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Li Z, Peng M, Cheng L, Wang Z, Wu Z, Feng F, Feng X, Wang S, Guo Y, Li Y. Identification of aberrant interferon-stimulated gene associated host responses potentially linked to poor prognosis in COVID-19 during the Omicron wave. Virol J 2025; 22:89. [PMID: 40155905 PMCID: PMC11954226 DOI: 10.1186/s12985-025-02696-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Accepted: 03/06/2025] [Indexed: 04/01/2025] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron has demonstrated decreased pathogenicity, yet a few individuals suffer severe pneumonia from coronavirus disease 2019 (COVID-19) infection; the underlying mechanisms are unknown. METHODS The present work investigated the role of Interferon-stimulated genes (ISGs) in the occurrence and progression of severe Omicron infection. The expression and dynamic changes of ISGs were assessed using quantitative real-time polymerase chain reaction (qRT-PCR), and the anti-ISG15 autoantibody in infected patients was detected by ELISA. Moreover, we evaluated the correlation of ISGs with disease severity and outcomes by comparing expression of ISGs among each group. RESULTS Decreased expression of several ISGs such as IFI6 are potentially linked to increased severity or poor outcomes of Omicron infection. Longitudinal data also demonstrates that the dynamic variation of IFI6 in the Omicron infection phase may be linked to the prognosis of the disease. The increase of anti-ISG15 autoantibody potentially links to the disease progression and poor outcome of patients with high level of ISG15 expression. CONCLUSIONS These findings define aberrant Interferon-stimulated gene associated host responses and reveal potential mechanisms and therapeutic targets for Omicron or other viral infection.
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Affiliation(s)
- Zhan Li
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Min Peng
- Division of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Linlin Cheng
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - ZiRan Wang
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Ziyan Wu
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Futai Feng
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xinxin Feng
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Siyu Wang
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Ye Guo
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| | - Yongzhe Li
- Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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2
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Chowdhury O, Bammidi S, Gautam P, Babu VS, Liu H, Shang P, Xin Y, Mahally E, Nemani M, Koontz V, Lathrop K, Kedziora KM, Franks J, Sun M, Smith JW, DeVine LR, Cole RN, Stepicheva N, Strizhakova A, Chattopadhyay S, Hose S, Zigler JS, Sahel JA, Qian J, Guha P, Handa JT, Ghosh S, Sinha D. Activated mTOR Signaling in the RPE Drives EMT, Autophagy, and Metabolic Disruption, Resulting in AMD-Like Pathology in Mice. Aging Cell 2025:e70018. [PMID: 39957408 DOI: 10.1111/acel.70018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Revised: 02/02/2025] [Accepted: 02/06/2025] [Indexed: 02/18/2025] Open
Abstract
The mechanistic target of rapamycin (mTOR) complexes 1 and 2 (mTORC1/2) are crucial for various physiological functions. Although the role of mTORC1 in retinal pigmented epithelium (RPE) homeostasis and age-related macular degeneration (AMD) pathogenesis is established, the function of mTORC2 remains unclear. We investigated both complexes in RPE health and disease. Therefore, in this study, we have attempted to demonstrate that the specific overexpression of mammalian lethal with Sec13 protein 8 (mLST8) in the mouse RPE activates both mTORC1 and mTORC2, inducing epithelial-mesenchymal transition (EMT)-like changes and subretinal/RPE deposits resembling early AMD-like pathogenesis. Aging in these mice leads to RPE degeneration, causing retinal damage, impaired debris clearance, and metabolic and mitochondrial dysfunction. Inhibition of mTOR with TORIN1 in vitro or βA3/A1-crystallin in vivo normalized mTORC1/2 activity and restored function, revealing a novel role for the mTOR complexes in regulating RPE function, impacting retinal health and disease.
