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Juhász N, Bartha FA, Marzban S, Han R, Röst G. Probability of early infection extinction depends linearly on the virus clearance rate. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240903. [PMID: 39359461 PMCID: PMC11444767 DOI: 10.1098/rsos.240903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 08/21/2024] [Accepted: 08/23/2024] [Indexed: 10/04/2024]
Abstract
We provide an in silico study of stochastic viral infection extinction from a pharmacokinetical viewpoint. Our work considers a non-specific antiviral drug that increases the virus clearance rate, and we investigate the effect of this drug on early infection extinction. Infection extinction data are generated by a hybrid multiscale framework that applies both continuous and discrete mathematical approaches. The central result of our paper is the observation, analysis and explanation of a linear relationship between the virus clearance rate and the probability of early infection extinction. The derivation behind this simple relationship is given by merging different mathematical toolboxes.
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Affiliation(s)
- N Juhász
- National Laboratory for Health Security, 6720 Szeged, Hungary
- Bolyai Institute, University of Szeged, 6720 Szeged, Hungary
| | - F A Bartha
- National Laboratory for Health Security, 6720 Szeged, Hungary
- Bolyai Institute, University of Szeged, 6720 Szeged, Hungary
| | - S Marzban
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - R Han
- School of Sciences, Zhejiang University of Science and Technology, Hangzhou, 310023, People's Republic of China
| | - G Röst
- National Laboratory for Health Security, 6720 Szeged, Hungary
- Bolyai Institute, University of Szeged, 6720 Szeged, Hungary
- Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Szeged, Hungary
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Rüdiger D, Piasecka J, Küchler J, Pontes C, Laske T, Kupke SY, Reichl U. Mathematical model calibrated to in vitro data predicts mechanisms of antiviral action of the influenza defective interfering particle "OP7". iScience 2024; 27:109421. [PMID: 38523782 PMCID: PMC10959662 DOI: 10.1016/j.isci.2024.109421] [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: 08/23/2023] [Revised: 02/08/2024] [Accepted: 02/29/2024] [Indexed: 03/26/2024] Open
Abstract
Defective interfering particles (DIPs) are regarded as potent broad-spectrum antivirals. We developed a mathematical model that describes intracellular co-infection dynamics of influenza standard virus (STV) and "OP7", a new type of influenza DIP discovered recently. Based on experimental data from in vitro studies to calibrate the model and confirm its predictions, we deduce OP7's mechanisms of interference, which were yet unknown. Simulations suggest that the "superpromoter" on OP7 genomic viral RNA enhances its replication and results in a depletion of viral proteins. This reduces STV genomic RNA replication, which appears to constitute an antiviral effect. Further, a defective viral protein (M1-OP7) likely causes the deficiency of OP7's replication. It appears unable to bind to genomic viral RNAs to facilitate their nuclear export, a critical step in the viral life cycle. An improved understanding of OP7's antiviral mechanism is crucial toward application in humans as a prospective antiviral treatment strategy.
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Affiliation(s)
- Daniel Rüdiger
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
| | - Julita Piasecka
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
| | - Jan Küchler
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
| | - Carolina Pontes
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
| | - Tanja Laske
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
- Institute for Computational Systems Biology, University of Hamburg, 20148 Hamburg, Germany
| | - Sascha Y. Kupke
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
| | - Udo Reichl
- Department of Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Saxony-Anhalt, Germany
- Chair of Bioprocess Engineering, Otto-von-Guericke University, 39106 Magdeburg, Saxony-Anhalt, Germany
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Staroverov V, Galatenko A, Knyazev E, Tonevitsky A. Mathematical model explains differences in Omicron and Delta SARS-CoV-2 dynamics in Caco-2 and Calu-3 cells. PeerJ 2024; 12:e16964. [PMID: 38560455 PMCID: PMC10981414 DOI: 10.7717/peerj.16964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/26/2024] [Indexed: 04/04/2024] Open
Abstract
Within-host infection dynamics of Omicron dramatically differs from previous variants of SARS-CoV-2. However, little is still known about which parameters of virus-cell interplay contribute to the observed attenuated replication and pathogenicity of Omicron. Mathematical models, often expressed as systems of differential equations, are frequently employed to study the infection dynamics of various viruses. Adopting such models for results of in vitro experiments can be beneficial in a number of aspects, such as model simplification (e.g., the absence of adaptive immune response and innate immunity cells), better measurement accuracy, and the possibility to measure additional data types in comparison with in vivo case. In this study, we consider a refinement of our previously developed and validated model based on a system of integro-differential equations. We fit the model to the experimental data of Omicron and Delta infections in Caco-2 (human intestinal epithelium model) and Calu-3 (lung epithelium model) cell lines. The data include known information on initial conditions, infectious virus titers, and intracellular viral RNA measurements at several time points post-infection. The model accurately explains the experimental data for both variants in both cell lines using only three variant- and cell-line-specific parameters. Namely, the cell entry rate is significantly lower for Omicron, and Omicron triggers a stronger cytokine production rate (i.e., innate immune response) in infected cells, ultimately making uninfected cells resistant to the virus. Notably, differences in only a single parameter (e.g., cell entry rate) are insufficient to obtain a reliable model fit for the experimental data.
