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Islam MA, Getz M, Macklin P, Ford Versypt AN. An agent-based modeling approach for lung fibrosis in response to COVID-19. PLoS Comput Biol 2023; 19:e1011741. [PMID: 38127835 PMCID: PMC10769079 DOI: 10.1371/journal.pcbi.1011741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 01/05/2024] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-β). The TGF-β-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-β sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-β, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-β, and the duration of TGF-β sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-β sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-β, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-β sources changed the fibrotic patterns; in the presence of persistent TGF-β sources, partial removal of TGF-β from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-β sources may find applications in the current clinical trials targeting TGF-β for the resolution of lung fibrosis.
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Affiliation(s)
- Mohammad Aminul Islam
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America
| | - Ashlee N. Ford Versypt
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
- Institute for Artificial Intelligence and Data Science, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
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2
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Potamias G, Gkoublia P, Kanterakis A. The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest. Front Immunol 2023; 14:1251067. [PMID: 38077337 PMCID: PMC10699200 DOI: 10.3389/fimmu.2023.1251067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
Introduction The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes. Methods Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results. Results The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events. Discussion The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.
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Affiliation(s)
- George Potamias
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
| | - Polymnia Gkoublia
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
- Graduate Bioinformatics Program, School of Medicine, University of Crete, Heraklion, Greece
| | - Alexandros Kanterakis
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece
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3
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Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
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4
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Raach B, Bundgaard N, Haase MJ, Starruß J, Sotillo R, Stanifer ML, Graw F. Influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics within human airway epithelium. PLoS Comput Biol 2023; 19:e1011356. [PMID: 37566610 PMCID: PMC10446191 DOI: 10.1371/journal.pcbi.1011356] [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: 01/23/2023] [Revised: 08/23/2023] [Accepted: 07/13/2023] [Indexed: 08/13/2023] Open
Abstract
Human airway epithelium (HAE) represents the primary site of viral infection for SARS-CoV-2. Comprising different cell populations, a lot of research has been aimed at deciphering the major cell types and infection dynamics that determine disease progression and severity. However, the cell type-specific replication kinetics, as well as the contribution of cellular composition of the respiratory epithelium to infection and pathology are still not fully understood. Although experimental advances, including Air-liquid interface (ALI) cultures of reconstituted pseudostratified HAE, as well as lung organoid systems, allow the observation of infection dynamics under physiological conditions in unprecedented level of detail, disentangling and quantifying the contribution of individual processes and cells to these dynamics remains challenging. Here, we present how a combination of experimental data and mathematical modelling can be used to infer and address the influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics. Using a stepwise approach that integrates various experimental data on HAE culture systems with regard to tissue differentiation and infection dynamics, we develop an individual cell-based model that enables investigation of infection and regeneration dynamics within pseudostratified HAE. In addition, we present a novel method to quantify tissue integrity based on image data related to the standard measures of transepithelial electrical resistance measurements. Our analysis provides a first aim of quantitatively assessing cell type specific infection kinetics and shows how tissue composition and changes in regeneration capacity, as e.g. in smokers, can influence disease progression and pathology. Furthermore, we identified key measurements that still need to be assessed in order to improve inference of cell type specific infection kinetics and disease progression. Our approach provides a method that, in combination with additional experimental data, can be used to disentangle the complex dynamics of viral infection and immunity within human airway epithelial culture systems.