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Affiliation(s)
- Olivia Chowdhury
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Sridhar Bammidi
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Pooja Gautam
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Vishnu Suresh Babu
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Haitao Liu
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Peng Shang
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ying Xin
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Emma Mahally
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Mihir Nemani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Victoria Koontz
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kira Lathrop
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Katarzyna M Kedziora
- Department of Cell Biology and Center for Biologic Imaging, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jonathan Franks
- Department of Cell Biology and Center for Biologic Imaging, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ming Sun
- Department of Cell Biology and Center for Biologic Imaging, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Joshua W Smith
- Johns Hopkins Mass Spectrometry and Proteomics Facility, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Lauren R DeVine
- Johns Hopkins Mass Spectrometry and Proteomics Facility, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Robert N Cole
- Johns Hopkins Mass Spectrometry and Proteomics Facility, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Nadezda Stepicheva
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Anastasia Strizhakova
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Sreya Chattopadhyay
- Department of Physiology, University of Calcutta, Kolkata, West Bengal, India
| | - Stacey Hose
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jacob Samuel Zigler
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - José-Alain Sahel
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Institut De La Vision, INSERM, CNRS, Sorbonne Université, Paris, France
| | - Jiang Qian
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Prasun Guha
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, Nevada, USA
| | - James T Handa
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Sayan Ghosh
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Debasish Sinha
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Ophthalmology, The Wilmer Eye Institute, the Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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3
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Fenn J, Madon K, Conibear E, Derelle R, Nevin S, Kundu R, Hakki S, Tregoning JS, Koycheva A, Derqui N, Tolosa-Wright M, Jonnerby J, Wang L, Baldwin S, Pillay TD, Thwaites RS, Luca C, Varro R, Badhan A, Parker E, Rosadas C, McClure M, Tedder R, Taylor G, Lalvani A. An ultra-early, transient interferon-associated innate immune response associates with protection from SARS-CoV-2 infection despite exposure. EBioMedicine 2025; 111:105475. [PMID: 39667271 PMCID: PMC11697275 DOI: 10.1016/j.ebiom.2024.105475] [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: 03/20/2024] [Revised: 11/06/2024] [Accepted: 11/12/2024] [Indexed: 12/14/2024] Open
Abstract
BACKGROUND A proportion of individuals exposed to respiratory viruses avoid contracting detectable infection. We tested the hypothesis that early innate immune responses associate with resistance to detectable infection in close contacts of COVID-19 cases. METHODS 48 recently-exposed household contacts of symptomatic COVID-19 cases were recruited in London, UK between May 2020 and March 2021 through a prospective, longitudinal observational study. Blood and nose and throat swabs were collected during the acute period of index case viral shedding and longitudinally thereafter. Magnitude of SARS-CoV-2 exposure was quantified, and serial PCR and serological assays used to determine infection status of contacts. Whole-blood RNA-seq was performed and analysed to identify transcriptomic signatures of early infection and resistance to infection. FINDINGS 24 highly-exposed household contacts became PCR-positive and seropositive whilst 24 remained persistently PCR-negative and seronegative. A 96-gene transcriptomic signature of early SARS-CoV-2 infection was identified using RNA-seq of longitudinal blood samples from PCR-positive contacts. This signature was dominated by interferon-associated genes and expression correlated positively with viral load. Elevated expression of this 96-gene signature was also observed during exposure in 25% (6/24) of persistently PCR-negative, seronegative contacts. PCR-negative contacts with elevated signature expression had higher-magnitude SARS-CoV-2 exposure compared to those with low signature expression. We validated this signature in SARS-CoV-2-infected individuals in two independent cohorts. In naturally-exposed healthcare workers (HCWs) we found that 7/58 (12%) PCR-negative HCWs exhibited elevated signature expression. Comparing gene-signature expression in SARS-CoV-2 Controlled Human Infection Model (CHIM) volunteers pre- and post-inoculation, we observed that 14 signature genes were transiently upregulated as soon as 6 hr post-inoculation in PCR-negative volunteers, while in PCR-positive volunteers gene-signature upregulation did not occur until 3 days later. INTERPRETATION Our interferon-associated signature of early SARS-CoV-2 infection characterises a subgroup of exposed, uninfected contacts in three independent cohorts who may have successfully aborted infection prior to induction of adaptive immunity. The earlier transient upregulation of signature genes in PCR-negative compared to PCR-positive CHIM volunteers suggests that ultra-early interferon-associated innate immune responses correlate with, and may contribute to, protection against SARS-CoV-2 infection. FUNDING This work was supported by the NIHR Health Protection Research Unit in Respiratory Infections, United Kingdom, NIHR Imperial College London, United Kingdom (Grant number: NIHR200927; AL) in partnership with the UK Health Security Agency and the NIHR Medical Research Council (MRC), United Kingdom (Grant number: MR/X004058/1). Support for sequencing was provided by the Imperial BRC Genomics Facility which is funded by the NIHR, United Kingdom. The development of the hybrid DABA assay used for quantification of SARS-CoV-2 anti-Spike RBD antibodies was supported by the MRC (MC_PC_19078).
