(648e) Multicellular Spatial Model of RNA Virus Replication and Interferon Responses Reveals Factors Controlling Plaque Growth Dynamics

Respiratory virus infections contribute significantly to death rates worldwide. Seasonal influenza virus infection is responsible for 290,000 – 650,000 average annual deaths globally (1), and occasional, highly pathogenic pandemics strains can emerge, such as the 1918 Spanish Flu (2), resulting in significantly higher mortality rates. As of February 16^th, 2021, the SARS-CoV-2 virus has caused over 108.3 million recorded infections and 2.3 million deaths worldwide (3). Both influenza and SARS-CoV-2 are RNA viruses, and studies of severe SARS-CoV-2 and influenza infections find that impaired innate immune responses correlate with more severe outcomes (4–6). In highly pathogenic infections, aberrant innate immune responses – specifically elevated inflammation and high production of type-I interferons leading to hypercytokinemia (cytokine storm) (7) – are believed to be significant drivers of mortality (8,9). Excessive inflammation exacerbates tissue damage and hinders clinical recovery (10,11). Influenza studies show that immunomodulation can improve infection outcomes. Prestimulation of toll-like receptors is protective against highly pathogenic influenza strains in mice (12), while cell culture prestimulation with type-I interferons prevents viral plaque growth by SARS-CoV (13), SARS-CoV-2 (14), and influenza (15). Nebulized interferon α2b and interferon β are being investigated as an early treatment for COVID-19 (16,17). Collectively, these studies demonstrate the necessity of immune response regulation as a means to balance tissue damage from inflammatory responses with viral clearance. Computational modeling may reveal how complex responses emerge during infection, rapidly identify and optimize treatments, and reveal dynamic insights into immunoregulation during respiratory infection that can drive the discovery and design of immune targeted treatments.

Recent computational models have considered many aspects of virus infection induced immune responses (18–21), however, few models describe virus replication and interferon regulation in a cell culture. Ordinary Differential Equation (ODE) based models assume either homogeneity or a compartment based structure, and typically ignore the diffusion of virions and cytokine signaling, heterogeneity of cell response to stimuli, and stochasticity of individual cells’ response (19,22). Many recent models (20,23,24) of interferon response use a simple virally resistant cell state which does not capture interferon stimulated genes’ (ISGs) effect on viral growth (23,24). A spatial model of viral spread and plaque growth (22) mentions the impact of diffusion constants on viral plaque formation, but does not incorporate the cells’ innate immune response to infection and paracrine induced activation, limiting the ability of the model to explain plaque growth arrest due to ISGs.

We developed a multiscale, multicellular, spatiotemporal model of the innate immune response to RNA viral respiratory infections in vitro in CompuCell3D (25) (CC3D), with the ability to simulate plaque growth, cytokine response, and plaque arrest. The model of IFN production and virus replication determines the conditions leading to arrest or promotion of plaque growth during the infection of lung epithelial cells with an RNA virus. Plaque growth assays seed the virus at low multiplicity of infection (MOI) and allow it to replicate across a sheet of host cells in a cell culture to form visible plaques. Our aim in replicating plaque growth experiments in a spatial computer simulation was twofold. First, realistic simulations of physical experiments allow in silico experimentation for cheaper, faster, and higher throughput hypothesis testing of more simultaneous outputs than would be experimentally viable. Second, our model replicates familiar biological experimental measurements and imaging, making our results accessible to wet lab biologists. The model includes two competing mechanisms, viral replication and the host cells’ innate immune response. Viral reproduction consists of the virus production within and export from infected cells, and viral particle spread via diffusion in the extracellular matrix. The host immune response includes interferon production, export, and diffusion, and the initiation of virally resistant cell states via ISGs. The extracellular environment allows diffusion of virions to spread the virus and form viral plaques and type-I interferons responsible for spatiotemporal paracrine signaling. Viral plaque growth is shown to be arrested under elevated STATP activity, when the epithelial cells are pretreated with type-I interferons, and when interferon diffusion is sufficiently elevated over the diffusion of viral particles, with the relationship between the two diffusion constants being nonlinear. Targeted local sensitivity analyses under both arrested and continuous plaque growth conditions reveals that which factors control plaque growth vary significantly based on these same conditions. Since epithelial regulation of IFN is an important early regulator of the immune system more broadly, the model could be expanded in future work to account for additional regulation via immune cells.

