1
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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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Williams T, McCaw JM, Osborne JM. Choice of spatial discretisation influences the progression of viral infection within multicellular tissues. J Theor Biol 2023; 573:111592. [PMID: 37558160 DOI: 10.1016/j.jtbi.2023.111592] [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: 02/20/2023] [Revised: 06/16/2023] [Accepted: 08/02/2023] [Indexed: 08/11/2023]
Abstract
There has been an increasing recognition of the utility of models of the spatial dynamics of viral spread within tissues. Multicellular models, where cells are represented as discrete regions of space coupled to a virus density surface, are a popular approach to capture these dynamics. Conventionally, such models are simulated by discretising the viral surface and depending on the rate of viral diffusion and other considerations, a finer or coarser discretisation may be used. The impact that this choice may have on the behaviour of the system has not been studied. Here we demonstrate that under realistic parameter regimes - where viral diffusion is small enough to support the formation of familiar ring-shaped infection plaques - the choice of spatial discretisation of the viral surface can qualitatively change key model outcomes including the time scale of infection. Importantly, we show that the choice between implementing viral spread as a cell-scale process, or as a high-resolution converged PDE can generate distinct model outcomes, which raises important conceptual questions about the strength of assumptions underpinning the spatial structure of the model. We investigate the mechanisms driving these discretisation artefacts, the impacts they may have on model predictions, and provide guidance on the design and implementation of spatial and especially multicellular models of viral dynamics. We obtain our results using the simplest TIV construct for the viral dynamics, and therefore anticipate that the important effects we describe will also influence model predictions in more complex models of virus-cell-immune system interactions. This analysis will aid in the construction of models for robust and biologically realistic modelling and inference.
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Affiliation(s)
- Thomas Williams
- School of Mathematics and Statistics, University of Melbourne, Australia
| | - James M McCaw
- School of Mathematics and Statistics, University of Melbourne, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Australia
| | - James M Osborne
- School of Mathematics and Statistics, University of Melbourne, Australia.
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3
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Darquenne C, Borojeni AA, Colebank MJ, Forest MG, Madas BG, Tawhai M, Jiang Y. Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations. Front Physiol 2022; 13:923945. [PMID: 35795643 PMCID: PMC9251577 DOI: 10.3389/fphys.2022.923945] [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: 04/19/2022] [Accepted: 05/24/2022] [Indexed: 12/18/2022] Open
Abstract
The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals.
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Affiliation(s)
- Chantal Darquenne
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
- *Correspondence: Chantal Darquenne,
| | - Azadeh A.T. Borojeni
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Mitchel J. Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center and Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - M. Gregory Forest
- Departments of Mathematics, Applied Physical Sciences, and Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Balázs G. Madas
- Environmental Physics Department, Centre for Energy Research, Budapest, Hungary
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Yi Jiang
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States
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4
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Chen A, Wessler T, Daftari K, Hinton K, Boucher RC, Pickles R, Freeman R, Lai SK, Forest MG. Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection. Biophys J 2022; 121:1619-1631. [PMID: 35378080 PMCID: PMC8975607 DOI: 10.1016/j.bpj.2022.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/27/2021] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.
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Affiliation(s)
- Alexander Chen
- Department of Mathematics, CSU Dominguez Hills, Carson, California
| | - Timothy Wessler
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina.
| | - Katherine Daftari
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Kameryn Hinton
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Richard C Boucher
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Raymond Pickles
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Ronit Freeman
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Samuel K Lai
- Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina; Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, UNC Chapel Hill, Chapel Hill, North Carolina
| | - M Gregory Forest
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina.
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5
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Moses ME, Hofmeyr S, Cannon JL, Andrews A, Gridley R, Hinga M, Leyba K, Pribisova A, Surjadidjaja V, Tasnim H, Forrest S. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection. PLoS Comput Biol 2021; 17:e1009735. [PMID: 34941862 PMCID: PMC8740970 DOI: 10.1371/journal.pcbi.1009735] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/07/2022] [Accepted: 12/09/2021] [Indexed: 01/03/2023] Open
Abstract
A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.
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Affiliation(s)
- Melanie E. Moses
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
| | - Steven Hofmeyr
- Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Judy L. Cannon
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Akil Andrews
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Rebekah Gridley
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Monica Hinga
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Kirtus Leyba
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
| | - Abigail Pribisova
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Vanessa Surjadidjaja
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Humayra Tasnim
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Stephanie Forrest
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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6
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Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Ford Versypt AN, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.04.02.019075. [PMID: 32511322 PMCID: PMC7239052 DOI: 10.1101/2020.04.02.019075] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.
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7
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Myers MA, Smith AP, Lane LC, Moquin DJ, Aogo R, Woolard S, Thomas P, Vogel P, Smith AM. Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity. eLife 2021; 10:68864. [PMID: 34282728 PMCID: PMC8370774 DOI: 10.7554/elife.68864] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Influenza viruses cause a significant amount of morbidity and mortality. Understanding host immune control efficacy and how different factors influence lung injury and disease severity are critical. We established and validated dynamical connections between viral loads, infected cells, CD8+ T cells, lung injury, inflammation, and disease severity using an integrative mathematical model-experiment exchange. Our results showed that the dynamics of inflammation and virus-inflicted lung injury are distinct and nonlinearly related to disease severity, and that these two pathologic measurements can be independently predicted using the model-derived infected cell dynamics. Our findings further indicated that the relative CD8+ T cell dynamics paralleled the percent of the lung that had resolved with the rate of CD8+ T cell-mediated clearance rapidly accelerating by over 48,000 times in 2 days. This complimented our analyses showing a negative correlation between the efficacy of innate and adaptive immune-mediated infected cell clearance, and that infection duration was driven by CD8+ T cell magnitude rather than efficacy and could be significantly prolonged if the ratio of CD8+ T cells to infected cells was sufficiently low. These links between important pathogen kinetics and host pathology enhance our ability to forecast disease progression, potential complications, and therapeutic efficacy.
