tamminga A, Sealfon SC, Zaslavsky E, Kleinstein SH. Benchmarking transcriptional host response signatures for infection diagnosis. Cell Syst 2022; 13:974-988.e7. [PMID: 36549274 PMCID: PMC9768893 DOI: 10.1016/j.cels.2022.11.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/04/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Daniel G Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrea Tamminga
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA; Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT 06511, USA.
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19
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Rao AM, Popper SJ, Gupta S, Davong V, Vaidya K, Chanthongthip A, Dittrich S, Robinson MT, Vongsouvath M, Mayxay M, Nawtaisong P, Karmacharya B, Thair SA, Bogoch I, Sweeney TE, Newton PN, Andrews JR, Relman DA, Khatri P. A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations. Cell Rep Med 2022; 3:100842. [PMID: 36543117 PMCID: PMC9797950 DOI: 10.1016/j.xcrm.2022.100842] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/12/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022]
Abstract
Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.
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Affiliation(s)
- Aditya M. Rao
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Immunology Graduate Program, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Stephen J. Popper
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Sanjana Gupta
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Viengmon Davong
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Krista Vaidya
- Dhulikhel Hospital, Kathmandu University Hospital, Kavrepalanchok, Nepal
| | - Anisone Chanthongthip
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Sabine Dittrich
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Matthew T. Robinson
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Manivanh Vongsouvath
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Mayfong Mayxay
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Institute of Research and Education Development (IRED), University of Health Sciences, Ministry of Health, Vientiane, Lao PDR
| | - Pruksa Nawtaisong
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Biraj Karmacharya
- Dhulikhel Hospital, Kathmandu University Hospital, Kavrepalanchok, Nepal
| | - Simone A. Thair
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Isaac Bogoch
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Paul N. Newton
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jason R. Andrews
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - David A. Relman
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA,Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA,Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Purvesh Khatri
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA,Corresponding author
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20
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Li X, Kolling FW, Aridgides D, Mellinger D, Ashare A, Jakubzick CV. ScRNA-seq expression of IFI27 and APOC2 identifies four alveolar macrophage superclusters in healthy BALF. Life Sci Alliance 2022; 5:e202201458. [PMID: 35820705 PMCID: PMC9275597 DOI: 10.26508/lsa.202201458] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/20/2022] [Accepted: 06/27/2022] [Indexed: 12/15/2022] Open
Abstract
Alveolar macrophages (AMs) reside on the luminal surface of the airways and alveoli, ensuring proper gas exchange by ingesting cellular debris and pathogens, and regulating inflammatory responses. Therefore, understanding the heterogeneity and diverse roles played by AMs, interstitial macrophages, and recruited monocytes is critical for treating airway diseases. We performed single-cell RNA sequencing on 113,213 bronchoalveolar lavage cells from four healthy and three uninflamed cystic fibrosis subjects and identified two MARCKS+LGMN+IMs, FOLR2+SELENOP+ and SPP1+PLA2G7+ IMs, monocyte subtypes, DC1, DC2, migDCs, plasmacytoid DCs, lymphocytes, epithelial cells, and four AM superclusters (families) based on the gene expression of IFI27 and APOC2 These four AM families have at least eight distinct functional members (subclusters) named after their differentially expressed gene(s): IGF1, CCL18, CXCL5, cholesterol, chemokine, metallothionein, interferon, and small-cluster AMs. Interestingly, the chemokine cluster further divides with each subcluster selectively expressing a unique combination of chemokines. One of the most striking observations, besides the heterogeneity, is the conservation of AM family members in relatively equal ratio across all AM superclusters and individuals. Transcriptional data and TotalSeq technology were used to investigate cell surface markers that distinguish resident AMs from recruited monocytes. Last, other AM datasets were projected onto our dataset. Similar AM superclusters and functional subclusters were observed, along with a significant increase in chemokine and IFN AM subclusters in individuals infected with COVID-19. Overall, functional specializations of the AM subclusters suggest that there are highly regulated AM niches with defined programming states, highlighting a clear division of labor.
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Affiliation(s)
- Xin Li
- Department of Microbiology and Immunology, Dartmouth Geisel School of Medicine, Hanover, NH, USA
| | - Fred W Kolling
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, USA
| | - Daniel Aridgides
- Department of Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Diane Mellinger
- Department of Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Alix Ashare
- Department of Microbiology and Immunology, Dartmouth Geisel School of Medicine, Hanover, NH, USA
- Department of Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Claudia V Jakubzick
- Department of Microbiology and Immunology, Dartmouth Geisel School of Medicine, Hanover, NH, USA
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21
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Bowler S, Papoutsoglou G, Karanikas A, Tsamardinos I, Corley MJ, Ndhlovu LC. A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity. Sci Rep 2022; 12:17480. [PMID: 36261477 PMCID: PMC9580434 DOI: 10.1038/s41598-022-22201-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 10/11/2022] [Indexed: 01/12/2023] Open
Abstract
Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies.
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Affiliation(s)
- Scott Bowler
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, 413 E 69th St, New York, NY, 10021, USA
| | - Georgios Papoutsoglou
- JADBio - Gnosis DA S.A, Science and Technology Park of Crete, 70013, Heraklion, Greece
| | - Aristides Karanikas
- JADBio - Gnosis DA S.A, Science and Technology Park of Crete, 70013, Heraklion, Greece
| | - Ioannis Tsamardinos
- JADBio - Gnosis DA S.A, Science and Technology Park of Crete, 70013, Heraklion, Greece
- Department of Computer Science, University of Crete, 70013, Heraklion, Greece
| | - Michael J Corley
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, 413 E 69th St, New York, NY, 10021, USA
| | - Lishomwa C Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, 413 E 69th St, New York, NY, 10021, USA.
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22
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Chen L, Hua J, He X. Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation. BMC Genomics 2022; 23:703. [PMID: 36243706 PMCID: PMC9569050 DOI: 10.1186/s12864-022-08915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). RESULTS In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. CONCLUSION We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.
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Affiliation(s)
- Liang Chen
- Department of Infectious Diseases, Nanjing Lishui People's Hospital, Zhongda Hospital Lishui Branch, Southeast University, Nanjing, China
| | - Jie Hua
- Department of Gastroenterology, Liyang People's Hospital, Liyang Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Xiaopu He
- Department of Geriatric Gastroenterology, The First Affiliated Hospital With Nanjing Medical University, No.300 Guangzhou Road, Nanjing city, 210029, Jiangsu Province, China.
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Zhang Z. Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19. Vaccines (Basel) 2022; 10:vaccines10101657. [PMID: 36298522 PMCID: PMC9608907 DOI: 10.3390/vaccines10101657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.