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Affiliation(s)
- Joe Fenn
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK.
| | - Kieran Madon
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Emily Conibear
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Romain Derelle
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Sean Nevin
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Rhia Kundu
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Seran Hakki
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - John S Tregoning
- Department of Infectious Disease, Imperial College London, London, UK
| | - Aleksandra Koycheva
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Nieves Derqui
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Mica Tolosa-Wright
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Jakob Jonnerby
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Lulu Wang
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Samuel Baldwin
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Timesh D Pillay
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Ryan S Thwaites
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Constanta Luca
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Robert Varro
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
| | - Anjna Badhan
- Department of Infectious Disease, Imperial College London, London, UK
| | - Eleanor Parker
- Department of Infectious Disease, Imperial College London, London, UK
| | - Carolina Rosadas
- Department of Infectious Disease, Imperial College London, London, UK
| | - Myra McClure
- Department of Infectious Disease, Imperial College London, London, UK
| | - Richard Tedder
- Department of Infectious Disease, Imperial College London, London, UK
| | - Graham Taylor
- Department of Infectious Disease, Imperial College London, London, UK
| | - Ajit Lalvani
- NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London, London, UK; National Heart and Lung Institute, Imperial College London, London, UK
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4
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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] [Download PDF] [Figures] [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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5
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Davies LRL, Wang C, Steigler P, Bowman KA, Fischinger S, Hatherill M, Fisher M, Mbandi SK, Rodo M, Ottenhoff THM, Dockrell HM, Sutherland JS, Mayanja-Kizza H, Boom WH, Walzl G, Kaufmann SHE, Nemes E, Scriba TJ, Lauffenburger D, Alter G, Fortune SM. Age and sex influence antibody profiles associated with tuberculosis progression. Nat Microbiol 2024; 9:1513-1525. [PMID: 38658786 PMCID: PMC11153143 DOI: 10.1038/s41564-024-01678-x] [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: 10/12/2023] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Antibody features vary with tuberculosis (TB) disease state. Whether clinical variables, such as age or sex, influence associations between Mycobacterium tuberculosis-specific antibody responses and disease state is not well explored. Here we profiled Mycobacterium tuberculosis-specific antibody responses in 140 TB-exposed South African individuals from the Adolescent Cohort Study. We identified distinct response features in individuals progressing to active TB from non-progressing, matched controls. A multivariate antibody score differentially associated with progression (SeroScore) identified progressors up to 2 years before TB diagnosis, earlier than that achieved with the RISK6 transcriptional signature of progression. We validated these antibody response features in the Grand Challenges 6-74 cohort. Both the SeroScore and RISK6 correlated better with risk of TB progression in adolescents compared with adults, and in males compared with females. This suggests that age and sex are important, underappreciated modifiers of antibody responses associated with TB progression.
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Affiliation(s)
- Leela R L Davies
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Chuangqi Wang
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pia Steigler
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Kathryn A Bowman
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Michelle Fisher
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Stanley Kimbung Mbandi
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Miguel Rodo
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, the Netherlands
| | - Hazel M Dockrell
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Banjul, The Gambia
| | - Jayne S Sutherland
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, Banjul, The Gambia
| | - Harriet Mayanja-Kizza
- Department of Medicine and Department of Microbiology, Makerere University, Kampala, Uganda
| | - W Henry Boom
- Tuberculosis Research Unit, Case Western Reserve University, Cleveland, OH, USA
| | - Gerhard Walzl
- Department of Science and Technology National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stefan H E Kaufmann
- Max Planck Institute for Infection Biology, Berlin, Germany
- Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Hagler Institute for Advanced Study, Texas A&M University, College Station, TX, USA
| | - Elisa Nemes
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative and Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | | | - Galit Alter
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA.
