1
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Eriksson M, Nylén S, Grönvik KO. Passive immunization of mice with IgY anti-H5N1 protects against experimental influenza virus infection and allows development of protective immunity. Vaccine 2024; 42:126133. [PMID: 39019655 DOI: 10.1016/j.vaccine.2024.07.034] [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: 03/31/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024]
Abstract
Influenza virus contributes substantially to the global human and animal disease burden. To protect individuals against disease, strategies are needed to minimize the time an individual is at risk of developing disease symptoms. Passive immunization using avian IgY antibodies can protect individuals against a variety of pathogens, including influenza virus. Yet the effect of IgY administration on generation of protective immunity is largely unknown. To address the effect of passive immunization on the host immune response development, adult or aged, male and female C57BL/6NCrl mice received chicken IgY anti-H5N1, normal IgY or PBS intranasally four hours before, and 20 hours after intranasal infection with H1N1 influenza A virus (PR8). The mice receiving cross-reactive IgY anti-H5N1 were protected from disease and developed influenza virus-specific memory T cells similar to control-treated mice. When re-challenged with PR8 35 days post primary infection IgY anti-H5N1-treated mice were fully protected. Moreover, when challenged with heterologous H3N2 influenza A virus (X-31) or with PR8 three months post infection the mice were protected against severe disease and death, albeit a slight transient weight loss was noted. The results show that passive immunization with IgY anti-H5N1 is safe and protects mice against disease induced by influenza virus without inhibiting development of protective immunity after virus exposure. This indicate that passive immunization can be used as prophylactic therapy in combination with immunization to prevent disease.
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Affiliation(s)
- Malin Eriksson
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 171 77 Stockholm, Sweden; Department of Microbiology, Swedish Veterinary Agency, 751 89 Uppsala, Sweden.
| | - Susanne Nylén
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 171 77 Stockholm, Sweden.
| | - Kjell-Olov Grönvik
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 171 77 Stockholm, Sweden; Uppsala Immunobiology Lab, 752 37 Uppsala, Sweden.
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2
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Liyanage YR, Heitzman-Breen N, Tuncer N, Ciupe SM. Identifiability investigation of within-host models of acute virus infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.09.593464. [PMID: 38766177 PMCID: PMC11100786 DOI: 10.1101/2024.05.09.593464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we use four mathematical models of influenza A infection with increased degrees of biological realism. We test the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refine the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we present insight into the sources of uncertainty in parameter estimation and provide guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.
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Affiliation(s)
- Yuganthi R Liyanage
- Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL, USA
| | - Nora Heitzman-Breen
- Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Necibe Tuncer
- Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL, USA
| | - Stanca M Ciupe
- Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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3
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Zitzmann C, Ke R, Ribeiro RM, Perelson AS. How robust are estimates of key parameters in standard viral dynamic models? PLoS Comput Biol 2024; 20:e1011437. [PMID: 38626190 PMCID: PMC11051641 DOI: 10.1371/journal.pcbi.1011437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 04/26/2024] [Accepted: 04/01/2024] [Indexed: 04/18/2024] Open
Abstract
Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.
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Affiliation(s)
- Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Alan S. Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico
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4
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Phan T, Zitzmann C, Chew KW, Smith DM, Daar ES, Wohl DA, Eron JJ, Currier JS, Hughes MD, Choudhary MC, Deo R, Li JZ, Ribeiro RM, Ke R, Perelson AS. Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody. PLoS Pathog 2024; 20:e1011680. [PMID: 38635853 PMCID: PMC11060554 DOI: 10.1371/journal.ppat.1011680] [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: 09/13/2023] [Revised: 04/30/2024] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.
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Affiliation(s)
- Tin Phan
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Carolin Zitzmann
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Kara W. Chew
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Davey M. Smith
- Department of Medicine, University of California, San Diego, California, United States of America
| | - Eric S. Daar
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - David A. Wohl
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Joseph J. Eron
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Judith S. Currier
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Michael D. Hughes
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Manish C. Choudhary
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rinki Deo
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jonathan Z. Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ruy M. Ribeiro
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ruian Ke
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alan S. Perelson
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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5
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Cassidy T, Stephenson KE, Barouch DH, Perelson AS. Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1. PLoS Comput Biol 2024; 20:e1011518. [PMID: 38551976 PMCID: PMC11006161 DOI: 10.1371/journal.pcbi.1011518] [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: 09/16/2023] [Revised: 04/10/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
PGT121 is a broadly neutralizing antibody in clinical development for the treatment and prevention of HIV-1 infection via passive administration. PGT121 targets the HIV-1 V3-glycan and demonstrated potent antiviral activity in a phase I clinical trial. Resistance to PGT121 monotherapy rapidly occurred in the majority of participants in this trial with the sampled rebound viruses being entirely resistant to PGT121 mediated neutralization. However, two individuals experienced long-term ART-free viral suppression following antibody infusion and retained sensitivity to PGT121 upon viral rebound. Here, we develop mathematical models of the HIV-1 dynamics during this phase I clinical trial. We utilize these models to understand the dynamics leading to PGT121 resistance and to identify the mechanisms driving the observed long-term viral control. Our modeling highlights the importance of the relative fitness difference between PGT121 sensitive and resistant subpopulations prior to treatment. Specifically, by fitting our models to data, we identify the treatment-induced competitive advantage of previously existing or newly generated resistant population as a primary driver of resistance. Finally, our modeling emphasizes the high neutralization ability of PGT121 in both participants who exhibited long-term viral control.
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Affiliation(s)
- Tyler Cassidy
- School of Mathematics, University of Leeds, Leeds, United Kingdom
| | - Kathryn E. Stephenson
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Alan S. Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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6
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Bergmann S, Brunotte L, Schughart K. Differential lung gene expression changes in C57BL/6 and DBA/2 mice carrying an identical functional Mx1 gene reveals crucial differences in the host response. BMC Genom Data 2024; 25:19. [PMID: 38360537 PMCID: PMC10870463 DOI: 10.1186/s12863-024-01203-3] [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: 10/27/2023] [Accepted: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Influenza virus infections represent a major global health problem. The dynamin-like GTPase MX1 is an interferon-dependent antiviral host protein that confers resistance to influenza virus infections. Infection models in mice are an important experimental system to understand the host response and susceptibility to developing severe disease following influenza infections. However, almost all laboratory mouse strains carry a non-functional Mx1 gene whereas humans have a functional MX1 gene. Most studies in mice have been performed with strains carrying a non-functional Mx1 gene. It is therefore very important to investigate the host response in mouse strains with a functional Mx1 gene. RESULTS Here, we analyzed the host response to influenza virus infections in two congenic mouse strains carrying the functional Mx1 gene from the A2G strain. B6.A2G-Mx1r/r(B6-Mx1r/r) mice are highly resistant to influenza A virus (IAV) H1N1 infections. On the other hand, D2(B6).A2G-Mx1r/r(D2-Mx1r/r) mice, although carrying a functional Mx1 gene, were highly susceptible, exhibited rapid weight loss, and died. We performed gene expression analysis using RNAseq from infected lungs at days 3 and 5 post-infection (p.i.) of both mouse strains to identify genes and pathways that were differentially expressed between the two mouse strains. The susceptible D2-Mx1r/r mice showed a high viral replication already at day 3 p.i. and exhibited a much higher number of differentially expressed genes (DEGs) and many DEGs had elevated expression levels compared to B6-Mx1r/r mice. On the other hand, some DEGs were specifically up-regulated only in B6-Mx1r/r mice at day 3 p.i., many of which were related to host immune response functions. CONCLUSIONS From these results, we conclude that at early times of infection, D2-Mx1r/r mice showed a very high and rapid replication of the virus, which resulted in lung damage and a hyperinflammatory response leading to death. We hypothesize that the activation of certain immune response genes was missing and that others, especially Mx1, were expressed at a time in D2-Mx1r/r mice when the virus had already massively spread in the lung and were thus not able anymore to protect them from severe disease. Our study represents an important addition to previously published studies in mouse models and contributes to a better understanding of the molecular pathways and genes that protect against severe influenza disease.