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Affiliation(s)
- Zhengjun Zhang
- Department of Statistics, School of Computer, Data & Information Sciences, University of Wisconsin, Madison, WI 53706, USA
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24
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Bhat A, Irizar H, Couch ACM, Raval P, Duarte RRR, Dutan Polit L, Hanger B, Powell T, Deans PJM, Shum C, Nagy R, McAlonan G, Iyegbe CO, Price J, Bramon E, Bhattacharyya S, Vernon AC, Srivastava DP. Attenuated transcriptional response to pro-inflammatory cytokines in schizophrenia hiPSC-derived neural progenitor cells. Brain Behav Immun 2022; 105:82-97. [PMID: 35716830 PMCID: PMC9810540 DOI: 10.1016/j.bbi.2022.06.010] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 05/29/2022] [Accepted: 06/13/2022] [Indexed: 01/07/2023] Open
Abstract
Maternal immune activation (MIA) during prenatal development is an environmental risk factor for psychiatric disorders including schizophrenia (SZ). Converging lines of evidence from human and animal model studies suggest that elevated cytokine levels in the maternal and fetal compartments are an important indication of the mechanisms driving this association. However, there is variability in susceptibility to the psychiatric risk conferred by MIA, likely influenced by genetic factors. How MIA interacts with a genetic profile susceptible to SZ is challenging to test in animal models. To address this gap, we examined whether differential gene expression responses occur in forebrain-lineage neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (hiPSC) generated from three individuals with a diagnosis of schizophrenia and three healthy controls. Following acute (24 h) treatment with either interferon-gamma (IFNγ; 25 ng/μl) or interleukin (IL)-1β (10 ng/μl), we identified, by RNA sequencing, 3380 differentially expressed genes (DEGs) in the IFNγ-treated control lines (compared to untreated controls), and 1980 DEGs in IFNγ-treated SZ lines (compared to untreated SZ lines). Out of 4137 genes that responded significantly to IFNγ across all lines, 1223 were common to both SZ and control lines. The 2914 genes that appeared to respond differentially to IFNγ treatment in SZ lines were subjected to a further test of significance (multiple testing correction applied to the interaction effect between IFNγ treatment and SZ diagnosis), yielding 359 genes that passed the significance threshold. There were no differentially expressed genes in the IL-1β-treatment conditions after Benjamini-Hochberg correction. Gene set enrichment analysis however showed that IL-1β impacts immune function and neuronal differentiation. Overall, our data suggest that a) SZ NPCs show an attenuated transcriptional response to IFNγ treatment compared to controls; b) Due to low IL-1β receptor expression in NPCs, NPC cultures appear to be less responsive to IL-1β than IFNγ; and c) the genes differentially regulated in SZ lines - in the face of a cytokine challenge - are primarily associated with mitochondrial, "loss-of-function", pre- and post-synaptic gene sets. Our findings particularly highlight the role of early synaptic development in the association between maternal immune activation and schizophrenia risk.
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Affiliation(s)
- Anjali Bhat
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK; Division of Psychiatry, University College London, London, UK; Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Haritz Irizar
- Division of Psychiatry, University College London, London, UK; Icahn School of Medicine, Mount Sinai Hospital, NY, USA
| | - Amalie C M Couch
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Pooja Raval
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Rodrigo R R Duarte
- Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Medicine, Weill Cornell Medical College, Cornell University, NY, USA
| | - Lucia Dutan Polit
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Bjorn Hanger
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Timothy Powell
- Department of Social, Genetic & Developmental Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Medicine, Weill Cornell Medical College, Cornell University, NY, USA
| | - P J Michael Deans
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Carole Shum
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Roland Nagy
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, UK; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Conrad O Iyegbe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Jack Price
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | | | - Anthony C Vernon
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK.
| | - Deepak P Srivastava
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, UK.
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25
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McLean AS, Shojaei M. Transcriptomics in the intensive care unit. THE LANCET. RESPIRATORY MEDICINE 2022; 10:824-826. [PMID: 35878620 DOI: 10.1016/s2213-2600(22)00257-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Anthony S McLean
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW 2747, Australia; Centre for Immunology and Allergy Research, Watermead Institute for Medical Research, Sydney, NSW, Australia.
| | - Maryam Shojaei
- Department of Intensive Care Medicine, Nepean Hospital, Sydney, NSW 2747, Australia; Centre for Immunology and Allergy Research, Watermead Institute for Medical Research, Sydney, NSW, Australia
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26
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Xiong N, Sun Q. Identification of stage-related and severity-related biomarkers and exploration of immune landscape for Dengue by comprehensive analyses. Virol J 2022; 19:130. [PMID: 35918744 PMCID: PMC9344228 DOI: 10.1186/s12985-022-01853-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 07/14/2022] [Indexed: 11/22/2022] Open
Abstract
Background At present, there are still no specific therapeutic drugs and appropriate vaccines for Dengue. Therefore, it is important to explore distinct clinical diagnostic indicators. Methods In this study, we combined differentially expressed genes (DEGs) analysis, weighted co-expression network analysis (WGCNA) and Receiver Operator Characteristic Curve (ROC) to screen a stable and robust biomarker with diagnosis value for Dengue patients. CIBERSORT was used to evaluate immune landscape of Dengue patients. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were applied to explore potential functions of hub genes. Results CD38 and Plasma cells have excellent Area Under the Curve (AUC) in distinguishing clinical stages for Dengue patients, and activated memory CD4+ T cells and Monocytes have good AUC for this function. ZNF595 has acceptable AUC in discriminating dengue hemorrhagic fever (DHF) from dengue fever (DF) in whole acute stages. Analyzing any serotype, we can obtain consistent results. Negative inhibition of viral replication based on GO, KEGG and GSEA analysis results, up-regulated autophagy genes and the impairing immune system are potential reasons resulting in DHF. Conclusions CD38, Plasma cells, activated memory CD4+ T cells and Monocytes can be used to distinguish clinical stages for dengue patients, and ZNF595 can be used to discriminate DHF from DF, regardless of serotypes. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12985-022-01853-8.
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Affiliation(s)
- Nan Xiong
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, People's Republic of China.,Kunming Medical University, Kunming, 650500, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, 650118, People's Republic of China
| | - Qiangming Sun
- Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, People's Republic of China. .,Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, 650118, People's Republic of China.
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27
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Virus Infection and Systemic Inflammation: Lessons Learnt from COVID-19 and Beyond. Cells 2022; 11:cells11142198. [PMID: 35883640 PMCID: PMC9316821 DOI: 10.3390/cells11142198] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/28/2022] [Accepted: 07/09/2022] [Indexed: 02/06/2023] Open
Abstract
Respiratory infections with newly emerging zoonotic viruses such as SARS-CoV-2, the etiological agent of COVID-19, often lead to the perturbation of the human innate and adaptive immune responses causing severe disease with high mortality. The responsible mechanisms are commonly virus-specific and often include either over-activated or delayed local interferon responses, which facilitate efficient viral replication in the primary target organ, systemic viral spread, and rapid onset of organ-specific and harmful inflammatory responses. Despite the distinct replication strategies, human infections with SARS-CoV-2 and highly pathogenic avian influenza viruses demonstrate remarkable similarities and differences regarding the mechanisms of immune induction, disease dynamics, as well as the long-term sequelae, which will be discussed in this review. In addition, we will highlight some important lessons about the effectiveness of antiviral and immunomodulatory therapeutic strategies that this pandemic has taught us.
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28
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Ravi N, Chang SE, Franco LM, Nagamani SCS, Khatri P, Utz PJ, Wang SX. A GMR-based assay for quantification of the human response to influenza. Biosens Bioelectron 2022; 205:114086. [PMID: 35192997 PMCID: PMC8986584 DOI: 10.1016/j.bios.2022.114086] [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: 10/14/2021] [Revised: 01/12/2022] [Accepted: 02/07/2022] [Indexed: 01/26/2023]
Abstract
Detecting and quantifying the host transcriptional response to influenza virus infection can serve as a real-time diagnostic tool for clinical management. We have employed the multiplexing capabilities of GMR sensors to develop a novel assay based on the influenza metasignature (IMS), which can classify influenza infection based on transcript levels. We show that the assay can reliably detect ten IMS transcripts and distinguish subjects with naturally acquired influenza infection from those with other symptomatic viral infections (AUC 0.93, 95% CI: 0.82-1.00). Separately, we validated that the gene IFI27, not included in the IMS panel, has very high single-biomarker accuracy (AUC 0.95, 95% CI: 0.90-0.99) in stratifying patients with influenza. We demonstrate that a portable GMR biosensor can be used as a tool to diagnose influenza infection by measuring the host response, simultaneously highlighting the power of immune system metrics and advancing the field of gene expression-based diagnostics.
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Affiliation(s)
- Neeraja Ravi
- Department of Bioengineering, Stanford University, Stanford, CA, 93405, USA.
| | - Sarah E Chang
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA; Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Luis M Franco
- Functional Immunogenomics Unit, Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Sandesh C S Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Purvesh Khatri
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Division of Biomedical Informatics, Department of Medicine, Stanford University, Stanford, CA, USA.
| | - Paul J Utz
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA; Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
| | - Shan X Wang
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
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Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .