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Affiliation(s)
- Vladimir Staroverov
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
| | - Alexei Galatenko
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
| | - Evgeny Knyazev
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Alexander Tonevitsky
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Art Photonics GmbH, Berlin, Germany
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Staroverov V, Nersisyan S, Galatenko A, Alekseev D, Lukashevich S, Polyakov F, Anisimov N, Tonevitsky A. Development of a novel mathematical model that explains SARS-CoV-2 infection dynamics in Caco-2 cells. PeerJ 2023; 11:e14828. [PMID: 36748087 PMCID: PMC9899056 DOI: 10.7717/peerj.14828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023] Open
Abstract
Mathematical modeling is widely used to study within-host viral dynamics. However, to the best of our knowledge, for the case of SARS-CoV-2 such analyses were mainly conducted with the use of viral load data and for the wild type (WT) variant of the virus. In addition, only few studies analyzed models for in vitro data, which are less noisy and more reproducible. In this work we collected multiple data types for SARS-CoV-2-infected Caco-2 cell lines, including infectious virus titers, measurements of intracellular viral RNA, cell viability data and percentage of infected cells for the WT and Delta variants. We showed that standard models cannot explain some key observations given the absence of cytopathic effect in human cell lines. We propose a novel mathematical model for in vitro SARS-CoV-2 dynamics, which included explicit modeling of intracellular events such as exhaustion of cellular resources required for virus production. The model also explicitly considers innate immune response. The proposed model accurately explained experimental data. Attenuated replication of the Delta variant in Caco-2 cells could be explained by our model on the basis of just two parameters: decreased cell entry rate and increased cytokine production rate.
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Affiliation(s)
- Vladimir Staroverov
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
| | - Stepan Nersisyan
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Institute of Molecular Biology, The National Academy of Sciences of the Republic of Armenia, Yerevan, Armenia,Armenian Bioinformatics Institute (ABI), Yerevan, Armenia,Current Affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States
| | - Alexei Galatenko
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitriy Alekseev
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
| | - Sofya Lukashevich
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
| | - Fedor Polyakov
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Nikita Anisimov
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
| | - Alexander Tonevitsky
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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Jin Q, Li W, Yu W, Zeng M, Liu J, Xu P. Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19. Comput Biol Med 2022; 150:106134. [PMID: 36201886 PMCID: PMC9528635 DOI: 10.1016/j.compbiomed.2022.106134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 08/30/2022] [Accepted: 09/18/2022] [Indexed: 11/19/2022]
Abstract
COVID-19 pandemic poses a severe threat to public health. However, so far, there are no effective drugs for COVID-19. Transcriptomic changes and key genes related to Th2 cells in COVID-19 have not been reported. These genes play an important role in host interactions with SARS-COV-2 and may be used as promising target. We analyzed five COVID-19-associated GEO datasets (GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627) using the xCell algorithm and weighted gene co-expression network analysis (WGCNA). Results showed that 5 closely correlated modular genes to COVID-19 and Th2 cell enrichment levels, including purple, blue, pink, tan and turquoise, were intersected with differentially expressed genes (DEGs) and 648 shared genes were obtained. GO and KEGG pathway enrichment analyses revealed that they were enriched in cell proliferation, differentiation, and immune responses after virus infection. The most significantly enriched pathway involved the regulation of viral life cycle. Three key genes, namely CCNB1, BUB1, and UBE2C, may clarify the pathogenesis of COVID-19 associated with Th2 cells. 11 drug candidates were identified that could down-regulate three key genes using the cMAP database and demonstrated strong drugs binding energies aganist the three keygenes using molecular docking methods. BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.