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Affiliation(s)
- Silke Bergmann
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Linda Brunotte
- Institute of Virology Münster, University of Münster, Von-Esmarch-Straße 56, 48149, Münster, Germany
| | - Klaus Schughart
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA.
- Institute of Virology Münster, University of Münster, Von-Esmarch-Straße 56, 48149, Münster, Germany.
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7
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Deng X, Gantner P, Forestell J, Pagliuzza A, Brunet‐Ratnasingham E, Durand M, Kaufmann DE, Chomont N, Craig M. Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity. Clin Transl Immunology 2023; 12:e1468. [PMID: 38020729 PMCID: PMC10666810 DOI: 10.1002/cti2.1468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning. Methods In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19+ patients. Results Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients. Conclusion Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.
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Affiliation(s)
- Xiaoyan Deng
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
- Département de mathématiques et de statistiqueUniversité de MontréalMontréalQCCanada
| | - Pierre Gantner
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Julia Forestell
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
| | - Amélie Pagliuzza
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Elsa Brunet‐Ratnasingham
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Madeleine Durand
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
| | - Daniel E Kaufmann
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Centre hospitalier de l'Université de Montréal (CHUM)MontréalQCCanada
- Département de MédecineUniversité de MontréalMontréalQCCanada
- Division of Infectious Diseases, Department of MedicineUniversity Hospital and University of LausanneLausanneSwitzerland
| | - Nicolas Chomont
- Research Centre of the Centre Hospitalier de l'Université de Montréal (CRCHUM)MontréalQCCanada
- Département de Microbiologie, Infectiologie et ImmunologieUniversité de MontréalMontréalQCCanada
| | - Morgan Craig
- Research Centre of the Centre Hospitalier Universitaire Sainte‐JustineMontréalQCCanada
- Département de mathématiques et de statistiqueUniversité de MontréalMontréalQCCanada
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8
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Phan T, Zitzmann C, Chew KW, Smith DM, Daar ES, Wohl DA, Eron JJ, Currier JS, Hughes MD, Choudhary MC, Deo R, Li JZ, Ribeiro RM, Ke R, Perelson AS. Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557679. [PMID: 37745410 PMCID: PMC10515893 DOI: 10.1101/2023.09.14.557679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.
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Affiliation(s)
- Tin Phan
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Carolin Zitzmann
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Kara W. Chew
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Davey M. Smith
- Department of Medicine, University of California, San Diego, CA, USA
| | - Eric S. Daar
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David A. Wohl
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Joseph J. Eron
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Judith S. Currier
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | | | - Manish C. Choudhary
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Rinki Deo
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan Z. Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruy M. Ribeiro
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ruian Ke
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Alan S. Perelson
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
- Santa Fe Institute, Santa Fe, NM, USA
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9
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Azam MS, Islam MN, Wahiduzzaman M, Alam M, Dhrubo AAK. Antiviral foods in the battle against viral infections: Understanding the molecular mechanism. Food Sci Nutr 2023; 11:4444-4459. [PMID: 37576049 PMCID: PMC10420791 DOI: 10.1002/fsn3.3454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 08/15/2023] Open
Abstract
Viruses produce a variety of illnesses, which may also cause acute respiratory syndrome. All viral infections, including COVID-19, are associated with the strength of the immune system. Till now, traditional medicine or vaccines for most viral diseases have not been effective. Antiviral and immune-boosting diets may provide defense against viral diseases by lowering the risk of infection and assisting rapid recovery. The purpose of this review was to gather, analyze, and present data based on scientific evidence in order to provide an overview of the mechanistic insights of antiviral bioactive metabolites. We have covered a wide range of food with antiviral properties in this review, along with their potential mechanism of action against viral infections. Additionally, the opportunities and challenges of using antiviral food have been critically reviewed. Bioactive plant compounds, not only help in maintaining the body's normal physiological mechanism and good health but are also essential for improving the body's immunity and therefore can be effective against viral diseases. These agents fight viral diseases either by incorporating the body's defense mechanism or by enhancing the cell's immune system. Regular intake of antiviral foods may prevent future pandemic and consumption of these antiviral agents with traditional medicine may reduce the severity of viral diseases. Therefore, the synergistic effect of antiviral foods and medication needs to be investigated.
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Affiliation(s)
- Md. Shofiul Azam
- Department of Food EngineeringDhaka University of Engineering & TechnologyGazipurBangladesh
| | - Md. Nahidul Islam
- Department of Agro‐ProcessingBangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipurBangladesh
- Institute of Food Safety and ProcessingBangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipurBangladesh
| | - Md. Wahiduzzaman
- Bio‐Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS‐MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of Sciences, Chinese Academy of SciencesShanghaiChina
| | - Mahabub Alam
- Department of Food Engineering and Tea TechnologyShahjalal University of Science and TechnologySylhetBangladesh
| | - Akib Atique Khan Dhrubo
- Department of Chemical EngineeringDhaka University of Engineering & TechnologyGazipurBangladesh
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10
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Perelson AS, Ribeiro RM, Phan T. An explanation for SARS-CoV-2 rebound after Paxlovid treatment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.30.23290747. [PMID: 37398088 PMCID: PMC10312846 DOI: 10.1101/2023.05.30.23290747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
In a fraction of SARS-CoV-2 infected individuals treated with the oral antiviral Paxlovid, the virus rebounds following treatment. The mechanism driving rebound is not understood. Here, we show that viral dynamic models based on the hypothesis that Paxlovid treatment near the time of symptom onset halts the depletion of target cells, but may not fully eliminate the virus, which can lead to viral rebound. We also show that the occurrence of viral rebound is sensitive to model parameters, and the time treatment is initiated, which may explain why only a fraction of individuals develop viral rebound. Finally, the models are used to test the therapeutic effects of two alternative treatment schemes. These findings also provide a possible explanation for rebounds following other antiviral treatments for SARS-CoV-2.
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Affiliation(s)
- Alan S. Perelson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87544 USA
- Santa Fe Institute, Santa Fe, NM 87501 USA
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87544 USA
| | - Tin Phan
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87544 USA
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11
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Pinky L, DeAguero JR, Remien CH, Smith AM. How Interactions during Viral-Viral Coinfection Can Shape Infection Kinetics. Viruses 2023; 15:1303. [PMID: 37376603 PMCID: PMC10301061 DOI: 10.3390/v15061303] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20-30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.
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Affiliation(s)
- Lubna Pinky
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Joseph R. DeAguero
- Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID 83844, USA
| | - Christopher H. Remien
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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12
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Pinky L, DeAguero JR, Remien CH, Smith AM. How Interactions During Viral-Viral Coinfection Can Shape Infection Kinetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.05.535744. [PMID: 37066297 PMCID: PMC10104040 DOI: 10.1101/2023.04.05.535744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Respiratory virus infections are a leading cause of disease worldwide with multiple viruses detected in 20-30% of cases and several viruses simultaneously circulating. Some infections with viral copathogens have been shown to result in reduced pathogenicity while other virus pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral-viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV) followed by influenza A virus (IAV) after 3 days. The results suggested that IAV reduced the rate of RSV production while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios not examined experimentally, including different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis showed that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to slower clearance of IAV infected cells by the other viruses. On the contrary, the improved outcome when IAV followed RV could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral-viral coinfections in this way provides new insights about how viral-viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.