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Affiliation(s)
- Ya Wang
- Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia. .,Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, Australia.
| | - Anthony S McLean
- Department of Intensive Care Medicine, Nepean Hospital, Kingswood, NSW, Australia
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30
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Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence. Pathogens 2022; 11:pathogens11030311. [PMID: 35335635 PMCID: PMC8948624 DOI: 10.3390/pathogens11030311] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/24/2022] [Accepted: 02/27/2022] [Indexed: 02/04/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil–lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.
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31
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Winter C, Camarão AAR, Steffen I, Jung K. Network meta-analysis of transcriptome expression changes in different manifestations of dengue virus infection. BMC Genomics 2022; 23:165. [PMID: 35220956 PMCID: PMC8882220 DOI: 10.1186/s12864-022-08390-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/15/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Several studies have been performed to study transcriptome profiles after dengue virus infections with partly different results. Due to slightly different settings of the individual studies, different genes and enriched gene sets are reported in these studies. The main aim of this network meta-analysis was to aggregate a selection of these studies to identify genes and gene sets that are more generally associated with dengue virus infection, i.e. with less dependence on the individual study settings.
Methods
We performed network meta-analysis by different approaches using publicly available gene expression data of five selected studies from the Gene Expression Omnibus database. The study network includes dengue fever (DF), hemorrhagic fever (DHF), shock syndrome (DSS) patients as well as convalescent and healthy control individuals. After data merging and missing value imputation, study-specific batch effects were removed. Pairwise differential expression analysis and subsequent gene-set enrichment analysis were performed between the five study groups. Furthermore, mutual information networks were derived from the top genes of each group comparison, and the separability between the three patient groups was studied by machine learning models.
Results
From the 10 possible pairwise group comparisons in the study network, six genes (IFI27, TPX2, CDT1, DTL, KCTD14 and CDCA3) occur with a noticeable frequency among the top listed genes of each comparison. Thus, there is an increased evidence that these genes play a general role in dengue virus infections. IFI27 and TPX2 have also been highlighted in the context of dengue virus infection by other studies. A few of the identified gene sets from the network meta-analysis overlap with findings from the original studies. Mutual information networks yield additional genes for which the observed pairwise correlation is different between the patient groups. Machine learning analysis shows a moderate separability of samples from the DF, DHF and DSS groups (accuracy about 80%).
Conclusions
Due to an increased sample size, the network meta-analysis could reveal additional genes which are called differentially expressed between the studied groups and that may help to better understand the molecular basis of this disease.
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32
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Atallah J, Mansour MK. Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review. Front Med (Lausanne) 2022; 9:805107. [PMID: 35186993 PMCID: PMC8850635 DOI: 10.3389/fmed.2022.805107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 12/15/2022] Open
Abstract
Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.
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Affiliation(s)
- Johnny Atallah
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Michael K Mansour
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
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33
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Bodkin N, Ross M, McClain MT, Ko ER, Woods CW, Ginsburg GS, Henao R, Tsalik EL. Systematic comparison of published host gene expression signatures for bacterial/viral discrimination. Genome Med 2022; 14:18. [PMID: 35184750 PMCID: PMC8858657 DOI: 10.1186/s13073-022-01025-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 02/09/2022] [Indexed: 12/13/2022] Open
Abstract
Background Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. Methods This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. Results Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. Conclusions In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01025-x.
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Wang Y, Li J, Zhang L, Sun HX, Zhang Z, Xu J, Xu Y, Lin Y, Zhu A, Luo Y, Zhou H, Wu Y, Lin S, Sun Y, Xiao F, Chen R, Wen L, Chen W, Li F, Ou R, Zhang Y, Kuo T, Li Y, Li L, Sun J, Sun K, Zhuang Z, Lu H, Chen Z, Mai G, Zhuo J, Qian P, Chen J, Yang H, Wang J, Xu X, Zhong N, Zhao J, Li J, Zhao J, Jin X. Plasma cell-free RNA characteristics in COVID-19 patients. Genome Res 2022; 32:228-241. [PMID: 35064006 PMCID: PMC8805721 DOI: 10.1101/gr.276175.121] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/21/2021] [Indexed: 12/15/2022]
Abstract
The pathogenesis of COVID-19 is still elusive, which impedes disease progression prediction, differential diagnosis, and targeted therapy. Plasma cell-free RNAs (cfRNAs) carry unique information from human tissue and thus could point to resourceful solutions for pathogenesis and host-pathogen interactions. Here, we performed a comparative analysis of cfRNA profiles between COVID-19 patients and healthy donors using serial plasma. Analyses of the cfRNA landscape, potential gene regulatory mechanisms, dynamic changes in tRNA pools upon infection, and microbial communities were performed. A total of 380 cfRNA molecules were up-regulated in all COVID-19 patients, of which seven could serve as potential biomarkers (AUC > 0.85) with great sensitivity and specificity. Antiviral (NFKB1A, IFITM3, and IFI27) and neutrophil activation (S100A8, CD68, and CD63)–related genes exhibited decreased expression levels during treatment in COVID-19 patients, which is in accordance with the dynamically enhanced inflammatory response in COVID-19 patients. Noncoding RNAs, including some microRNAs (let 7 family) and long noncoding RNAs (GJA9-MYCBP) targeting interleukin (IL6/IL6R), were differentially expressed between COVID-19 patients and healthy donors, which accounts for the potential core mechanism of cytokine storm syndromes; the tRNA pools change significantly between the COVID-19 and healthy group, leading to the accumulation of SARS-CoV-2 biased codons, which facilitate SARS-CoV-2 replication. Finally, several pneumonia-related microorganisms were detected in the plasma of COVID-19 patients, raising the possibility of simultaneously monitoring immune response regulation and microbial communities using cfRNA analysis. This study fills the knowledge gap in the plasma cfRNA landscape of COVID-19 patients and offers insight into the potential mechanisms of cfRNAs to explain COVID-19 pathogenesis.
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A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections. Sci Rep 2022; 12:889. [PMID: 35042868 PMCID: PMC8766462 DOI: 10.1038/s41598-021-04509-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 12/23/2021] [Indexed: 01/26/2023] Open
Abstract
Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.
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36
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Prashanth G, Vastrad B, Vastrad C, Kotrashetti S. Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis. Bioinform Biol Insights 2022; 15:11779322211067365. [PMID: 34992355 PMCID: PMC8725226 DOI: 10.1177/11779322211067365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/29/2021] [Indexed: 11/27/2022] Open
Abstract
Introduction: Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections
(COVID 19) is a progressive viral infection that has been investigated
extensively. However, genetic features and molecular pathogenesis underlying
remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used
bioinformatics to investigate the candidate genes associated in the
molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected
patients. Methods: Expression profiling by high-throughput sequencing dataset (GSE149273) was
downloaded from the Gene Expression Omnibus, and the differentially
expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples
and nontreated SARS-CoV-2 infection samples with an adjusted
P value of <.05 and a |log fold change| > 1.3
were first identified by limma in R software package. Next, pathway and gene
ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub
genes were identified by the NetworkAnalyzer plugin and the other
bioinformatics approaches including protein-protein interaction network
analysis, module analysis, target gene—miRNA regulatory network, and target
gene—TF regulatory network. Finally, a receiver-operating characteristic
analysis was performed for diagnostic values associated with hub genes. Results: A total of 909 DEGs were identified, including 453 upregulated genes and 457
downregulated genes. As for the pathway and GO enrichment analysis, the
upregulated genes were mainly linked with influenza A and defense response,
whereas downregulated genes were mainly linked with drug
metabolism—cytochrome P450 and reproductive process. In addition, 10 hub
genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and
APBB1) were identified. Receiver-operating characteristic analysis showed
that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2,
DUSP9, FMO5, and PDE1A) had good diagnostic values. Conclusion: This study provided insights into the molecular mechanism of
remdesivir-treated SARS-CoV-2 infection that might be useful in further
investigations.