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Affiliation(s)
- Benjamin Raach
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Nils Bundgaard
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Marika J. Haase
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Jörn Starruß
- Center for Information Services and High Performance Computing, TU Dresden, Dresden, Germany
| | - Rocio Sotillo
- Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Megan L. Stanifer
- Department of Infectious Diseases, Molecular Virology, University Hospital Heidelberg, Heidelberg, Germany
- University of Florida, College of Medicine, Dept. of Molecular Genetics and Microbiology, Gainesville, Florida, United States of America
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Medicine 5, Erlangen, Germany
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5
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Pearson J, Wessler T, Chen A, Boucher RC, Freeman R, Lai SK, Pickles R, Forest MG. Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection. J Theor Biol 2023; 565:111470. [PMID: 36965846 PMCID: PMC10033495 DOI: 10.1016/j.jtbi.2023.111470] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 03/25/2023]
Abstract
The SARS-CoV-2 coronavirus continues to evolve with scores of mutations of the spike, membrane, envelope, and nucleocapsid structural proteins that impact pathogenesis. Infection data from nasal swabs, nasal PCR assays, upper respiratory samples, ex vivo cell cultures and nasal epithelial organoids reveal extreme variabilities in SARS-CoV-2 RNA titers within and between the variants. Some variabilities are naturally prone to clinical testing protocols and experimental controls. Here we focus on nasal viral load sensitivity arising from the timing of sample collection relative to onset of infection and from heterogeneity in the kinetics of cellular infection, uptake, replication, and shedding of viral RNA copies. The sources of between-variant variability are likely due to SARS-CoV-2 structural protein mutations, whereas within-variant population variability is likely due to heterogeneity in cellular response to that particular variant. With the physiologically faithful, agent-based mechanistic model of inhaled exposure and infection from (Chen et al., 2022), we perform statistical sensitivity analyses of the progression of nasal viral titers in the first 0-48 h post infection, focusing on three kinetic mechanisms. Model simulations reveal shorter latency times of infected cells (including cellular uptake, viral RNA replication, until the onset of viral RNA shedding) exponentially accelerate nasal viral load. Further, the rate of infectious RNA copies shed per day has a proportional influence on nasal viral load. Finally, there is a very weak, negative correlation of viral load with the probability of infection per virus-cell encounter, the model proxy for spike-receptor binding affinity.
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Affiliation(s)
- Jason Pearson
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Timothy Wessler
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alex Chen
- Department of Mathematics, California State University-Dominguez Hills, Carson, CA 90747, USA
| | - Richard C Boucher
- Marsico Lung Institute, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ronit Freeman
- Department of Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Samuel K Lai
- Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA and North Carolina State University, Raleigh, NC 27606, USA; Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - Raymond Pickles
- Marsico Lung Institute, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA
| | - M Gregory Forest
- Department of Mathematics, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; Department of Applied Physical Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA; UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill, Chapel Hill, NC 27599, USA and North Carolina State University, Raleigh, NC 27606, USA.
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6
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Blanco-Rodríguez R, Ordoñez-Jiménez F, Almocera AES, Chinney-Herrera G, Hernández-Vargas E. Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19. Math Biosci 2023; 361:109011. [PMID: 37149125 PMCID: PMC10159681 DOI: 10.1016/j.mbs.2023.109011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/08/2023]
Abstract
The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different longitudinal data sets will be central for a holistic way of tackling COVID-19.
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Affiliation(s)
- Rodolfo Blanco-Rodríguez
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, 83844-1103, USA; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, USA; Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico
| | - Fernanda Ordoñez-Jiménez
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico
| | - Alexis Erich S Almocera
- Department of Mathematics, Physics and Computer Science, College of Science and Mathematics, University of the Philippines Mindanao, Davao City, Philippines
| | - Gustavo Chinney-Herrera
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico
| | - Esteban Hernández-Vargas
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, 83844-1103, USA; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID, USA; Instituto de Matemáticas, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Qro., 76230, Mexico.
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7
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Cable J, Balachandran S, Daley-Bauer LP, Rustagi A, Antony F, Frere JJ, Strampe J, Kedzierska K, Cannon JL, McGargill MA, Weiskopf D, Mettelman RC, Niessl J, Thomas PG, Briney B, Valkenburg SA, Bloom JD, Bjorkman PJ, Iketani S, Rappazzo CG, Crooks CM, Crofts KF, Pöhlmann S, Krammer F, Sant AJ, Nabel GJ, Schultz-Cherry S. Viral immunity: Basic mechanisms and therapeutic applications-a Keystone Symposia report. Ann N Y Acad Sci 2023; 1521:32-45. [PMID: 36718537 DOI: 10.1111/nyas.14960] [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: 02/01/2023]
Abstract
Viruses infect millions of people each year. Both endemic viruses circulating throughout the population as well as novel epidemic and pandemic viruses pose ongoing threats to global public health. Developing more effective tools to address viruses requires not only in-depth knowledge of the virus itself but also of our immune system's response to infection. On June 29 to July 2, 2022, researchers met for the Keystone symposium "Viral Immunity: Basic Mechanisms and Therapeutic Applications." This report presents concise summaries from several of the symposium presenters.