- Moderna Therapeutics, Cambridge, MA, USA.
| | - Sarah M Fortune
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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6
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Sui Y, Andersen H, Li J, Hoang T, Minai M, Nagata BM, Bock KW, Alves DA, Lewis MG, Berzofsky JA. SARS-CoV-2 mucosal vaccine protects against clinical disease with sex bias in efficacy. Vaccine 2024; 42:339-351. [PMID: 38071106 PMCID: PMC10843685 DOI: 10.1016/j.vaccine.2023.11.059] [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: 05/30/2023] [Revised: 09/14/2023] [Accepted: 11/28/2023] [Indexed: 01/01/2024]
Abstract
Intranasal mucosal vaccines can more effectively induce mucosal immune responses against SARS-CoV-2. Here, we show in hamsters that an intranasal subunit mucosal vaccine boost with the beta variant S1 can prevent weight loss, in addition to reducing viral load, which cannot be studied in macaques that don't develop COVID-like disease. Protective efficacy against both viral load and weight loss correlated with serum antibody titers. A sex bias was detected in that immune responses and protection against viral load were greater in females than males. We also found that priming with S1 from the Wuhan strain elicited lower humoral immune responses against beta variant and led to less protection against beta viral challenge, suggesting the importance of matched antigens. The greater efficacy of mucosal vaccines in the upper respiratory tract and the need to consider sex differences in vaccine protection are important in the development of future improved COVID-19 vaccines.
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Affiliation(s)
- Yongjun Sui
- Vaccine Branch, Center of for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.
| | | | - Jianping Li
- Vaccine Branch, Center of for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Tanya Hoang
- Vaccine Branch, Center of for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Mahnaz Minai
- Infectious Disease Pathogenesis Section, National Institute of Allergy and Infectious Diseases, Rockville, MD 20852, USA
| | - Bianca M Nagata
- Infectious Disease Pathogenesis Section, National Institute of Allergy and Infectious Diseases, Rockville, MD 20852, USA
| | - Kevin W Bock
- Infectious Disease Pathogenesis Section, National Institute of Allergy and Infectious Diseases, Rockville, MD 20852, USA
| | - Derron A Alves
- Infectious Disease Pathogenesis Section, National Institute of Allergy and Infectious Diseases, Rockville, MD 20852, USA
| | | | - Jay A Berzofsky
- Vaccine Branch, Center of for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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7
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Kyosei Y, Yoshimura T, Ito E. Removal of Soluble ACE2 in VeroE6 Cells by 17β-Estradiol Reduces SARS-CoV-2 Infectivity. Biol Pharm Bull 2023; 46:1842-1845. [PMID: 37866890 DOI: 10.1248/bpb.b23-00568] [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] [Indexed: 10/24/2023]
Abstract
Women are less susceptible than men to coronavirus disease 2019 (COVID-19), which might be due to the female steroid hormone 17β-estradiol. We hypothesized that 17β-estradiol removes the soluble portion of angiotensin-converting enzyme 2 (sACE2) to which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds in host cells and that sACE2 then binds to the virus, thereby reducing the infectivity. In the present study, VeroE6/serine protease 2 transmembrane protein (TMPRSS2) cells were infected with pseudo SARS-CoV-2 viruses and used as our model system. This infectivity was reduced by the application of 17β-estradiol. After applying 17β-estradiol to the VeroE6/TMPRSS2 cells, we used a sandwich enzyme-linked immunosorbent assay (ELISA) to measure the sACE2 concentration in the culture medium. Our findings revealed that 17β-estradiol removes sACE2 from VeroE6/TMPRSS2 cells. Furthermore, the pseudo SARS-CoV-2 viruses were incubated in culture medium with ACE2 collected from 17β-estradiol-treated VeroE6/TMPRSS2 cells, and the viruses were measured with an ultrasensitive ELISA using anti-spike protein antibodies. The amount of spike proteins decreased according to the concentration of 17β-estradiol applied. These results clearly demonstrated that the soluble portion of ACE2, which was removed from 17β-estradiol-treated VeroE6/TMPRSS2 cells, bound to the spike proteins of SARS-CoV-2, thereby reducing COVID-19 infectivity.