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Affiliation(s)
- G Prashanth
- Department of General Medicine, Basaveshwara Medical College, Chitradurga, India
| | - Basavaraj Vastrad
- Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, India
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Sohail A, Iqbal AA, Sahini N, Chen F, Tantawy M, Waqas SF, Winterhoff M, Ebensen T, Schultz K, Geffers R, Schughart K, Preusse M, Shehata M, Bähre H, Pils MC, Guzman CA, Mostafa A, Pleschka S, Falk C, Michelucci A, Pessler F. Itaconate and derivatives reduce interferon responses and inflammation in influenza A virus infection. PLoS Pathog 2022; 18:e1010219. [PMID: 35025971 PMCID: PMC8846506 DOI: 10.1371/journal.ppat.1010219] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 02/15/2022] [Accepted: 12/17/2021] [Indexed: 02/06/2023] Open
Abstract
Excessive inflammation is a major cause of morbidity and mortality in many viral infections including influenza. Therefore, there is a need for therapeutic interventions that dampen and redirect inflammatory responses and, ideally, exert antiviral effects. Itaconate is an immunomodulatory metabolite which also reprograms cell metabolism and inflammatory responses when applied exogenously. We evaluated effects of endogenous itaconate and exogenous application of itaconate and its variants dimethyl- and 4-octyl-itaconate (DI, 4OI) on host responses to influenza A virus (IAV). Infection induced expression of ACOD1, the enzyme catalyzing itaconate synthesis, in monocytes and macrophages, which correlated with viral replication and was abrogated by DI and 4OI treatment. In IAV-infected mice, pulmonary inflammation and weight loss were greater in Acod1-/- than in wild-type mice, and DI treatment reduced pulmonary inflammation and mortality. The compounds reversed infection-triggered interferon responses and modulated inflammation in human cells supporting non-productive and productive infection, in peripheral blood mononuclear cells, and in human lung tissue. All three itaconates reduced ROS levels and STAT1 phosphorylation, whereas AKT phosphorylation was reduced by 4OI and DI but increased by itaconate. Single-cell RNA sequencing identified monocytes as the main target of infection and the exclusive source of ACOD1 mRNA in peripheral blood. DI treatment silenced IFN-responses predominantly in monocytes, but also in lymphocytes and natural killer cells. Ectopic synthesis of itaconate in A549 cells, which do not physiologically express ACOD1, reduced infection-driven inflammation, and DI reduced IAV- and IFNγ-induced CXCL10 expression in murine macrophages independent of the presence of endogenous ACOD1. The compounds differed greatly in their effects on cellular gene homeostasis and released cytokines/chemokines, but all three markedly reduced release of the pro-inflammatory chemokines CXCL10 (IP-10) and CCL2 (MCP-1). Viral replication did not increase under treatment despite the dramatically repressed IFN responses. In fact, 4OI strongly inhibited viral transcription in peripheral blood mononuclear cells, and the compounds reduced viral titers (4OI>Ita>DI) in A549 cells whereas viral transcription was unaffected. Taken together, these results reveal itaconates as immunomodulatory and antiviral interventions for influenza virus infection. Interferon responses are part of the primary host defenses against infections. However, excessive inflammation is often a major factor in severe disease or even death in respiratory infections such as influenza, as it can lead to acute respiratory distress syndrome and sepsis-like multiorgan involvement. We applied itaconate and chemically modified versions of it (which enter cells more efficiently and can be applied at lower doses) to influenza A virus-infected human cells and lung tissue and found that these compounds markedly repress interferon responses and some pro-inflammatory processes without increasing viral replication. In fact, 4-octyl itaconate greatly decreased viral RNA replication in peripheral blood, and itaconate and 4-octyl itaconate reduced production of infectious virus in a human lung cell line. By analyzing gene expression patterns of single mononuclear cells in peripheral blood, we found that the virus infects predominantly monocytes and that these cells are the only source of ACOD1, the enzyme that synthesizes itaconate in humans. In a mouse model of influenza A virus infection, dimethyl-itaconate prevented lung inflammation and improved survival. Thus, our results suggest that novel medications based on itaconate promise to be effective treatments for influenza because they reduce deleterious inflammation and potentially also limit viral spread in the patient.
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Affiliation(s)
- Aaqib Sohail
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Azeem A. Iqbal
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Nishika Sahini
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Fangfang Chen
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Mohamed Tantawy
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
- Hormones Department, Medical Research and Clinical Studies Institute, National Research Center, Dokki, Giza, Egypt
- Stem Cells Lab, Center of Excellence for Advanced Sciences, National Research Center, Dokki, Giza, Egypt
| | - Syed F.H. Waqas
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Moritz Winterhoff
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
| | - Thomas Ebensen
- Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Kristin Schultz
- Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Robert Geffers
- Genome Analytics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Klaus Schughart
- Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
- University of Veterinary Medicine Hannover, Hannover, Germany
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Matthias Preusse
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Mahmoud Shehata
- Institute for Medical Virology, Justus-Liebig-University, Giessen, Germany
- Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza, Egypt
| | - Heike Bähre
- Research Core Unit Metabolomics, Hannover Medical School, Hannover, Germany
| | - Marina C. Pils
- Mouse Pathology Platform, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Carlos A. Guzman
- Vaccinology and Applied Microbiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Ahmed Mostafa
- Institute for Medical Virology, Justus-Liebig-University, Giessen, Germany
- Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza, Egypt
| | - Stephan Pleschka
- Institute for Medical Virology, Justus-Liebig-University, Giessen, Germany
- German Center for Infection Research (DZIF) partner site Giessen, Germany
| | - Christine Falk
- Department of Transplantation Immunology, Hannover Medical School, Hannover, Germany
| | - Alessandro Michelucci
- Neuro-Immunology Group, Department of Cancer Research, Luxembourg Institute of Health (LIH), Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Pessler
- Biomarkers for Infectious Diseases, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE Centre for Experimental and Clinical Infection Research, Hannover, Germany
- Centre for Individualised Infection Medicine, Hannover, Germany
- * E-mail: , frank.pesslerwincore.de
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38
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Mucaki EJ, Shirley BC, Rogan PK. Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures. Int J Radiat Biol 2021; 98:924-941. [PMID: 34699300 DOI: 10.1080/09553002.2021.1998709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
PURPOSE Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning (ML) based signatures (with 8-20% misclassification rates). These signatures can quantify therapeutically relevant as well as accidental radiation exposures. The prodromal symptoms of acute radiation syndrome (ARS) overlap those present in influenza and dengue fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy. METHODS This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-neoplastic blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to ML-based classifiers to predict radiation exposure in other hematological conditions. RESULTS Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8-54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thrombosis, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death or extrusion of DNA fragments. Riboviral infections (e.g. influenza or dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions. CONCLUSIONS This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.