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Affiliation(s)
| | - Siddharth Balachandran
- Blood Cell Development and Function Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Lisa P Daley-Bauer
- Department of Microbiology and Immunology, Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Arjun Rustagi
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - Ferrin Antony
- Department of Pathobiology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA
| | - Justin J Frere
- East Harlem Health Outreach Partnership; Department of Medical Education; and Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jamie Strampe
- Bioinformatics Program, Boston University and National Emerging Infectious Diseases Laboratories, Boston, Massachusetts, USA
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Judy L Cannon
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
| | - Maureen A McGargill
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Daniela Weiskopf
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, California, USA
| | - Robert C Mettelman
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Julia Niessl
- Department of Medicine, Center for Infectious Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Microbiology, Immunology, and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Bryan Briney
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, USA
| | - Sophie A Valkenburg
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, PR China
| | - Jesse D Bloom
- Basic Sciences Division and Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Microbiology and Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Pamela J Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Sho Iketani
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
| | | | - Chelsea M Crooks
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Kali F Crofts
- Department of Microbiology and Immunology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Stefan Pöhlmann
- Infection Biology Unit, German Primate Center and Faculty of Biology and Psychology, University Medical Center Göttingen, Georg-August-University Göttingen, Göttingen, Germany
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrea J Sant
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Gary J Nabel
- Modex Therapeutics Inc., an OPKO Health Company, Natick, Massachusetts, USA
| | - Stacey Schultz-Cherry
- Department of Laboratory Medicine and Department of Immunology, Yale University School of Medicine, New Haven, Connecticut, USA
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8
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Chen A, Wessler T, Gregory Forest M. Antibody protection from SARS-CoV-2 respiratory tract exposure and infection. J Theor Biol 2023; 557:111334. [PMID: 36306828 PMCID: PMC9597531 DOI: 10.1016/j.jtbi.2022.111334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
The COVID-19 pandemic has underscored the need to understand the dynamics of SARS-CoV-2 respiratory infection and protection provided by the immune response. SARS-CoV-2 infections are characterized by a particularly high viral load, and further by the small number of inhaled virions sufficient to generate a high viral titer in the nasal passage a few days after exposure. SARS-CoV-2 specific antibodies (Ab), induced from vaccines, previous infection, or inhaled monoclonal Ab, have proven effective against SARS-CoV-2 infection. Our goal in this work is to model the protective mechanisms that Ab can provide and to assess the degree of protection from individual and combined mechanisms at different locations in the respiratory tract. Neutralization, in which Ab bind to virion spikes and inhibit them from binding to and infecting target cells, is one widely reported protective mechanism. A second mechanism of Ab protection is muco-trapping, in which Ab crosslink virions to domains on mucin polymers, effectively immobilizing them in the mucus layer. When muco-trapped, the continuous clearance of the mucus barrier by coordinated ciliary propulsion entrains the trapped viral load toward the esophagus to be swallowed. We model and simulate the protection provided by either and both mechanisms at different locations in the respiratory tract, parametrized by the Ab titer and binding-unbinding rates of Ab to viral spikes and mucin domains. Our results illustrate limits in the degree of protection by neutralizing Ab alone, the powerful protection afforded by muco-trapping Ab, and the potential for dual protection by muco-trapping and neutralizing Ab to arrest a SARS-CoV-2 infection. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Alex Chen
- Department of Mathematics, California State University-Dominguez Hills, Carson, CA 90747, USA.