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13
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Farrell A, Phan T, Brooke CB, Koelle K, Ke R. Semi-infectious particles contribute substantially to influenza virus within-host dynamics when infection is dominated by spatial structure. Virus Evol 2023; 9:vead020. [PMID: 37538918 PMCID: PMC10395763 DOI: 10.1093/ve/vead020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 03/01/2023] [Accepted: 03/17/2023] [Indexed: 08/05/2023] Open
Abstract
Influenza is an ribonucleic acid virus with a genome that comprises eight segments. Experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models did not explicitly consider SIPs and largely ignore the potential effects of coinfection during virus infection. Here, we constructed and analyzed two distinct models explicitly keeping track of SIPs and coinfection: one without spatial structure and the other implicitly considering spatial structure. While the model without spatial structure fails to reproduce key aspects of within-host influenza virus dynamics, we found that the model implicitly considering the spatial structure of the infection process makes predictions that are consistent with biological observations, highlighting the crucial role that spatial structure plays during an influenza infection. This model predicts two phases of viral growth prior to the viral peak: a first phase driven by fully infectious particles at the initiation of infection followed by a second phase largely driven by coinfections of fully infectious particles and SIPs. Fitting this model to two sets of data, we show that SIPs can contribute substantially to viral load during infection. Overall, the model provides a new interpretation of the in vivo exponential viral growth observed in experiments and a mechanistic explanation for why the production of large numbers of SIPs does not strongly impede viral growth. Being simple and predictive, our model framework serves as a useful tool to understand coinfection dynamics in spatially structured acute viral infections.
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Affiliation(s)
| | - Tin Phan
- T-6, Theoretical Biology and Biophysics, Los Alamos, NM 87545, USA
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14
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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15
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Taylor MK, Williams EP, Xue Y, Jenjaroenpun P, Wongsurawat T, Smith AP, Smith AM, Parvathareddy J, Kong Y, Vogel P, Cao X, Reichard W, Spruill-Harrell B, Samarasinghe AE, Nookaew I, Fitzpatrick EA, Smith MD, Aranha M, Smith JC, Jonsson CB. Dissecting Phenotype from Genotype with Clinical Isolates of SARS-CoV-2 First Wave Variants. Viruses 2023; 15:611. [PMID: 36992320 PMCID: PMC10059853 DOI: 10.3390/v15030611] [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: 01/12/2023] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
The emergence and availability of closely related clinical isolates of SARS-CoV-2 offers a unique opportunity to identify novel nonsynonymous mutations that may impact phenotype. Global sequencing efforts show that SARS-CoV-2 variants have emerged and then been replaced since the beginning of the pandemic, yet we have limited information regarding the breadth of variant-specific host responses. Using primary cell cultures and the K18-hACE2 mouse, we investigated the replication, innate immune response, and pathology of closely related, clinical variants circulating during the first wave of the pandemic. Mathematical modeling of the lung viral replication of four clinical isolates showed a dichotomy between two B.1. isolates with significantly faster and slower infected cell clearance rates, respectively. While isolates induced several common immune host responses to infection, one B.1 isolate was unique in the promotion of eosinophil-associated proteins IL-5 and CCL11. Moreover, its mortality rate was significantly slower. Lung microscopic histopathology suggested further phenotypic divergence among the five isolates showing three distinct sets of phenotypes: (i) consolidation, alveolar hemorrhage, and inflammation, (ii) interstitial inflammation/septal thickening and peribronchiolar/perivascular lymphoid cells, and (iii) consolidation, alveolar involvement, and endothelial hypertrophy/margination. Together these findings show divergence in the phenotypic outcomes of these clinical isolates and reveal the potential importance of nonsynonymous mutations in nsp2 and ORF8.
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Affiliation(s)
- Mariah K. Taylor
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan P. Williams
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Yi Xue
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Piroon Jenjaroenpun
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Thidathip Wongsurawat
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Amanda P. Smith
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
| | - Amber M. Smith
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Jyothi Parvathareddy
- Regional Biocontainment Laboratory, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Ying Kong
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Peter Vogel
- Veterinary Pathology Core Laboratory, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Xueyuan Cao
- Department of Health Promotion and Disease Prevention, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Walter Reichard
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Briana Spruill-Harrell
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Amali E. Samarasinghe
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
| | - Intawat Nookaew
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Elizabeth A. Fitzpatrick
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Micholas Dean Smith
- Center for Molecular Biophysics, University of Tennessee-Oak Ridge National Laboratory, Knoxville, TN 37996, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Michelle Aranha
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Jeremy C. Smith
- Center for Molecular Biophysics, University of Tennessee-Oak Ridge National Laboratory, Knoxville, TN 37996, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Colleen B. Jonsson
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Regional Biocontainment Laboratory, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
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16
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Waghmare A, Krantz EM, Baral S, Vasquez E, Loeffelholz T, Chung EL, Pandey U, Kuypers J, Duke ER, Jerome KR, Greninger AL, Reeves DB, Hladik F, Cardozo-Ojeda EF, Boeckh M, Schiffer JT. Reliability of Self-Sampling for Accurate Assessment of Respiratory Virus Viral and Immunologic Kinetics. J Infect Dis 2022; 226:278-286. [PMID: 32710762 PMCID: PMC7454707 DOI: 10.1093/infdis/jiaa451] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/20/2020] [Indexed: 12/16/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrates the need for accurate and convenient approaches to diagnose and therapeutically monitor respiratory viral infections. We demonstrated that self-sampling with mid-nasal foam swabs is well-tolerated and provides quantitative viral output concordant with flocked swabs. Using longitudinal home-based self-sampling, we demonstrate that nasal cytokine levels correlate and cluster according to immune cell of origin. Periods of stable viral loads are followed by rapid elimination, which could be coupled with cytokine expansion and contraction. Nasal foam swab self-sampling at home provides a precise, mechanistic readout of respiratory virus shedding and local immune responses.
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Affiliation(s)
- Alpana Waghmare
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
- Center for Clinical and Translational Research, Seattle Children’s Research Institute, Seattle, Washington, USA
| | - Elizabeth M Krantz
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Subhasish Baral
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Emma Vasquez
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Tillie Loeffelholz
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - E Lisa Chung
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Urvashi Pandey
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Obstetrics and Gynecology, University of Washington, Seattle, Washington, USA
| | - Jane Kuypers
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA
| | - Elizabeth R Duke
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Keith R Jerome
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA
| | - Alexander L Greninger
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA
| | - Daniel B Reeves
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Florian Hladik
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Obstetrics and Gynecology, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - E Fabian Cardozo-Ojeda
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael Boeckh
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Joshua T Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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17
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Smith AP, Lane LC, Ramirez Zuniga I, Moquin DM, Vogel P, Smith AM. Increased virus dissemination leads to enhanced lung injury but not inflammation during influenza-associated secondary bacterial infection. FEMS MICROBES 2022; 3:xtac022. [PMID: 37332507 PMCID: PMC10117793 DOI: 10.1093/femsmc/xtac022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/19/2022] [Accepted: 07/21/2022] [Indexed: 09/08/2023] Open
Abstract
Secondary bacterial infections increase influenza-related morbidity and mortality, particularly if acquired after 5-7 d from the viral onset. Synergistic host responses and direct pathogen-pathogen interactions are thought to lead to a state of hyperinflammation, but the kinetics of the lung pathology have not yet been detailed, and identifying the contribution of different mechanisms to disease is difficult because these may change over time. To address this gap, we examined host-pathogen and lung pathology dynamics following a secondary bacterial infection initiated at different time points after influenza within a murine model. We then used a mathematical approach to quantify the increased virus dissemination in the lung, coinfection time-dependent bacterial kinetics, and virus-mediated and postbacterial depletion of alveolar macrophages. The data showed that viral loads increase regardless of coinfection timing, which our mathematical model predicted and histomorphometry data confirmed was due to a robust increase in the number of infected cells. Bacterial loads were dependent on the time of coinfection and corresponded to the level of IAV-induced alveolar macrophage depletion. Our mathematical model suggested that the additional depletion of these cells following the bacterial invasion was mediated primarily by the virus. Contrary to current belief, inflammation was not enhanced and did not correlate with neutrophilia. The enhanced disease severity was correlated to inflammation, but this was due to a nonlinearity in this correlation. This study highlights the importance of dissecting nonlinearities during complex infections and demonstrated the increased dissemination of virus within the lung during bacterial coinfection and simultaneous modulation of immune responses during influenza-associated bacterial pneumonia.