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Affiliation(s)
- Eliseos J Mucaki
- Department of Biochemistry, University of Western Ontario, London, Canada
| | | | - Peter K Rogan
- Department of Biochemistry, University of Western Ontario, London, Canada.,CytoGnomix Inc., London, Canada
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39
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Tsalik EL, Fiorino C, Aqeel A, Liu Y, Henao R, Ko ER, Burke TW, Reller ME, Bodinayake CK, Nagahawatte A, Arachchi WK, Devasiri V, Kurukulasooriya R, McClain MT, Woods CW, Ginsburg GS, Tillekeratne LG, Schughart K. The Host Response to Viral Infections Reveals Common and Virus-Specific Signatures in the Peripheral Blood. Front Immunol 2021; 12:741837. [PMID: 34777354 PMCID: PMC8578928 DOI: 10.3389/fimmu.2021.741837] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Viruses cause a wide spectrum of clinical disease, the majority being acute respiratory infections (ARI). In most cases, ARI symptoms are similar for different viruses although severity can be variable. The objective of this study was to understand the shared and unique elements of the host transcriptional response to different viral pathogens. We identified 162 subjects in the US and Sri Lanka with infections due to influenza, enterovirus/rhinovirus, human metapneumovirus, dengue virus, cytomegalovirus, Epstein Barr Virus, or adenovirus. Our dataset allowed us to identify common pathways at the molecular level as well as virus-specific differences in the host immune response. Conserved elements of the host response to these viral infections highlighted the importance of interferon pathway activation. However, the magnitude of the responses varied between pathogens. We also identified virus-specific responses to influenza, enterovirus/rhinovirus, and dengue infections. Influenza-specific differentially expressed genes (DEG) revealed up-regulation of pathways related to viral defense and down-regulation of pathways related to T cell and neutrophil responses. Functional analysis of entero/rhinovirus-specific DEGs revealed up-regulation of pathways for neutrophil activation, negative regulation of immune response, and p38MAPK cascade and down-regulation of virus defenses and complement activation. Functional analysis of dengue-specific up-regulated DEGs showed enrichment of pathways for DNA replication and cell division whereas down-regulated DEGs were mainly associated with erythrocyte and myeloid cell homeostasis, reactive oxygen and peroxide metabolic processes. In conclusion, our study will contribute to a better understanding of molecular mechanisms to viral infections in humans and the identification of biomarkers to distinguish different types of viral infections.
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Affiliation(s)
- Ephraim L. Tsalik
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Emergency Department Service, Durham Veterans Affairs Health Care System, Durham, NC, United States
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Cassandra Fiorino
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Ammara Aqeel
- Duke Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, United States
| | - Yiling Liu
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Ricardo Henao
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
| | - Emily R. Ko
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Medicine, Duke Regional Hospital, Durham, NC, United States
| | - Thomas W. Burke
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Megan E. Reller
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | | | | | | | | | | | - Micah T. McClain
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Medical Service, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Christopher W. Woods
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Medical Service, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Geoffrey S. Ginsburg
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
| | - L. Gayani Tillekeratne
- Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, United States
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Medical Service, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
- University of Veterinary Medicine Hannover, Hannover, Germany
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, United States
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40
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Kulasinghe A, Tan CW, Dos Santos Miggiolaro AFR, Monkman J, SadeghiRad H, Bhuva DD, da Silva Motta Junior J, Vaz de Paula CB, Nagashima S, Baena CP, Souza-Fonseca-Guimaraes P, de Noronha L, McCulloch T, Rodrigues Rossi G, Cooper C, Tang B, Short KR, Davis MJ, Souza-Fonseca-Guimaraes F, Belz GT, O'Byrne K. Profiling of lung SARS-CoV-2 and influenza virus infection dissects virus-specific host responses and gene signatures. Eur Respir J 2021; 59:13993003.01881-2021. [PMID: 34675048 PMCID: PMC8542865 DOI: 10.1183/13993003.01881-2021] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 10/07/2021] [Indexed: 01/08/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19). A better definition of the pulmonary host response to SARS-CoV-2 infection is required to understand viral pathogenesis and to validate putative COVID-19 biomarkers that have been proposed in clinical studies. Here, we use targeted transcriptomics of FFPE tissue using the Nanostring GeoMX™ platform to generate an in-depth picture of the pulmonary transcriptional landscape of COVID-19, pandemic H1N1 influenza and uninfected control patients. Host transcriptomics showed a significant upregulation of genes associated with inflammation, type I interferon production, coagulation and angiogenesis in the lungs of COVID-19 patients compared to non-infected controls. SARS-CoV-2 was non-uniformly distributed in lungs (emphasising the advantages of spatial transcriptomics) with the areas of high viral load associated with an increased type I interferon response. Once the dominant cell type present in the sample, within patient correlations and patient-patient variation had been controlled for, only a very limited number of genes were differentially expressed between the lungs of fatal influenza and COVID-19 patients. Strikingly, the interferon-associated gene IFI27, previously identified as a useful blood biomarker to differentiate bacterial and viral lung infections, was significantly upregulated in the lungs of COVID-19 patients compared to patients with influenza. Collectively, these data demonstrate that spatial transcriptomics is a powerful tool to identify novel gene signatures within tissues, offering new insights into the pathogenesis of SARS-COV-2 to aid in patient triage and treatment.
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Affiliation(s)
- Arutha Kulasinghe
- Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Queensland, Australia .,Translational Research Institute, Brisbane, Queensland, Australia.,University of Queensland Diamantina Institute, University of Queensland, Woollongabba, Queensland, Australia.,co-first authors
| | - Chin Wee Tan
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia.,co-first authors
| | - Anna Flavia Ribeiro Dos Santos Miggiolaro
- Postgraduate Program of Health Sciences - School of Medicine - Hospital Marcelino Champagnat - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, PR, Brazil.,co-first authors
| | - James Monkman
- Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Queensland, Australia.,Translational Research Institute, Brisbane, Queensland, Australia
| | - Habib SadeghiRad
- Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Queensland, Australia.,Translational Research Institute, Brisbane, Queensland, Australia
| | - Dharmesh D Bhuva
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Jarbas da Silva Motta Junior
- Postgraduate Program of Health Sciences - School of Medicine - Hospital Marcelino Champagnat - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, PR, Brazil
| | - Caroline Busatta Vaz de Paula
- Postgraduate Program of Health Sciences - School of Medicine - Hospital Marcelino Champagnat - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, PR, Brazil
| | - Seigo Nagashima
- Postgraduate Program of Health Sciences - School of Medicine - Hospital Marcelino Champagnat - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, PR, Brazil
| | - Cristina Pellegrino Baena
- School of Medicine & Center of Education, Research and Innovation - Hospital Marcelino Champagnat - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Brazil
| | - Paulo Souza-Fonseca-Guimaraes
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Lucia de Noronha
- Laboratory of Experimental Pathology - School of Medicine - Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba, Brazil
| | - Timothy McCulloch
- Translational Research Institute, Brisbane, Queensland, Australia.,University of Queensland Diamantina Institute, University of Queensland, Woollongabba, Queensland, Australia
| | - Gustavo Rodrigues Rossi
- Translational Research Institute, Brisbane, Queensland, Australia.,University of Queensland Diamantina Institute, University of Queensland, Woollongabba, Queensland, Australia
| | - Caroline Cooper
- Pathology Queensland, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.,University of Queensland, Faculty of Medicine, Woolloongabba, Queensland, Australia
| | - Benjamin Tang
- Centre for Immunology and Allergy Research, the Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Kirsty R Short
- The University of Queensland, School of Chemistry and Molecular Biosciences, St Lucia, Brisbane, Queensland, Australia.,Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, Queensland, Australia.,co-senior authors
| | - Melissa J Davis
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia.,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, VIC, Australia.,co-senior authors
| | - Fernando Souza-Fonseca-Guimaraes
- Translational Research Institute, Brisbane, Queensland, Australia.,University of Queensland Diamantina Institute, University of Queensland, Woollongabba, Queensland, Australia.,co-senior authors
| | - Gabrielle T Belz
- Translational Research Institute, Brisbane, Queensland, Australia.,University of Queensland Diamantina Institute, University of Queensland, Woollongabba, Queensland, Australia.,The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria, Australia.,Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, Queensland, Australia.,co-senior authors
| | - Ken O'Byrne
- Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Queensland, Australia.,Translational Research Institute, Brisbane, Queensland, Australia.,co-senior authors
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41
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Krasnov A, Johansen LH, Karlsen C, Sveen L, Ytteborg E, Timmerhaus G, Lazado CC, Afanasyev S. Transcriptome Responses of Atlantic Salmon ( Salmo salar L.) to Viral and Bacterial Pathogens, Inflammation, and Stress. Front Immunol 2021; 12:705601. [PMID: 34621264 PMCID: PMC8490804 DOI: 10.3389/fimmu.2021.705601] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/03/2021] [Indexed: 11/15/2022] Open
Abstract
Transcriptomics provides valuable data for functional annotations of genes, the discovery of biomarkers, and quantitative assessment of responses to challenges. Meta-analysis of Nofima’s Atlantic salmon microarray database was performed for the selection of genes that have shown strong and reproducible expression changes. Using data from 127 experiments including 6440 microarrays, four transcription modules (TM) were identified with a total of 902 annotated genes: 161 virus responsive genes – VRG (activated with five viruses and poly I:C), genes that responded to three pathogenic bacteria (523 up and 33 down-regulated genes), inflammation not caused by infections – wounds, melanized foci in skeletal muscle and exposure to PAMP (180 up and 72 down-regulated genes), and stress by exercise, crowding and cortisol implants (33 genes). To assist the selection of gene markers, genes in each TM were ranked according to the scale of expression changes. In terms of functional annotations, association with diseases and stress was unknown or not reflected in public databases for a large part of genes, including several genes with the highest ranks. A set of multifunctional genes was discovered. Cholesterol 25-hydroxylase was present in all TM and 22 genes, including most differentially expressed matrix metalloproteinases 9 and 13 were assigned to three TMs. The meta-analysis has improved understanding of the defense strategies in Atlantic salmon. VRG have demonstrated equal or similar responses to RNA (SAV, IPNV, PRV, and ISAV), and DNA (gill pox) viruses, injection of bacterial DNA (plasmid) and exposure of cells to PAMP (CpG and gardiquimod) and relatively low sensitivity to inflammation and bacteria. Genes of the highest rank show preferential expression in erythrocytes. This group includes multigene families (gig and several trim families) and many paralogs. Of pathogen recognition receptors, only RNA helicases have shown strong expression changes. Most VRG (82%) are effectors with a preponderance of ubiquitin-related genes, GTPases, and genes of nucleotide metabolism. Many VRG have unknown roles. The identification of TMs makes possible quantification of responses and assessment of their interactions. Based on this, we are able to separate pathogen-specific responses from general inflammation and stress.
