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Metz-Zumaran C, Uckeley ZM, Doldan P, Muraca F, Keser Y, Lukas P, Kuropka B, Küchenhoff L, Rastgou Talemi S, Höfer T, Freund C, Cavalcanti-Adam EA, Graw F, Stanifer M, Boulant S. The population context is a driver of the heterogeneous response of epithelial cells to interferons. Mol Syst Biol 2024; 20:242-275. [PMID: 38273161 PMCID: PMC10912784 DOI: 10.1038/s44320-024-00011-2] [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: 05/19/2023] [Revised: 12/31/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.
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Affiliation(s)
- Camila Metz-Zumaran
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Zina M Uckeley
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
| | - Patricio Doldan
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Francesco Muraca
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Yagmur Keser
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
| | - Pascal Lukas
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
| | - Benno Kuropka
- Institute of Chemistry and Biochemistry, Protein Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany
| | - Leonie Küchenhoff
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
| | - Soheil Rastgou Talemi
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Freund
- Institute of Chemistry and Biochemistry, Protein Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany
| | - Elisabetta Ada Cavalcanti-Adam
- Max Planck Institute for Medical Research, Heidelberg, Germany
- Cellular Biomechanics, University of Bayreuth, Bayreuth, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Department of Medicine 5, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Megan Stanifer
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA.
| | - Steeve Boulant
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA.
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Grabowski F, Kochańczyk M, Korwek Z, Czerkies M, Prus W, Lipniacki T. Antagonism between viral infection and innate immunity at the single-cell level. PLoS Pathog 2023; 19:e1011597. [PMID: 37669278 PMCID: PMC10503725 DOI: 10.1371/journal.ppat.1011597] [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: 03/15/2023] [Revised: 09/15/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023] Open
Abstract
When infected with a virus, cells may secrete interferons (IFNs) that prompt nearby cells to prepare for upcoming infection. Reciprocally, viral proteins often interfere with IFN synthesis and IFN-induced signaling. We modeled the crosstalk between the propagating virus and the innate immune response using an agent-based stochastic approach. By analyzing immunofluorescence microscopy images we observed that the mutual antagonism between the respiratory syncytial virus (RSV) and infected A549 cells leads to dichotomous responses at the single-cell level and complex spatial patterns of cell signaling states. Our analysis indicates that RSV blocks innate responses at three levels: by inhibition of IRF3 activation, inhibition of IFN synthesis, and inhibition of STAT1/2 activation. In turn, proteins coded by IFN-stimulated (STAT1/2-activated) genes inhibit the synthesis of viral RNA and viral proteins. The striking consequence of these inhibitions is a lack of coincidence of viral proteins and IFN expression within single cells. The model enables investigation of the impact of immunostimulatory defective viral particles and signaling network perturbations that could potentially facilitate containment or clearance of the viral infection.
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Affiliation(s)
- Frederic Grabowski
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Marek Kochańczyk
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Zbigniew Korwek
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Maciej Czerkies
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Wiktor Prus
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Tomasz Lipniacki
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
- Department of Statistics, Rice University, Houston, Texas, United States of America
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Kardynska M, Kogut D, Pacholczyk M, Smieja J. Mathematical modeling of regulatory networks of intracellular processes - Aims and selected methods. Comput Struct Biotechnol J 2023; 21:1523-1532. [PMID: 36851915 PMCID: PMC9958294 DOI: 10.1016/j.csbj.2023.02.006] [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: 11/30/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Regulatory networks structure and signaling pathways dynamics are uncovered in time- and resource consuming experimental work. However, it is increasingly supported by modeling, analytical and computational techniques as well as discrete mathematics and artificial intelligence applied to to extract knowledge from existing databases. This review is focused on mathematical modeling used to analyze dynamics and robustness of these networks. This paper presents a review of selected modeling methods that facilitate advances in molecular biology.
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Affiliation(s)
- Malgorzata Kardynska
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland
| | - Daria Kogut
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Marcin Pacholczyk
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Jaroslaw Smieja
- Dept. of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Gliwice, Poland.,Dept. of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
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