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Affiliation(s)
- Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Lindey C Lane
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ivan Ramirez Zuniga
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
| | - David M Moquin
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Peter Vogel
- Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, United States
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18
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Alexandre M, Marlin R, Prague M, Coleon S, Kahlaoui N, Cardinaud S, Naninck T, Delache B, Surenaud M, Galhaut M, Dereuddre-Bosquet N, Cavarelli M, Maisonnasse P, Centlivre M, Lacabaratz C, Wiedemann A, Zurawski S, Zurawski G, Schwartz O, Sanders RW, Le Grand R, Levy Y, Thiébaut R. Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection. eLife 2022; 11:75427. [PMID: 35801637 PMCID: PMC9282856 DOI: 10.7554/elife.75427] [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: 11/09/2021] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.
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Affiliation(s)
- Marie Alexandre
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
| | - Romain Marlin
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Mélanie Prague
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
| | - Severin Coleon
- Vaccine Research Institute, Inserm U955, Créteil, France
| | - Nidhal Kahlaoui
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | - Thibaut Naninck
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Benoit Delache
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | - Mathilde Galhaut
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Nathalie Dereuddre-Bosquet
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Mariangela Cavarelli
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Pauline Maisonnasse
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | | | | | - Sandra Zurawski
- Baylor Scott and White Research Institute, Dallas, United States
| | - Gerard Zurawski
- Baylor Scott and White Research Institute, Dallas, United States
| | | | - Rogier W Sanders
- Department of Medical Microbiology, University of Amsterdam, Amsterdam, Netherlands
| | - Roger Le Grand
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Yves Levy
- Vaccine Research Institute, Inserm U955, Créteil, France
| | - Rodolphe Thiébaut
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
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19
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Hofflich B, Lunardhi A, Sunku N, Tsujimoto J, Cauwenberghs G. Modeling the Viral Kinetics of Influenza A During Infection in Humans. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2244-2247. [PMID: 36086157 DOI: 10.1109/embc48229.2022.9871075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This study explores the natural control system in the body for responding to exposure to the Influenza A virus. More specifically, it delves into the development of a model to simulate the responses of target uninfected cell counts, infected cell counts, and viral titers. There are two particular models of interest: a delayed model that incorporates the brief inactive period for newly infected cells, and a non-delayed model reflecting only infected cells without delay after initial infection. Both models are commonly used in the literature and the benefits of each model are studied and explained. We generate Simulink models for both the delayed and non-delayed sets of ordinary differential equations (ODEs) to simulate responses to different viral titer impulses. Additionally, this study aims to extrapolate these models to the case for a vaccinated individual. To do this, we modify the viral clearance rate and infected cell death rate of our initial model to account for the improved immune response generated by vaccines.
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20
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Goyal A, Duke ER, Cardozo-Ojeda EF, Schiffer JT. Modeling explains prolonged SARS-CoV-2 nasal shedding relative to lung shedding in remdesivir treated rhesus macaques. iScience 2022; 25:104448. [PMID: 35634576 PMCID: PMC9130309 DOI: 10.1016/j.isci.2022.104448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/19/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the hypotheses that: 1) in contrast to nasal passages viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur due to a blunting of peak viral load which decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lung.
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Affiliation(s)
- Ashish Goyal
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Elizabeth R Duke
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle
| | | | - Joshua T Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle.,Clinical Research Division, Fred Hutchinson Cancer Research Center
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21
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Panatto D, Orsi A, Bruzzone B, Ricucci V, Fedele G, Reiner G, Giarratana N, Domnich A, Icardi G. Efficacy of the Sentinox Spray in Reducing Viral Load in Mild COVID-19 and Its Virucidal Activity against Other Respiratory Viruses: Results of a Randomized Controlled Trial and an In Vitro Study. Viruses 2022; 14:1033. [PMID: 35632774 PMCID: PMC9144724 DOI: 10.3390/v14051033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/10/2022] [Accepted: 05/11/2022] [Indexed: 01/25/2023] Open
Abstract
Sentinox (STX) is an acid-oxidizing solution containing hypochlorous acid in spray whose virucidal activity against SARS-CoV-2 has been demonstrated. In this paper, results of a randomized controlled trial (RCT) on the efficacy of STX in reducing viral load in mild COVID-19 patients (NCT04909996) and a complementary in vitro study on its activity against different respiratory viruses are reported. In the RCT, 57 patients were randomized (1:1:1) to receive STX three (STX-3) or five (STX-5) times/day plus standard therapy or standard therapy only (controls). Compared with controls, the log10 load reduction in groups STX-3 and STX-5 was 1.02 (p = 0.14) and 0.18 (p = 0.80), respectively. These results were likely driven by outliers with extreme baseline viral loads. When considering subjects with baseline cycle threshold values of 20-30, STX-3 showed a significant (p = 0.016) 2.01 log10 reduction. The proportion of subjects that turned negative by the end of treatment (day 5) was significantly higher in the STX-3 group than in controls, suggesting a shorter virus clearance time. STX was safe and well-tolerated. In the in vitro study, ≥99.9% reduction in titers against common respiratory viruses was observed. STX is a safe device with large virucidal spectrum and may reduce viral loads in mild COVID-19 patients.
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Affiliation(s)
- Donatella Panatto
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy; (D.P.); (A.O.); (G.I.)
| | - Andrea Orsi
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy; (D.P.); (A.O.); (G.I.)
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (B.B.); (V.R.)
| | - Bianca Bruzzone
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (B.B.); (V.R.)
| | - Valentina Ricucci
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (B.B.); (V.R.)
| | | | - Giorgio Reiner
- APR Applied Pharma Research SA, via Corti 5, CH-6828 Balerna, Switzerland; (G.R.); (N.G.)
| | - Nadia Giarratana
- APR Applied Pharma Research SA, via Corti 5, CH-6828 Balerna, Switzerland; (G.R.); (N.G.)
| | - Alexander Domnich
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (B.B.); (V.R.)
| | - Giancarlo Icardi
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy; (D.P.); (A.O.); (G.I.)
- Hygiene Unit, San Martino Policlinico Hospital-IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy; (B.B.); (V.R.)