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Affiliation(s)
| | | | | | - Lene Sveen
- Fish Health Department, Nofima AS, Ås, Norway
| | | | | | | | - Sergey Afanasyev
- Laboratory of Neurophysiology and Behavioral Pathology, I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Saint-Petersburg, Russia
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Gupta RK, Rosenheim J, Bell LC, Chandran A, Guerra-Assuncao JA, Pollara G, Whelan M, Artico J, Joy G, Kurdi H, Altmann DM, Boyton RJ, Maini MK, McKnight A, Lambourne J, Cutino-Moguel T, Manisty C, Treibel TA, Moon JC, Chain BM, Noursadeghi M. Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study. THE LANCET. MICROBE 2021; 2:e508-e517. [PMID: 34250515 PMCID: PMC8260104 DOI: 10.1016/s2666-5247(21)00146-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing. METHODS We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for "viral infection", "transcriptome", "biomarker", and "blood". We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity. FINDINGS We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27-47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91-0·99), sensitivity 0·84 (0·70-0·93), and specificity 0·95 (0·85-0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91-0·95). INTERPRETATION Our findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge. FUNDING Barts Charity, Wellcome Trust, and National Institute of Health Research.
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Affiliation(s)
- Rishi K Gupta
- Institute of Global Health, University College London, London, UK
- Division of Infection and Immunity, University College London, London, UK
| | - Joshua Rosenheim
- Division of Infection and Immunity, University College London, London, UK
| | - Lucy C Bell
- Division of Infection and Immunity, University College London, London, UK
| | - Aneesh Chandran
- Division of Infection and Immunity, University College London, London, UK
| | | | - Gabriele Pollara
- Division of Infection and Immunity, University College London, London, UK
| | - Matthew Whelan
- Division of Infection and Immunity, University College London, London, UK
| | - Jessica Artico
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - George Joy
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Hibba Kurdi
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Daniel M Altmann
- Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Rosemary J Boyton
- Lung Division, Royal Brompton & Harefield NHS Foundation Trust, London, UK
| | - Mala K Maini
- Division of Infection and Immunity, University College London, London, UK
| | - Aine McKnight
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Jonathan Lambourne
- Department of Infection, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Teresa Cutino-Moguel
- Department of Virology, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Charlotte Manisty
- Institute of Cardiovascular Sciences, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Thomas A Treibel
- Institute of Cardiovascular Sciences, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - James C Moon
- Institute of Cardiovascular Sciences, University College London, London, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Benjamin M Chain
- Division of Infection and Immunity, University College London, London, UK
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, UK
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Croft DP, Burton DS, Nagel DJ, Bhattacharya S, Falsey AR, Georas SN, Hopke PK, Johnston CJ, Kottmann RM, Litonjua AA, Mariani TJ, Rich DQ, Thevenet-Morrison K, Thurston SW, Utell MJ, McCall MN. The effect of air pollution on the transcriptomics of the immune response to respiratory infection. Sci Rep 2021; 11:19436. [PMID: 34593881 PMCID: PMC8484285 DOI: 10.1038/s41598-021-98729-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/13/2021] [Indexed: 12/24/2022] Open
Abstract
Combustion related particulate matter air pollution (PM) is associated with an increased risk of respiratory infections in adults. The exact mechanism underlying this association has not been determined. We hypothesized that increased concentrations of combustion related PM would result in dysregulation of the innate immune system. This epidemiological study includes 111 adult patients hospitalized with respiratory infections who underwent transcriptional analysis of their peripheral blood. We examined the association between gene expression at the time of hospitalization and ambient measurements of particulate air pollutants in the 28 days prior to hospitalization. For each pollutant and time lag, gene-specific linear models adjusting for infection type were fit using LIMMA (Linear Models For Microarray Data), and pathway/gene set analyses were performed using the CAMERA (Correlation Adjusted Mean Rank) program. Comparing patients with viral and/or bacterial infection, the expression patterns associated with air pollution exposure differed. Adjusting for the type of infection, increased concentrations of Delta-C (a marker of biomass smoke) and other PM were associated with upregulation of iron homeostasis and protein folding. Increased concentrations of black carbon (BC) were associated with upregulation of viral related gene pathways and downregulation of pathways related to antigen presentation. The pollutant/pathway associations differed by lag time and by type of infection. This study suggests that the effect of air pollution on the pathogenesis of respiratory infection may be pollutant, timing, and infection specific.
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Affiliation(s)
- Daniel P Croft
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA.
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA.
