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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] [What about the content of this article? (0)] [Affiliation(s)] [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: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Solodova R, Tolstykh M, Isaev T, Trushkin R, Vtorenko V, Staroverov V, Sokolov M, Podolskii V. Instrumental mechanoreceptoric palpation in renal surgery: a pilot study. Eur J Surg Oncol 2019. [DOI: 10.1016/j.ejso.2018.10.274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Solodova R, Staroverov V, Galatenko V, Galatenko A, Solodov E, Antonov A, Budanov V, Sokolov M, Sadovnichy V. Automated Detection of Heterogeneity in Medical Tactile Images. Stud Health Technol Inform 2016; 220:383-389. [PMID: 27046610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Artificial tactile sensing is a capability important for many applications and, in particular, for endoscopic surgery. A recently developed Medical Tactile Endosurgical Complex (MTEC) that is a certified and commercially available product is an efficient tool that provides such a capability. Currently the analysis of intraoperative tactile images that are registered and visualized by MTEC is performed manually by a surgeon. We show that heterogeneity detection - a key constituent of intraoperative tactile images analysis - can be efficiently automated. Such automation essentially reduces the requirement of attention retaining during the MTEC-based palpation.
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Affiliation(s)
- Rozalia Solodova
- Lomonosov Moscow State University, Laboratory of Mechanoreceptoral Diagnostics
| | | | | | - Alexey Galatenko
- Lomonosov Moscow State University, Faculty of Mechanics and Mathematics
| | - Evgeny Solodov
- Lomonosov Moscow State University, Laboratory of Mechanoreceptoral Diagnostics
| | - Alexey Antonov
- Lomonosov Moscow State University, Faculty of Mechanics and Mathematics
| | - Vladimir Budanov
- Lomonosov Moscow State University, Laboratory of Mechanoreceptoral Diagnostics
| | - Mikhail Sokolov
- Lomonosov Moscow State University, Laboratory of Mechanoreceptoral Diagnostics
| | - Victor Sadovnichy
- Lomonosov Moscow State University, Faculty of Mechanics and Mathematics
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Solodova R, Sokolov M, Galatenko V, Budanov V, Staroverov V, Sadovnichy V. Automatic robotic system of diagnosing and treatment in intensive care unit. J Crit Care 2015. [DOI: 10.1016/j.jcrc.2015.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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