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22
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Ke R, Martinez PP, Smith RL, Gibson LL, Mirza A, Conte M, Gallagher N, Luo CH, Jarrett J, Zhou R, Conte A, Liu T, Farjo M, Walden KKO, Rendon G, Fields CJ, Wang L, Fredrickson R, Edmonson DC, Baughman ME, Chiu KK, Choi H, Scardina KR, Bradley S, Gloss SL, Reinhart C, Yedetore J, Quicksall J, Owens AN, Broach J, Barton B, Lazar P, Heetderks WJ, Robinson ML, Mostafa HH, Manabe YC, Pekosz A, McManus DD, Brooke CB. Daily longitudinal sampling of SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness. Nat Microbiol 2022; 7:640-652. [PMID: 35484231 PMCID: PMC9084242 DOI: 10.1038/s41564-022-01105-z] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/15/2022] [Indexed: 02/07/2023]
Abstract
The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimated viral expansion and clearance rates and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to 'superspreading'. Viral genome loads often peaked days earlier in saliva than in nasal swabs, indicating strong tissue compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of Alpha (B.1.1.7) and previously circulating non-variant-of-concern viruses were mostly indistinguishable, indicating that the enhanced transmissibility of this variant cannot be explained simply by higher viral loads or delayed clearance. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.
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Affiliation(s)
- Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Pamela P Martinez
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca L Smith
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Laura L Gibson
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA
| | - Agha Mirza
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Madison Conte
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nicholas Gallagher
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chun Huai Luo
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Junko Jarrett
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ruifeng Zhou
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abigail Conte
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tongyu Liu
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mireille Farjo
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Kimberly K O Walden
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Gloria Rendon
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher J Fields
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Leyi Wang
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Richard Fredrickson
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Darci C Edmonson
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Melinda E Baughman
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Karen K Chiu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hannah Choi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Kevin R Scardina
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shannon Bradley
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Stacy L Gloss
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Crystal Reinhart
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jagadeesh Yedetore
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jessica Quicksall
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alyssa N Owens
- Center for Clinical and Translational Research, University of Massachusetts Medical School, Worcester, MA, USA
| | - John Broach
- UMass Memorial Medical Center, Worcester, MA, USA
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bruce Barton
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, MA, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
| | - Peter Lazar
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, MA, USA
| | - William J Heetderks
- National Institute for Biomedical Imaging and Bioengineering, Bethesda, MD, USA
| | - Matthew L Robinson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Heba H Mostafa
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David D McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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23
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Day JD, Park S, Ranard BL, Singh H, Chow CC, Vodovotz Y. Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model. Front Immunol 2021; 12:754127. [PMID: 34777366 PMCID: PMC8582279 DOI: 10.3389/fimmu.2021.754127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/04/2021] [Indexed: 01/08/2023] Open
Abstract
COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed “patient archetypes”, whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.
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Affiliation(s)
- Judy D Day
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States.,Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, United States
| | - Soojin Park
- Department of Neurology & Division of Critical Care and Hospital Neurology, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States.,Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| | - Benjamin L Ranard
- Program for Hospital and Intensive Care Informatics, Department of Neurology, Columbia University College of Physicians and Surgeons, New York, NY, United States.,Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Columbia University College of Physicians and Surgeons, New York Presbyterian Hospital - Columbia University Irving Medical Center, New York, NY, United States
| | - Harinder Singh
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Carson C Chow
- Mathematical Biology Section, Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, United States
| | - Yoram Vodovotz
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States.,Center for Inflammation and Regeneration Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States
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24
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Contreras C, Newby JM, Hillen T. Personalized Virus Load Curves for Acute Viral Infections. Viruses 2021; 13:1815. [PMID: 34578396 PMCID: PMC8472998 DOI: 10.3390/v13091815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/09/2021] [Accepted: 09/03/2021] [Indexed: 12/11/2022] Open
Abstract
We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.
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Affiliation(s)
- Carlos Contreras
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jay M. Newby
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
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25
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Quantifying dose-, strain-, and tissue-specific kinetics of parainfluenza virus infection. PLoS Comput Biol 2021; 17:e1009299. [PMID: 34383757 PMCID: PMC8384156 DOI: 10.1371/journal.pcbi.1009299] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 08/24/2021] [Accepted: 07/23/2021] [Indexed: 11/25/2022] Open
Abstract
Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection hospitalization in children, yet little is known about how dose, strain, tissue tropism, and individual heterogeneity affects the processes driving growth and clearance kinetics. Longitudinal measurements are possible by using reporter Sendai viruses, the murine counterpart of HPIV 1, that express luciferase, where the insertion location yields a wild-type (rSeV-luc(M-F*)) or attenuated (rSeV-luc(P-M)) phenotype. Bioluminescence from individual animals suggests that there is a rapid increase in expression followed by a peak, biphasic clearance, and resolution. However, these kinetics vary between individuals and with dose, strain, and whether the infection was initiated in the upper and/or lower respiratory tract. To quantify the differences, we translated the bioluminescence measurements from the nasopharynx, trachea, and lung into viral loads and used a mathematical model together a nonlinear mixed effects approach to define the mechanisms distinguishing each scenario. The results confirmed a higher rate of virus production with the rSeV-luc(M-F*) virus compared to its attenuated counterpart, and suggested that low doses result in disproportionately fewer infected cells. The analyses indicated faster infectivity and infected cell clearance rates in the lung and that higher viral doses, and concomitantly higher infected cell numbers, resulted in more rapid clearance. This parameter was also highly variable amongst individuals, which was particularly evident during infection in the lung. These critical differences provide important insight into distinct HPIV dynamics, and show how bioluminescence data can be combined with quantitative analyses to dissect host-, virus-, and dose-dependent effects. Human parainfluenza viruses (HPIVs) cause acute respiratory infections and can lead to the hospitalization of children. HPIV infection severity may vary due to dose, strain, patient, and whether the infection initiates within the upper or lower respiratory tract. There is a need to determine how the rates of virus spread and clearance change in different infection scenarios in order to better understand varying clinical manifestations. The significance of our research is in identifying the dominant mechanisms driving strain-, dose-, and tissue-specific HPIV infection kinetics, and in pairing bioluminescence data with quantitative analyses to determine how the same virus can yield patient-specific outcomes. This work enhances our understanding of HPIV infection and broadens our knowledge viral dynamics in the upper and lower respiratory tracts.
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26
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Wertheim KY, Puniya BL, La Fleur A, Shah AR, Barberis M, Helikar T. A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Comput Biol 2021; 17:e1009209. [PMID: 34343169 PMCID: PMC8376204 DOI: 10.1371/journal.pcbi.1009209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/19/2021] [Accepted: 06/23/2021] [Indexed: 12/24/2022] Open
Abstract
Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.
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Affiliation(s)
- Kenneth Y. Wertheim
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- Department of Computer Science and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Alyssa La Fleur
- Department of Biochemistry, Department of Mathematics and Computer Science, Whitworth University, Spokane, Washington, United States of America
| | - Ab Rauf Shah
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail: , (MB); (TH)
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- * E-mail: , (MB); (TH)
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27
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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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28
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Jenner AL, Aogo RA, Alfonso S, Crowe V, Deng X, Smith AP, Morel PA, Davis CL, Smith AM, Craig M. COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes. PLoS Pathog 2021; 17:e1009753. [PMID: 34260666 PMCID: PMC8312984 DOI: 10.1371/journal.ppat.1009753] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/26/2021] [Accepted: 06/24/2021] [Indexed: 12/11/2022] Open
Abstract
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.