| | - Timothy Wessler
- Department of Mathematics, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA
| | - M. Gregory Forest
- Department of Mathematics, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA,Department of Applied Physical Sciences, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA,UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599 and North Carolina State University, Raleigh, NC 27606, USA
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9
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Aristotelous AC, Chen A, Forest MG. A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response. J Theor Biol 2022; 555:111293. [PMID: 36208668 PMCID: PMC9533651 DOI: 10.1016/j.jtbi.2022.111293] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 01/14/2023]
Abstract
We develop a lattice-based, hybrid discrete-continuum modeling framework for SARS-CoV-2 exposure and infection in the human lung alveolar region, or parenchyma, the massive surface area for gas exchange. COVID-19 pneumonia is alveolar infection by the SARS-CoV-2 virus significant enough to compromise gas exchange. The modeling framework orchestrates the onset and progression of alveolar infection, spatially and temporally, beginning with a pre-immunity baseline, upon which we superimpose multiple mechanisms of immune protection conveyed by interferons and antibodies. The modeling framework is tunable to individual profiles, focusing here on degrees of innate immunity, and to the evolving infection-replication properties of SARS-CoV-2 variant strains. The model employs partial differential equations for virion, interferon, and antibody concentrations governed by diffusion in the thin fluid coating of alveolar cells, species and lattice interactions corresponding to sources and sinks for each species, and multiple immune protections signaled by interferons. The spatial domain is a two-dimensional, rectangular lattice of alveolar type I (non-infectable) and type II (infectable) cells with a stochastic, species-concentration-governed, switching dynamics of type II lattice sites from healthy to infected. Once infected, type II cells evolve through three phases: an eclipse phase during which RNA copies (virions) are assembled; a shedding phase during which virions and interferons are released; and then cell death. Model simulations yield the dynamic spread of, and immune protection against, alveolar infection and viral load from initial sites of exposure. We focus in this paper on model illustrations of the diversity of outcomes possible from alveolar infection, first absent of immune protection, and then with varying degrees of four known mechanisms of interferon-induced innate immune protection. We defer model illustrations of antibody protection to future studies. Results presented reinforce previous recognition that interferons produced solely by infected cells are insufficient to maintain a high efficacy level of immune protection, compelling additional mechanisms to clear alveolar infection, such as interferon production by immune cells and adaptive immunity (e.g., T cells). This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Andreas C. Aristotelous
- Department of Mathematics, The University of Akron, Akron, OH 44325-4002, United States of America,Corresponding author
| | - Alex Chen
- Department of Mathematics, California State University, Dominguez Hills, CA 90747, United States of America
| | - M. Gregory Forest
- Departments of Mathematics, Applied Physical Sciences, and Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3250, United States of America
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10
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Sumi T, Harada K. Immune response to SARS-CoV-2 in severe disease and long COVID-19. iScience 2022; 25:104723. [PMID: 35813874 PMCID: PMC9251893 DOI: 10.1016/j.isci.2022.104723] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/23/2022] [Accepted: 06/29/2022] [Indexed: 01/10/2023] Open
Abstract
COVID-19 is mild to moderate in otherwise healthy individuals but may nonetheless cause life-threatening disease and/or a wide range of persistent symptoms. The general determinant of disease severity is age mainly because the immune response declines in aging patients. Here, we developed a mathematical model of the immune response to SARS-CoV-2 and revealed that typical age-related risk factors such as only a several 10% decrease in innate immune cell activity and inhibition of type-I interferon signaling by autoantibodies drastically increased the viral load. It was reported that the numbers of certain dendritic cell subsets remained less than half those in healthy donors even seven months after infection. Hence, the inflammatory response was ongoing. Our model predicted the persistent DC reduction and showed that certain patients with severe and even mild symptoms could not effectively eliminate the virus and could potentially develop long COVID. Systemic SARS-CoV-2 infection owing to ACE2 expression on a wide range of cell types Persistent viral infection can be ongoing within the host even if it is not severe Long-term dendritic cell deficiency is owing to viruses that cannot be removed by the host Ongoing persistent viral infection within the host potentially causes long COVID or PASC
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Affiliation(s)
- Tomonari Sumi
- Research Institute for Interdisciplinary Science, Okayama University, 3-1-1 Tsushima-Naka, Kita-ku, Okayama 700-8530, Japan
- Department of Chemistry, Faculty of Science, Okayama University, 3-1-1 Tsushima-Naka, Kita-ku, Okayama 700-8530, Japan
- Corresponding author
| | - Kouji Harada
- Department of Computer Science and Engineering, Toyohashi University of Technology, Tempaku-cho, Toyohashi 441-8580, Japan
- Center for IT-Based Education, Toyohashi University of Technology, Tempaku-cho, Toyohashi, Aichi 441-8580, Japan
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