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Affiliation(s)
- Qiying Jin
- Institute of Tropical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, PR China
| | - Wanxi Li
- Institute of Tropical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, PR China
| | - Wendi Yu
- Institute of Tropical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, PR China
| | - Maosen Zeng
- Institute of Tropical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, PR China
| | - Jinyuan Liu
- Basic Medical College, Guangzhou University of Chinese Medicine, Guangzhou, PR China
| | - Peiping Xu
- Institute of Tropical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, PR China.
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Carroll JE. A two-dimensional discrete delay-differential system model of viremia. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:11195-11216. [PMID: 36124587 DOI: 10.3934/mbe.2022522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A deterministic model is proposed to describe the interaction between an immune system and an invading virus whose target cells circulate in the blood. The model is a system of two ordinary first order quadratic delay-differential equations with stipulated initial conditions, whose coefficients are eventually constant, so that the system becomes autonomous. The long-term behavior of the solution is investigated with some success. In particular, we find two simple functions of the parameters of the model, whose signs often, but not always, determine whether the virus persists above a nonzero threshold in the circulation or heads toward extinction.
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Affiliation(s)
- Joseph E Carroll
- Department of Mathematics, California State Polytechnic University, Humboldt
- Department of Family Medicine, Oregon Health and Science University
- Providence/St. Joseph Eureka Family Medicine Residency
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Caldera-Crespo LA, Paidas MJ, Roy S, Schulman CI, Kenyon NS, Daunert S, Jayakumar AR. Experimental Models of COVID-19. Front Cell Infect Microbiol 2022; 11:792584. [PMID: 35096645 PMCID: PMC8791197 DOI: 10.3389/fcimb.2021.792584] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/26/2021] [Indexed: 12/20/2022] Open
Abstract
COVID-19 is the most consequential pandemic of the 21st century. Since the earliest stage of the 2019-2020 epidemic, animal models have been useful in understanding the etiopathogenesis of SARS-CoV-2 infection and rapid development of vaccines/drugs to prevent, treat or eradicate SARS-CoV-2 infection. Early SARS-CoV-1 research using immortalized in-vitro cell lines have aided in understanding different cells and receptors needed for SARS-CoV-2 infection and, due to their ability to be easily manipulated, continue to broaden our understanding of COVID-19 disease in in-vivo models. The scientific community determined animal models as the most useful models which could demonstrate viral infection, replication, transmission, and spectrum of illness as seen in human populations. Until now, there have not been well-described animal models of SARS-CoV-2 infection although transgenic mouse models (i.e. mice with humanized ACE2 receptors with humanized receptors) have been proposed. Additionally, there are only limited facilities (Biosafety level 3 laboratories) available to contribute research to aid in eventually exterminating SARS-CoV-2 infection around the world. This review summarizes the most successful animal models of SARS-CoV-2 infection including studies in Non-Human Primates (NHPs) which were found to be susceptible to infection and transmitted the virus similarly to humans (e.g., Rhesus macaques, Cynomolgus, and African Green Monkeys), and animal models that do not require Biosafety level 3 laboratories (e.g., Mouse Hepatitis Virus models of COVID-19, Ferret model, Syrian Hamster model). Balancing safety, mimicking human COVID-19 and robustness of the animal model, the Murine Hepatitis Virus-1 Murine model currently represents the most optimal model for SARS-CoV-2/COVID19 research. Exploring future animal models will aid researchers/scientists in discovering the mechanisms of SARS-CoV-2 infection and in identifying therapies to prevent or treat COVID-19.
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Affiliation(s)
- Luis A Caldera-Crespo
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, United States
- St. George's University Graduate Medical Education Program, University Centre Grenada, West Indies, Grenada
| | - Michael J Paidas
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Sabita Roy
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Carl I Schulman
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Norma Sue Kenyon
- Department of Microbiology & Immunology, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Biomedical Engineering, University of Miami Miller School of Medicine, Miami, FL, United States
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Sylvia Daunert
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL, United States
- Dr. JT Macdonald Foundation Biomedical Nanotechnology Institute, University of Miami Miller School of Medicine, Miami, FL, United States
- University of Miami Clinical and Translational Science Institute, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Arumugam R Jayakumar
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Miami Miller School of Medicine, Miami, FL, United States
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