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Affiliation(s)
- Adrianne L. Jenner
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Rosemary A. Aogo
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Sofia Alfonso
- Department of Physiology, McGill University, Montréal, Québec, Canada
| | - Vivienne Crowe
- Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada
| | - Xiaoyan Deng
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
| | - Amanda P. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Penelope A. Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Courtney L. Davis
- Natural Science Division, Pepperdine University, Malibu, California, United States of America
| | - Amber M. Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Morgan Craig
- Sainte-Justine University Hospital Research Centre, Montréal, Québec, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada
- Department of Physiology, McGill University, Montréal, Québec, Canada
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29
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Perelson AS, Ke R. Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics. Clin Pharmacol Ther 2021; 109:829-840. [PMID: 33410134 DOI: 10.1002/cpt.2160] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.
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Affiliation(s)
- Alan S Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ruian Ke
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
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30
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Gjini E, Paupério FFS, Ganusov VV. Treatment timing shifts the benefits of short and long antibiotic treatment over infection. Evol Med Public Health 2020; 2020:249-263. [PMID: 33376597 PMCID: PMC7750949 DOI: 10.1093/emph/eoaa033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/19/2020] [Indexed: 12/13/2022] Open
Abstract
Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of 'aggression' have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual's infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be 'short and strong', while late optimal treatments tend to be 'mild and long'. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans. Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, "strong and short" therapy performs better, while if treatment begins at higher bacterial densities, a "mild and long" course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.
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Affiliation(s)
- Erida Gjini
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal
| | - Francisco F S Paupério
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal
- Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal
| | - Vitaly V Ganusov
- Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA
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31
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Goyal A, Cardozo-Ojeda EF, Schiffer JT. Potency and timing of antiviral therapy as determinants of duration of SARS-CoV-2 shedding and intensity of inflammatory response. SCIENCE ADVANCES 2020; 6:sciadv.abc7112. [PMID: 33097472 DOI: 10.1101/2020.04.10.20061325] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 10/07/2020] [Indexed: 05/27/2023]
Abstract
To affect the COVID-19 pandemic, lifesaving antiviral therapies must be identified. The number of clinical trials that can be performed is limited. We developed mathematical models to project multiple therapeutic approaches. Our models recapitulate off-treatment viral dynamics and predict a three-phase immune response. Simulated treatment with remdesivir, selinexor, neutralizing antibodies, or cellular immunotherapy demonstrates that rapid viral elimination is possible if in vivo potency is sufficiently high. Therapies dosed soon after peak viral load when symptoms develop may decrease shedding duration and immune response intensity but have little effect on viral area under the curve (AUC), which is driven by high early viral loads. Potent therapy dosed before viral peak during presymptomatic infection could lower AUC. Drug resistance may emerge with a moderately potent agent dosed before viral peak. Our results support early treatment for COVID-19 if shedding duration, not AUC, is most predictive of clinical severity.
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Affiliation(s)
- Ashish Goyal
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - E Fabian Cardozo-Ojeda
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Joshua T Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
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32
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Goyal A, Cardozo-Ojeda EF, Schiffer JT. Potency and timing of antiviral therapy as determinants of duration of SARS-CoV-2 shedding and intensity of inflammatory response. SCIENCE ADVANCES 2020; 6:eabc7112. [PMID: 33097472 PMCID: PMC7679107 DOI: 10.1126/sciadv.abc7112] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 10/07/2020] [Indexed: 05/18/2023]
Abstract
To affect the COVID-19 pandemic, lifesaving antiviral therapies must be identified. The number of clinical trials that can be performed is limited. We developed mathematical models to project multiple therapeutic approaches. Our models recapitulate off-treatment viral dynamics and predict a three-phase immune response. Simulated treatment with remdesivir, selinexor, neutralizing antibodies, or cellular immunotherapy demonstrates that rapid viral elimination is possible if in vivo potency is sufficiently high. Therapies dosed soon after peak viral load when symptoms develop may decrease shedding duration and immune response intensity but have little effect on viral area under the curve (AUC), which is driven by high early viral loads. Potent therapy dosed before viral peak during presymptomatic infection could lower AUC. Drug resistance may emerge with a moderately potent agent dosed before viral peak. Our results support early treatment for COVID-19 if shedding duration, not AUC, is most predictive of clinical severity.
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Affiliation(s)
- Ashish Goyal
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - E Fabian Cardozo-Ojeda
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Joshua T Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
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33
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Dimas Martins A, Gjini E. Modeling Competitive Mixtures With the Lotka-Volterra Framework for More Complex Fitness Assessment Between Strains. Front Microbiol 2020; 11:572487. [PMID: 33072034 PMCID: PMC7536265 DOI: 10.3389/fmicb.2020.572487] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 08/12/2020] [Indexed: 11/13/2022] Open
Abstract
With increasing resolution of microbial diversity at the genomic level, experimental and modeling frameworks that translate such diversity into phenotypes are highly needed. This is particularly important when comparing drug-resistant with drug-sensitive pathogen strains, when anticipating epidemiological implications of microbial diversity, and when designing control measures. Classical approaches quantify differences between microbial strains using the exponential growth model, and typically report a selection coefficient for the relative fitness differential between two strains. The apparent simplicity of such approaches comes with the costs of limiting the range of biological scenarios that can be captured, and biases strain fitness estimates to polarized extremes of competitive exclusion. Here, we propose a mathematical and statistical framework based on the Lotka-Volterra model, that can capture frequency-dependent competition between microbial strains within-host and upon transmission. As a proof-of-concept, the model is applied to a previously-published dataset from in-vivo competitive mixture experiments with influenza strains in ferrets (McCaw et al., 2011). We show that for the same data, our model predicts a scenario of coexistence between strains, and supports a higher bottleneck size in the range of 35–145 virions transmitted from donor to recipient host. Thanks to its simplicity and generality, such framework could be applied to other ecological scenarios of microbial competition, enabling a more complex and nuanced view of possible outcomes between two strains, beyond competitive exclusion.
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Affiliation(s)
- Afonso Dimas Martins
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Oeiras, Portugal.,Departamento de Estatística e Investigacão Operacional, Faculdade de Ciências, Universidade de Lisbon, Lisbon, Portugal
| | - Erida Gjini
- Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ciência, Oeiras, Portugal
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Chemical kinetics of the development of coronaviral infection in the human body: Critical conditions, toxicity mechanisms, "thermoheliox", and "thermovaccination". Chem Biol Interact 2020; 329:109209. [PMID: 32750325 PMCID: PMC7395817 DOI: 10.1016/j.cbi.2020.109209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 07/08/2020] [Accepted: 07/21/2020] [Indexed: 01/01/2023]
Abstract
Kinetic modeling of the behavior of complex chemical and biochemical systems is an effective approach to study of the mechanisms of the process. A kinetic model of coronaviral infection development with a description of the dynamic behavior of the main variables, including the concentration of viral particles, affected cells, and pathogenic microflora, is proposed. Changes in the concentration of hydrogen ions in the lungs and the pH -dependence of carbonic anhydrase activity (a key breathing enzyme) are critical. A significant result is the demonstration of an acute bifurcation transition that determines life or system collapse. This transition is connected with exponential growth of concentrations of the process participants and with functioning of the key enzyme carbonic anhydrase in development of toxic effects. Physical and chemical interpretations of the therapeutic effects of the body temperature rise and the potential therapeutic effect of “thermoheliox” (respiration with a thermolized mixture of helium and oxygen) are given. The phenomenon of “thermovaccination” is predicted, which involves stimulation of the immune response by “thermoheliox”. The proposed kinetic model describes the dynamics of coronaviral infection development. Acidification and pH dependence of key enzymes is discussed as a basis of viral toxicity. An acute bifurcation transition of the system to collapse is demonstrated. The theory and experimental facts of “thermoheliox” therapy are discussed. “Thermovaccination” by “thermoheliox” is predicted.