| | - David S Burton
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - David J Nagel
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
| | - Soumyaroop Bhattacharya
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Ann R Falsey
- Department of Medicine, Infectious Diseases Division, University of Rochester Medical Center, Rochester, NY, USA
| | - Steve N Georas
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
| | - Philip K Hopke
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
- Institute for a Sustainable Environment, and Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA
| | - Carl J Johnston
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - R Matthew Kottmann
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
| | - Augusto A Litonjua
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
| | - Thomas J Mariani
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biomedical Genetics, University of Rochester Medical Center, Rochester, NY, USA
| | - David Q Rich
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Kelly Thevenet-Morrison
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Sally W Thurston
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Mark J Utell
- Department of Medicine, Pulmonary and Critical Care Medicine Division, University of Rochester Medical Center, 601 Elmwood Avenue Box 692, Rochester, NY, 14642, USA
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
| | - Matthew N McCall
- Environmental Health Science Center, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
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Jethwani P, Shirali AC. Gene Expression Profiling in Kidney Transplant Recipients on Immune Checkpoint Inhibitors: More than Meets the Eye. Clin J Am Soc Nephrol 2021; 16:1315-1317. [PMID: 34497109 PMCID: PMC8729569 DOI: 10.2215/cjn.09860721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Priyanka Jethwani
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut.,Division of Transplantation Surgery and Immunology, Yale University School of Medicine, New Haven, Connecticut
| | - Anushree C Shirali
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
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He YD, Wohlford EM, Uhle F, Buturovic L, Liesenfeld O, Sweeney TE. The Optimization and Biological Significance of a 29-Host-Immune-mRNA Panel for the Diagnosis of Acute Infections and Sepsis. J Pers Med 2021; 11:735. [PMID: 34442377 PMCID: PMC8402342 DOI: 10.3390/jpm11080735] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022] Open
Abstract
In response to the unmet need for timely accurate diagnosis and prognosis of acute infections and sepsis, host-immune-response-based tests are being developed to help clinicians make more informed decisions including prescribing antimicrobials, ordering additional diagnostics, and assigning level of care. One such test (InSep™, Inflammatix, Inc.) uses a 29-mRNA panel to determine the likelihood of bacterial infection, the separate likelihood of viral infection, and the risk of physiologic decompensation (severity of illness). The test, being implemented in a rapid point-of-care platform with a turnaround time of 30 min, enables accurate and rapid diagnostic use at the point of impact. In this report, we provide details on how the 29-biomarker signature was chosen and optimized, together with its molecular, immunological, and medical significance to better understand the pathophysiological relevance of altered gene expression in disease. We synthesize key results obtained from gene-level functional annotations, geneset-level enrichment analysis, pathway-level analysis, and gene-network-level upstream regulator analysis. Emerging findings are summarized as hallmarks on immune cell interaction, inflammatory mediators, cellular metabolism and homeostasis, immune receptors, intracellular signaling and antiviral response; and converging themes on neutrophil degranulation and activation involved in immune response, interferon, and other signaling pathways.
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Affiliation(s)
| | | | | | | | | | - Timothy E. Sweeney
- Inflammatix, Inc., 863 Mitten Rd, Suite 104, Burlingame, CA 94010, USA; (Y.D.H.); (E.M.W.); (F.U.); (L.B.); (O.L.)
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Prokop JW, Hartog NL, Chesla D, Faber W, Love CP, Karam R, Abualkheir N, Feldmann B, Teng L, McBride T, Leimanis ML, English BK, Holsworth A, Frisch A, Bauss J, Kalpage N, Derbedrossian A, Pinti RM, Hale N, Mills J, Eby A, VanSickle EA, Pageau SC, Shankar R, Chen B, Carcillo JA, Sanfilippo D, Olivero R, Bupp CP, Rajasekaran S. High-Density Blood Transcriptomics Reveals Precision Immune Signatures of SARS-CoV-2 Infection in Hospitalized Individuals. Front Immunol 2021; 12:694243. [PMID: 34335605 PMCID: PMC8322982 DOI: 10.3389/fimmu.2021.694243] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/30/2021] [Indexed: 12/27/2022] Open
Abstract
The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient's clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.
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Affiliation(s)
- Jeremy W. Prokop
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, United States
| | - Nicholas L. Hartog
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Allergy & Immunology, Spectrum Health, Grand Rapids, MI, United States
| | - Dave Chesla
- Office of Research, Spectrum Health, Grand Rapids, MI, United States
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - William Faber
- Physical Sciences, Grand Rapids Community College, Grand Rapids, MI, United States
| | - Chanise P. Love
- Office of Research, Spectrum Health, Grand Rapids, MI, United States
| | | | | | | | - Li Teng
- Ambry Genetics, Aliso Viejo, CA, United States
| | | | - Mara L. Leimanis
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital, Grand Rapids, MI, United States
| | - B. Keith English
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Amanda Holsworth
- Allergy & Immunology, Spectrum Health, Grand Rapids, MI, United States
| | - Austin Frisch
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Jacob Bauss
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Nathisha Kalpage
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Aram Derbedrossian
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Ryan M. Pinti
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Nicole Hale
- The Department of Chemistry and Biochemistry, Calvin University, Grand Rapids, MI, United States
| | - Joshua Mills
- Department of Biology, Grand Valley State University, Allendale, MI, United States
| | - Alexandra Eby
- Department of Science, Davenport University, Grand Rapids, MI, United States
| | | | - Spencer C. Pageau
- Office of Research, Spectrum Health, Grand Rapids, MI, United States
| | - Rama Shankar
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, United States
| | - Bin Chen
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, United States
| | - Joseph A. Carcillo
- Department of Critical Care Medicine and Pediatrics, Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Dominic Sanfilippo
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital, Grand Rapids, MI, United States
| | - Rosemary Olivero
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Infectious Disease, Helen DeVos Children’s Hospital, Grand Rapids, MI, United States
| | - Caleb P. Bupp
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Medical Genetics, Spectrum Health Medical Genetics, Grand Rapids, MI, United States
| | - Surender Rajasekaran
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
- Office of Research, Spectrum Health, Grand Rapids, MI, United States
- Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital, Grand Rapids, MI, United States
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Dean MJ, Ochoa JB, Sanchez-Pino MD, Zabaleta J, Garai J, Del Valle L, Wyczechowska D, Baiamonte LB, Philbrook P, Majumder R, Vander Heide RS, Dunkenberger L, Thylur RP, Nossaman B, Roberts WM, Chapple AG, Wu J, Hicks C, Collins J, Luke B, Johnson R, Koul HK, Rees CA, Morris CR, Garcia-Diaz J, Ochoa AC. Severe COVID-19 Is Characterized by an Impaired Type I Interferon Response and Elevated Levels of Arginase Producing Granulocytic Myeloid Derived Suppressor Cells. Front Immunol 2021; 12:695972. [PMID: 34341659 PMCID: PMC8324422 DOI: 10.3389/fimmu.2021.695972] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023] Open
Abstract
COVID-19 ranges from asymptomatic in 35% of cases to severe in 20% of patients. Differences in the type and degree of inflammation appear to determine the severity of the disease. Recent reports show an increase in circulating monocytic-myeloid-derived suppressor cells (M-MDSC) in severe COVID 19 that deplete arginine but are not associated with respiratory complications. Our data shows that differences in the type, function and transcriptome of granulocytic-MDSC (G-MDSC) may in part explain the severity COVID-19, in particular the association with pulmonary complications. Large infiltrates by Arginase 1+ G-MDSC (Arg+G-MDSC), expressing NOX-1 and NOX-2 (important for production of reactive oxygen species) were found in the lungs of patients who died from COVID-19 complications. Increased circulating Arg+G-MDSC depleted arginine, which impaired T cell receptor and endothelial cell function. Transcriptomic signatures of G-MDSC from patients with different stages of COVID-19, revealed that asymptomatic patients had increased expression of pathways and genes associated with type I interferon (IFN), while patients with severe COVID-19 had increased expression of genes associated with arginase production, and granulocyte degranulation and function. These results suggest that asymptomatic patients develop a protective type I IFN response, while patients with severe COVID-19 have an increased inflammatory response that depletes arginine, impairs T cell and endothelial cell function, and causes extensive pulmonary damage. Therefore, inhibition of arginase-1 and/or replenishment of arginine may be important in preventing/treating severe COVID-19.