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Affiliation(s)
| | - Teruki Yoshimura
- School of Pharmaceutical Sciences, Health Sciences University of Hokkaido
| | - Etsuro Ito
- Department of Biology, Waseda University
- Graduate Institute of Medicine, Kaohsiung Medical University
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Miller RAJ, Williams AP, Kovats S. Sex chromosome complement and sex steroid signaling underlie sex differences in immunity to respiratory virus infection. Front Pharmacol 2023; 14:1150282. [PMID: 37063266 PMCID: PMC10097973 DOI: 10.3389/fphar.2023.1150282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/08/2023] [Indexed: 04/18/2023] Open
Abstract
Epidemiological studies have revealed sex differences in the incidence and morbidity of respiratory virus infection in the human population, and often these observations are correlated with sex differences in the quality or magnitude of the immune response. Sex differences in immunity and morbidity also are observed in animal models of respiratory virus infection, suggesting differential dominance of specific immune mechanisms. Emerging research shows intrinsic sex differences in immune cell transcriptomes, epigenomes, and proteomes that may regulate human immunity when challenged by viral infection. Here, we highlight recent research into the role(s) of sex steroids and X chromosome complement in immune cells and describe how these findings provide insight into immunity during respiratory virus infection. We focus on the regulation of innate and adaptive immune cells by receptors for androgen and estrogens, as well as genes with a propensity to escape X chromosome inactivation. A deeper mechanistic knowledge of these pathways will help us to understand the often significant sex differences in immunity to endemic or pandemic respiratory pathogens such as influenza viruses, respiratory syncytial viruses and pathogenic coronaviruses.
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Affiliation(s)
- Reegan A. J. Miller
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Abigael P. Williams
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Susan Kovats
- Arthritis and Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
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Ren J, Zhang Y, Guo W, Feng K, Yuan Y, Huang T, Cai YD. Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods. Life (Basel) 2023; 13:798. [PMID: 36983953 PMCID: PMC10051382 DOI: 10.3390/life13030798] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20-85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications.
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Affiliation(s)
- Jingxin Ren
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yuhang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, China
| | - Kaiyan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, 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 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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Zhang Z, Sauerwald N, Cappuccio A, Ramos I, Nair VD, Nudelman G, Zaslavsky E, Ge Y, Gaitas A, Ren H, Brockman J, Geis J, Ramalingam N, King D, McClain MT, Woods CW, Henao R, Burke TW, Tsalik EL, Goforth CW, Lizewski RA, Lizewski SE, Weir DL, Letizia AG, Sealfon SC, Troyanskaya OG. Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease. CELL REPORTS METHODS 2023; 3:100395. [PMID: 36936082 PMCID: PMC10014279 DOI: 10.1016/j.crmeth.2023.100395] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/31/2022] [Accepted: 01/09/2023] [Indexed: 01/13/2023]
Abstract
Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.
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Affiliation(s)
- Zijun Zhang
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Natalie Sauerwald
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - German Nudelman
- 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
| | - Yongchao Ge
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Angelo Gaitas
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hui Ren
- Fluidigm Corporation, South San Francisco, CA 94080, USA
| | - Joel Brockman
- Fluidigm Corporation, South San Francisco, CA 94080, USA
| | - Jennifer Geis
- Fluidigm Corporation, South San Francisco, CA 94080, USA
| | | | - David King
- Fluidigm Corporation, South San Francisco, CA 94080, USA
| | - Micah T. McClain
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Christopher W. Woods
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Ricardo Henao
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Thomas W. Burke
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Ephraim L. Tsalik
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | | | | | | | - Dawn L. Weir
- Naval Medical Research Center, Silver Spring, MD, USA
| | | | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Olga G. Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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