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Affiliation(s)
- Margaret A Myers
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Lindey C Lane
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - David J Moquin
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, United States
| | - Rosemary Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
| | - Stacie Woolard
- Flow Cytometry Core, St. Jude Children's Research Hospital, Memphis, United States
| | - Paul Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, United States
| | - Peter Vogel
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, United States
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, United States
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8
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Saad-Roy CM, McDermott AB, Grenfell BT. Dynamic Perspectives on the Search for a Universal Influenza Vaccine. J Infect Dis 2020; 219:S46-S56. [PMID: 30715467 DOI: 10.1093/infdis/jiz044] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
A universal influenza vaccine (UIV) could considerably alleviate the public health burden of both seasonal and pandemic influenza. Although significant progress has been achieved in clarifying basic immunology and virology relating to UIV, several important questions relating to the dynamics of infection, immunity, and pathogen evolution remain unsolved. In this study, we review these gaps, which span integrative levels, from cellular to global and timescales from molecular events to decades. We argue that they can be best addressed by a tight integration of empirical (laboratory, epidemiological) research and theory and suggest fruitful areas for this synthesis. In particular, quantifying natural and vaccinal limitations on viral transmission are central to this effort.
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Affiliation(s)
| | - Adrian B McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, New Jersey.,Woodrow Wilson School of Public and International Affairs, Princeton University, New Jersey.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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9
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Nikolaev EV, Zloza A, Sontag ED. Immunobiochemical Reconstruction of Influenza Lung Infection-Melanoma Skin Cancer Interactions. Front Immunol 2019; 10:4. [PMID: 30745900 PMCID: PMC6360404 DOI: 10.3389/fimmu.2019.00004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 01/02/2019] [Indexed: 12/20/2022] Open
Abstract
It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors.
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Affiliation(s)
- Evgeni V. Nikolaev
- Center for Quantitative Biology, Rutgers University, Piscataway, NJ, United States
- Clinical Investigations and Precision Therapeutics Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Andrew Zloza
- Section of Surgical Oncology Research, Division of Surgical Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Eduardo D. Sontag
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, United States
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10
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Gallagher ME, Brooke CB, Ke R, Koelle K. Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 2018; 10:E627. [PMID: 30428545 PMCID: PMC6267451 DOI: 10.3390/v10110627] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/08/2018] [Accepted: 11/10/2018] [Indexed: 02/07/2023] Open
Abstract
The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.
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Affiliation(s)
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
| | - Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, GA 30322, USA.
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11
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Bocharov G, Meyerhans A, Bessonov N, Trofimchuk S, Volpert V. Spatiotemporal Dynamics of Virus Infection Spreading in Tissues. PLoS One 2016; 11:e0168576. [PMID: 27997613 PMCID: PMC5173377 DOI: 10.1371/journal.pone.0168576] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/03/2016] [Indexed: 12/21/2022] Open
Abstract
Virus spreading in tissues is determined by virus transport, virus multiplication in host cells and the virus-induced immune response. Cytotoxic T cells remove infected cells with a rate determined by the infection level. The intensity of the immune response has a bell-shaped dependence on the concentration of virus, i.e., it increases at low and decays at high infection levels. A combination of these effects and a time delay in the immune response determine the development of virus infection in tissues like spleen or lymph nodes. The mathematical model described in this work consists of reaction-diffusion equations with a delay. It shows that the different regimes of infection spreading like the establishment of a low level infection, a high level infection or a transition between both are determined by the initial virus load and by the intensity of the immune response. The dynamics of the model solutions include simple and composed waves, and periodic and aperiodic oscillations. The results of analytical and numerical studies of the model provide a systematic basis for a quantitative understanding and interpretation of the determinants of the infection process in target organs and tissues from the image-derived data as well as of the spatiotemporal mechanisms of viral disease pathogenesis, and have direct implications for a biopsy-based medical testing of the chronic infection processes caused by viruses, e.g. HIV, HCV and HBV.
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Affiliation(s)
- Gennady Bocharov
- Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation
- Gamaleya Center of Epidemiology and Microbiology, Moscow, Russian Federation
- RUDN University, Moscow, Russian Federation
| | - Andreas Meyerhans
- Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation
- Infection Biology Laboratory, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- ICREA, Pg. Lluís Companys 23, Barcelona, Spain
| | - Nickolai Bessonov
- Institute of Problems of Mechanical Engineering, Russian Academy of Sciences, Saint Petersburg, Russian Federation
| | - Sergei Trofimchuk
- Instituto de Matemática y Fisica, Universidad de Talca, Talca, Chile
| | - Vitaly Volpert
- Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, France
- INRIA Team Dracula, INRIA Lyon La Doua, Villeurbanne, France
- Laboratoire Poncelet, UMI 2615 CNRS, Moscow, Russian Federation
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