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Affiliation(s)
- Matthew J. Dean
- Louisiana State University Cancer Center, New Orleans, LA, United States
| | - Juan B. Ochoa
- Department of Surgery, Ochsner Medical Center, New Orleans, LA, United States
| | - Maria Dulfary Sanchez-Pino
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Genetics, LSU Health, New Orleans, LA, United States
| | - Jovanny Zabaleta
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Pediatrics, LSU Health, New Orleans, LA, United States
| | - Jone Garai
- Louisiana State University Cancer Center, New Orleans, LA, United States
| | - Luis Del Valle
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Pathology LSU Health, New Orleans, LA, United States
| | | | | | - Phaethon Philbrook
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Genetics, LSU Health, New Orleans, LA, United States
| | - Rinku Majumder
- Department of Biochemistry, LSU Health, New Orleans, LA, United States
| | | | - Logan Dunkenberger
- Louisiana State University Cancer Center, New Orleans, LA, United States
| | | | - Bobby Nossaman
- Department of Surgery, Ochsner Medical Center, New Orleans, LA, United States
| | - W. Mark Roberts
- Department of Internal Medicine, Ochsner Medical Center, New Orleans, LA, United States
| | - Andrew G. Chapple
- Louisiana State University Cancer Center, New Orleans, LA, United States
- School of Public Health, LSU Health, New Orleans, LA, United States
| | - Jiande Wu
- Department of Genetics, LSU Health, New Orleans, LA, United States
| | - Chindo Hicks
- Department of Genetics, LSU Health, New Orleans, LA, United States
| | - Jack Collins
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Brian Luke
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Randall Johnson
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Hari K. Koul
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Biochemistry, LSU Health, New Orleans, LA, United States
| | - Chris A. Rees
- Division of Emergency Medicine, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Claudia R. Morris
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, Children’s Healthcare of Atlanta, Atlanta, GA, United States
| | - Julia Garcia-Diaz
- Tissue Biorepository, Ochsner Medical Center, New Orleans, LA, United States
| | - Augusto C. Ochoa
- Louisiana State University Cancer Center, New Orleans, LA, United States
- Department of Pediatrics, LSU Health, New Orleans, LA, United States
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Declerck K, Novo CP, Grielens L, Van Camp G, Suter A, Vanden Berghe W. Echinacea purpurea (L.) Moench treatment of monocytes promotes tonic interferon signaling, increased innate immunity gene expression and DNA repeat hypermethylated silencing of endogenous retroviral sequences. BMC Complement Med Ther 2021; 21:141. [PMID: 33980308 PMCID: PMC8114977 DOI: 10.1186/s12906-021-03310-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 04/27/2021] [Indexed: 12/12/2022] Open
Abstract
Background Herbal remedies of Echinacea purpurea tinctures are widely used today to reduce common cold respiratory tract infections. Methods Transcriptome, epigenome and kinome profiling allowed a systems biology level characterisation of genomewide immunomodulatory effects of a standardized Echinacea purpurea (L.) Moench extract in THP1 monocytes. Results Gene expression and DNA methylation analysis revealed that Echinaforce® treatment triggers antiviral innate immunity pathways, involving tonic IFN signaling, activation of pattern recognition receptors, chemotaxis and immunometabolism. Furthermore, phosphopeptide based kinome activity profiling and pharmacological inhibitor experiments with filgotinib confirm a key role for Janus Kinase (JAK)-1 dependent gene expression changes in innate immune signaling. Finally, Echinaforce® treatment induces DNA hypermethylation at intergenic CpG, long/short interspersed nuclear DNA repeat elements (LINE, SINE) or long termininal DNA repeats (LTR). This changes transcription of flanking endogenous retroviral sequences (HERVs), involved in an evolutionary conserved (epi) genomic protective response against viral infections. Conclusions Altogether, our results suggest that Echinaforce® phytochemicals strengthen antiviral innate immunity through tonic IFN regulation of pattern recognition and chemokine gene expression and DNA repeat hypermethylated silencing of HERVs in monocytes. These results suggest that immunomodulation by Echinaforce® treatment holds promise to reduce symptoms and duration of infection episodes of common cold corona viruses (CoV), Severe Acute Respiratory Syndrome (SARS)-CoV, and new occurring strains such as SARS-CoV-2, with strongly impaired interferon (IFN) response and weak innate antiviral defense. Supplementary Information The online version contains supplementary material available at 10.1186/s12906-021-03310-5.
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Affiliation(s)
- Ken Declerck
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Antwerp, Belgium
| | - Claudina Perez Novo
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Antwerp, Belgium
| | - Lisa Grielens
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Antwerp, Belgium
| | - Guy Van Camp
- Center of Medical Genetics, Department of Biomedical Sciences, University of Antwerp (UA) and University Hospital Antwerp (UZA), Antwerp, Belgium
| | | | - Wim Vanden Berghe
- Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp (UA), Antwerp, Belgium.
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Ravichandran S, Banerjee U, Dr GD, Kandukuru R, Thakur C, Chakravortty D, Balaji KN, Singh A, Chandra N. VB 10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections. EBioMedicine 2021; 67:103352. [PMID: 33906069 PMCID: PMC8099739 DOI: 10.1016/j.ebiom.2021.103352] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/04/2021] [Accepted: 04/07/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics. METHODS We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort. FINDING We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples. INTERPRETATION Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts. FUNDING Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.
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Affiliation(s)
| | - Ushashi Banerjee
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Gayathri Devi Dr
- Department of Microbiology, M S Ramaiah Medical College, Bangalore 560054, Karnataka, India
| | - Rooparani Kandukuru
- Department of Microbiology, M S Ramaiah Medical College, Bangalore 560054, Karnataka, India
| | - Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Dipshikha Chakravortty
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore 560012, India
| | | | - Amit Singh
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore 560012, India; Centre for Infectious Disease Research, Indian Institute of Science, Bangalore 560012, India
| | - Nagasuma Chandra
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, India; Department of Biochemistry, Indian Institute of Science, Bangalore 560012, India; Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.
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50
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Gupta R, Leimanis ML, Adams M, Bachmann AS, Uhl KL, Bupp CP, Hartog NL, Kort EJ, Olivero R, Comstock SS, Sanfilippo DJ, Lunt SY, Prokop JW, Rajasekaran S. Balancing precision versus cohort transcriptomic analysis of acute and recovery phase of viral bronchiolitis. Am J Physiol Lung Cell Mol Physiol 2021; 320:L1147-L1157. [PMID: 33851876 DOI: 10.1152/ajplung.00440.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Viral infections affecting the lower respiratory tract place enormous burdens on hospitals. As neither vaccines nor antiviral agents exist for many viruses, understanding risk factors and outcomes in each patient using minimally invasive analysis, such as blood, can lead to improved health care delivery. A cohort of PAXgene RNA sequencing of infants admitted with moderate or severe acute bronchiolitis and respiratory syncytial virus were compared with case-control statistical analysis and cohort-based outlier mapping for precision transcriptomics. Patients with severe bronchiolitis had signatures connected to the immune system, interferon signaling, and cytokine signaling, with marked sex differences in XIST, RPS4Y1, KDM5D, and LINC00278 for severity. Several patients had unique secondary infections, cytokine activation, immune responses, biological pathways, and immune cell activation, highlighting the need for defining patient-level transcriptomic signatures. Balancing relative contributions of cohort-based biomarker discoveries with patient's biological responses is needed to understand the totality of mechanisms of adverse outcomes in viral bronchiolitis.
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Affiliation(s)
- Ruchir Gupta
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Mara L Leimanis
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Pediatric Intensive Care Unit, Helen DeVos Children's Hospital, Grand Rapids, Michigan
| | - Marie Adams
- Genomics Core Facility, Van Andel Institute, Grand Rapids, Michigan
| | - André S Bachmann
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan
| | - Katie L Uhl
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Caleb P Bupp
- Spectrum Health Medical Genetics, Grand Rapids, Michigan
| | | | - Eric J Kort
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,DeVos Cardiovascular Research Program, Spectrum Health and Van Andel Institute, Grand Rapids, Michigan
| | - Rosemary Olivero
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Infectious Disease, Helen DeVos Children's Hospital, Grand Rapids, Michigan
| | - Sarah S Comstock
- Department of Food Science and Human Nutrition, Michigan State University, East Lansing, Michigan
| | - Dominic J Sanfilippo
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Pediatric Intensive Care Unit, Helen DeVos Children's Hospital, Grand Rapids, Michigan
| | - Sophia Y Lunt
- Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan.,Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan
| | - Jeremy W Prokop
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Surender Rajasekaran
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan.,Pediatric Intensive Care Unit, Helen DeVos Children's Hospital, Grand Rapids, Michigan.,Office of Research, Spectrum Health, Grand Rapids, Michigan
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