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35
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Rogers A, Towery L, McCown A, Carlson KA. Impaired Geotaxis as a Novel Phenotype of Nora Virus Infection of Drosophila melanogaster. SCIENTIFICA 2020; 2020:1804510. [PMID: 32802552 PMCID: PMC7414366 DOI: 10.1155/2020/1804510] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/25/2020] [Accepted: 07/01/2020] [Indexed: 06/11/2023]
Abstract
Nora virus (NV) is a picorna-like virus that contains a positive-sense, single-stranded RNA genome. The virus infects Drosophila melanogaster with no known characterized phenotype. In this study, geotaxis assays and longevity analyses were used to determine if Nora virus infection affects D. melanogaster's locomotor ability. In addition, Drosophila C virus (DCV), a well-characterized D. melanogaster virus, was used as a positive control, as it has previously shown a locomotor defect in infected flies. Stocks infected with NV (NV+) and DCV (DCV+) and virus-free (NV-/DCV-) stocks were established. Over a 3-year period, approximately 2,500 virgin female flies were tested for geotaxis and longevity using Kaplan-Meier analyses, as well as the Cox Proportional Hazards regression for survivorship. There was a significant decrease in the geotaxis when the D. melanogaster flies were infected with Nora virus compared to uninfected controls, but no difference was found between DCV+ and NV+ trials. There were not significant differences in longevity between the three groups. This is the first time that a phenotype has been recorded in association with Nora virus infection. Overall, the data demonstrate that geotaxis dysfunction may be a phenotypic hallmark of Nora virus infection.
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Affiliation(s)
- Abigail Rogers
- Department of Biology, University of Nebraska, Kearney, NE 68849, USA
| | - Lesley Towery
- Department of Biology, University of Nebraska, Kearney, NE 68849, USA
| | - Amanda McCown
- Department of Biology, University of Nebraska, Kearney, NE 68849, USA
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36
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Gaur AH, McCarthy JS, Panetta JC, Dallas RH, Woodford J, Tang L, Smith AM, Stewart TB, Branum KC, Freeman BB, Patel ND, John E, Chalon S, Ost S, Heine RN, Richardson JL, Christensen R, Flynn PM, Van Gessel Y, Mitasev B, Möhrle JJ, Gusovsky F, Bebrevska L, Guy RK. Safety, tolerability, pharmacokinetics, and antimalarial efficacy of a novel Plasmodium falciparum ATP4 inhibitor SJ733: a first-in-human and induced blood-stage malaria phase 1a/b trial. THE LANCET. INFECTIOUS DISEASES 2020; 20:964-975. [PMID: 32275867 DOI: 10.1016/s1473-3099(19)30611-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/11/2019] [Accepted: 10/10/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND (+)-SJ000557733 (SJ733) is a novel, orally bioavailable inhibitor of Plasmodium falciparum ATP4. In this first-in-human and induced blood-stage malaria phase 1a/b trial, we investigated the safety, tolerability, pharmacokinetics, and antimalarial activity of SJ733 in humans. METHODS The phase 1a was a single-centre, dose-escalation, first-in-human study of SJ733 allowing modifications to dose increments and dose-cohort size on the basis of safety and pharmacokinetic results. The phase 1a took place at St Jude Children's Research Hospital and at the University of Tennessee Clinical Research Center (Memphis, TN, USA). Enrolment in more than one non-consecutive dose cohort was allowed with at least 14 days required between doses. Participants were fasted in seven dose cohorts and fed in one 600 mg dose cohort. Single ascending doses of SJ733 (75, 150, 300, 600, 900, or 1200 mg) were administered to participants, who were followed up for 14 days after SJ733 dosing. Phase 1a primary endpoints were safety, tolerability, and pharmacokinetics of SJ733, and identification of an SJ733 dose to test in the induced blood-stage malaria model. The phase 1b was a single-centre, open-label, volunteer infection study using the induced blood-stage malaria model in which fasted participants were intravenously infected with blood-stage P falciparum and subsequently treated with a single dose of SJ733. Phase 1b took place at Q-Pharm (Herston, QLD, Australia) and was initiated only after phase 1a showed that exposure exceeding the threshold minimum exposure could be safely achieved in humans. Participants were inoculated on day 0 with P falciparum-infected human erythrocytes (around 2800 parasites in the 150 mg dose cohort and around 2300 parasites in the 600 mg dose cohort), and parasitaemia was monitored before malaria inoculation, after inoculation, immediately before SJ733 dosing, and then post-dose. Participants were treated with SJ733 within 24 h of reaching 5000 parasites per mL or at a clinical score higher than 6. Phase 1b primary endpoints were calculation of a parasite reduction ratio (PRR48) and parasite clearance half-life, and safety and tolerability of SJ733 (incidence, severity, and drug-relatedness of adverse events). In both phases of the trial, SJ733 hydrochloride salt was formulated as a powder blend in capsules containing 75 mg or 300 mg for oral administration. Healthy men and women (of non-childbearing potential) aged 18-55 years were eligible for both studies. Both studies are registered with ClinicalTrials.gov (NCT02661373 for the phase 1a and NCT02867059 for the phase 1b). FINDINGS In the phase 1a, 23 healthy participants were enrolled and received one to three non-consecutive doses of SJ733 between March 14 and Dec 7, 2016. SJ733 was safe and well tolerated at all doses and in fasted and fed conditions. 119 adverse events were recorded: 54 (45%) were unrelated, 63 (53%) unlikely to be related, and two (2%) possibly related to SJ733. In the phase 1b, 17 malaria-naive, healthy participants were enrolled. Seven participants in the 150 mg dose cohort were inoculated and dosed with SJ733. Eight participants in the 600 mg dose cohort were inoculated, but two participants could not be dosed with SJ733. Two additional participants were subsequently inoculated and dosed with SJ733. SJ733 exposure increased proportional to the dose through to the 600 mg dose, then was saturable at higher doses. Fasted participants receiving 600 mg exceeded the target area under the concentration curve extrapolated to infinity (AUC0-∞) of 13 000 μg × h/L (median AUC0-∞ 24 283 [IQR 16 135-31 311] μg × h/L, median terminal half-life 17·4 h [IQR 16·1-24·0], and median timepoint at which peak plasma concentration is reached 1·0 h [0·6-1·3]), and this dose was tested in the phase 1b. All 15 participants dosed with SJ733 had at least one adverse event. Of the 172 adverse events recorded, 128 (74%) were mild. The only adverse event attributed to SJ733 was mild bilateral foot paraesthesia that lasted 3·75 h and resolved spontaneously. The most common adverse events were related to malaria. Based on parasite clearance half-life, the derived log10PRR48 and corresponding parasite clearance half-lives were 2·2 (95% CI 2·0-2·5) and 6·47 h (95% CI 5·88-7·18) for 150 mg, and 4·1 (3·7-4·4) and 3·56 h (3·29-3·88) for 600 mg. INTERPRETATION The favourable pharmacokinetic, tolerability, and safety profile of SJ733, and rapid antiparasitic effect support its development as a fast-acting component of combination antimalarial therapy. FUNDING Global Health Innovative Technology Fund, Medicines for Malaria Venture, and the American Lebanese Syrian Associated Charities.
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Affiliation(s)
- Aditya H Gaur
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA.
| | - James S McCarthy
- Department of Clinical Tropical Medicine, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - John C Panetta
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Ronald H Dallas
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - John Woodford
- Department of Clinical Tropical Medicine, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Li Tang
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Amber M Smith
- University of Tennessee Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Tracy B Stewart
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Kristen C Branum
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Burgess B Freeman
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Nehali D Patel
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | | | - Shelley Ost
- University of Tennessee Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Ryan N Heine
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Julie L Richardson
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Robbin Christensen
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Patricia M Flynn
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | | | | | | | | | - R Kiplin Guy
- University of Kentucky College of Pharmacy, University of Kentucky, Lexington, KY, USA
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Waghmare A, Krantz EM, Baral S, Vasquez E, Loeffelholz T, Chung EL, Pandey U, Kuypers J, Duke ER, Jerome KR, Greninger AL, Reeves DB, Hladik F, Cardozo-Ojeda EF, Boeckh M, Schiffer JT. Reliability of self-sampling for accurate assessment of respiratory virus viral and immunologic kinetics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.04.03.20051706. [PMID: 32511581 PMCID: PMC7276008 DOI: 10.1101/2020.04.03.20051706] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
The SARS-CoV-2 pandemic demonstrates the need for accurate and convenient approaches to diagnose and therapeutically monitor respiratory viral infections. We demonstrated that self-sampling with foam swabs at home is well-tolerated and provides quantitative viral output concordant with flocked swabs. Nasal cytokine levels correlate and cluster according to immune cell of origin. Periods of stable viral loads are followed by rapid elimination, which could be coupled with cytokine expansion and contraction using mathematical models. Nasal foam swab self-sampling at home provides a precise, mechanistic readout of respiratory virus shedding and local immune responses.
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Affiliation(s)
- Alpana Waghmare
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Pediatrics, University of Washington
- Center for Clinical and Translational Research, Seattle Children’s Research Institute
| | - Elizabeth M. Krantz
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Subhasish Baral
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Emma Vasquez
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Tillie Loeffelholz
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - E. Lisa Chung
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Urvashi Pandey
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Obstetrics and Gynecology, University of Washington
| | - Jane Kuypers
- Department of Laboratory Medicine, University of Washington
| | - Elizabeth R Duke
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Medicine, University of Washington
| | - Keith R. Jerome
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Laboratory Medicine, University of Washington
| | | | - Daniel B. Reeves
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Florian Hladik
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Obstetrics and Gynecology, University of Washington
- Department of Medicine, University of Washington
| | | | - Michael Boeckh
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Medicine, University of Washington
- Clinical Research Division, Fred Hutchinson Cancer Research Center
| | - Joshua T. Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
- Department of Medicine, University of Washington
- Clinical Research Division, Fred Hutchinson Cancer Research Center
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38
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Moore JR, Ahmed H, Manicassamy B, Garcia-Sastre A, Handel A, Antia R. Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections. Bull Math Biol 2020; 82:35. [PMID: 32125535 DOI: 10.1007/s11538-020-00711-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 02/19/2020] [Indexed: 02/05/2023]
Abstract
It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.
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Affiliation(s)
- James R Moore
- Division of Vaccines and Infectious Diseases, Fred Hutchinson Cancer Research Center, Seattle, USA.
| | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, USA
| | - Balaji Manicassamy
- Department of Microbiology and Immunology, University of Iowa School College of Medicine, Iowa City, USA
| | | | - Andreas Handel
- Epidemiology and Biostatistics, University of Georgia, Athens, USA
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, USA
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39
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Chung M, Binois M, Gramacy RB, Bardsley JM, Moquin DJ, Smith AP, Smith AM. PARAMETER AND UNCERTAINTY ESTIMATION FOR DYNAMICAL SYSTEMS USING SURROGATE STOCHASTIC PROCESSES. SIAM JOURNAL ON SCIENTIFIC COMPUTING : A PUBLICATION OF THE SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS 2019; 41:A2212-A2238. [PMID: 31749599 PMCID: PMC6867882 DOI: 10.1137/18m1213403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g., ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations. To address these issues, we propose a new method for learning parameterized dynamical systems from data. We first customize and fit a surrogate stochastic process directly to observational data, front-loading with statistical learning to respect prior knowledge (e.g., smoothness), cope with challenging data features like heteroskedasticity, heavy tails, and censoring. Then, samples of the stochastic process are used as "surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. We demonstrate its advantages on a predator-prey simulation study and on a real-world application involving within-host influenza virus infection data paired with a viral kinetic model, with comparisons to a more conventional Markov chain Monte Carlo (MCMC) based Bayesian approach.
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Affiliation(s)
- Matthias Chung
- Department of Mathematics, Computational Modeling and Data Analytics Division, Academy of Integrated Science, Virginia Tech, Blacksburg, VA 24061
| | - Mickaël Binois
- Booth School of Business, University of Chicago, Chicago, IL 60637
| | | | | | - David J Moquin
- Department of Internal Medicine, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amanda P Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
| | - Amber M Smith
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103
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40
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Koelle K, Farrell AP, Brooke CB, Ke R. Within-host infectious disease models accommodating cellular coinfection, with an application to influenza. Virus Evol 2019; 5:vez018. [PMID: 31304043 PMCID: PMC6613536 DOI: 10.1093/ve/vez018] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.
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Affiliation(s)
- Katia Koelle
- Department of Biology, Emory University, 1510 Clifton Rd #2006, Atlanta, GA, USA
| | - Alex P Farrell
- Department of Mathematics, North Carolina State University, 2311 Stinson Dr, Raleigh, NC, USA.,Department of Mathematics, University of Arizona, 617 N Santa Rita Ave, Tucson, AZ, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, 601 S. Goodwin Ave, IL, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 601 S. Goodwin Ave, IL, USA
| | - Ruian Ke
- Department of Mathematics, North Carolina State University, 2311 Stinson Dr, Raleigh, NC, USA.,Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
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Peter S, Hölzer M, Lamkiewicz K, di Fenizio PS, Al Hwaeer H, Marz M, Schuster S, Dittrich P, Ibrahim B. Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis. Viruses 2019; 11:E449. [PMID: 31100972 PMCID: PMC6563504 DOI: 10.3390/v11050449] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/08/2019] [Accepted: 05/11/2019] [Indexed: 12/23/2022] Open
Abstract
Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model's organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area.
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Affiliation(s)
- Stephan Peter
- Ernst-Abbe University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany.
- Bio Systems Analysis Group, Department of Mathematics and Computer Science, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Martin Hölzer
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany.
| | - Kevin Lamkiewicz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany.
| | - Pietro Speroni di Fenizio
- Bio Systems Analysis Group, Department of Mathematics and Computer Science, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Hassan Al Hwaeer
- Mathematics and Computer Applications Department, Al-Nahrain University, Al-Jadriya, Baghdad 10072, Iraq.
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany.
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany.
| | - Stefan Schuster
- Chair of Bioinformatics, Matthias-Schleiden-Institute, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Peter Dittrich
- Bio Systems Analysis Group, Department of Mathematics and Computer Science, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Bashar Ibrahim
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany.
- Chair of Bioinformatics, Matthias-Schleiden-Institute, University of Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
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42
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Handel A, Liao LE, Beauchemin CA. Progress and trends in mathematical modelling of influenza A virus infections. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2018.08.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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43
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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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Abstract
Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.
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Abstract
Influenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi-pathogen infections makes dissecting contributing mechanisms, which may be non-linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza-bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza-related infections, the novel biological insight that has been gained through modeling, the importance of model-driven experimental design, and future directions of the field.
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Affiliation(s)
- Amber M Smith
- University of Tennessee Health Science CenterMemphisTNUSA
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