1. WHO. Global Influenza Strategy 2019-2030.
2. THE GEOGRAPHY AND MORTALITY OF THE 1918 INFLUENZA PANDEMIC on JSTOR [Internet]. [cited 2020 Nov 30]. Available from: https://www.jstor.org/stable/44447656?seq=1#metadata_info_tab_contents
3. Weekly epidemiological update - 16 February 2021 [Internet]. [cited 2021 Feb 22]. Available from: https://www.who.int/publications/m/item/weekly-epidemiological-update---...
4. Yang Y, Tang H. Aberrant coagulation causes a hyper-inflammatory response in severe influenza pneumonia. Cell Mol Immunol. 2016;13(4):432–42.
5. Amor S, Fernández Blanco L, Baker D. Innate immunity during SARS‐CoV‐2: evasion strategies and activation trigger hypoxia and vascular damage. Clin Exp Immunol [Internet]. 2020 Nov 12 [cited 2020 Dec 4];202(2):193–209. Available from: https://onlinelibrary.wiley.com/doi/10.1111/cei.13523
6. Shibabaw T, Molla MD, Teferi B, Ayelign B. Role of ifn and complements system: Innate immunity in sars-cov-2 [Internet]. Vol. 13, Journal of Inflammation Research. Dove Medical Press Ltd; 2020 [cited 2020 Dec 4]. p. 507–18. Available from: /pmc/articles/PMC7490109/?report=abstract
7. AbdelMassih AF, Ramzy D, Nathan L, Aziz S, Ashraf M, Youssef NH, et al. Possible molecular and paracrine involvement underlying the pathogenesis of COVID-19 cardiovascular complications. Cardiovasc Endocrinol Metab [Internet]. 2020 [cited 2020 May 11];Publish Ah:1–4. Available from: https://journals.lww.com/cardiovascularendocrinology/Fulltext/9000/Possi... NS -
8. Baskin CR, Bielefeldt-Ohmann H, Tumpey TM, Sabourin PJ, Long JP, García-Sastre A, et al. Early and sustained innate immune response defines pathology and death in nonhuman primates infected by highly pathogenic influenza virus. Proc Natl Acad Sci U S A [Internet]. 2009 Mar 3 [cited 2020 Dec 4];106(9):3455–60. Available from: www.pnas.org/cgi/content/full/
9. Kobasa D, Jones SM, Shinya K, Kash JC, Copps J, Ebihara H, et al. Aberrant innate immune response in lethal infection of macaques with the 1918 influenza virus. Nature. 2007;445(7125):319–23.
10. Cilloniz C, Pantin-Jackwood MJ, Ni C, Goodman AG, Peng X, Proll SC, et al. Lethal Dissemination of H5N1 Influenza Virus Is Associated with Dysregulation of Inflammation and Lipoxin Signaling in a Mouse Model of Infection. J Virol [Internet]. 2010 Aug 1 [cited 2019 Sep 24];84(15):7613–24. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20504916
11. Peiris JSM, Cheung CY, Leung CYH, Nicholls JM. Innate immune responses to influenza A H5N1: friend or foe? Trends Immunol [Internet]. 2009 Dec [cited 2019 Sep 23];30(12):574–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19864182
12. Shinya K, Okamura T, Sueta S, Kasai N, Tanaka M, Ginting TE, et al. Toll-like receptor pre-stimulation protects mice against lethal infection with highly pathogenic influenza viruses. Virol J. 2011;8:97.
13. Lokugamage K, Hage A, de Vries M, Valero-Jimenez A, Schindewolf C, Dittmann M, et al. Type I interferon susceptibility distinguishes SARS-CoV-2 from SARS-CoV. bioRxiv Prepr Serv Biol [Internet]. 2020 [cited 2020 Aug 5]; Available from: /pmc/articles/PMC7239075/?report=abstract
14. Lokugamage KG, Hage A, Schindewolf C, Rajsbaum R, Menachery VD. SARS-CoV-2 is sensitive to type I interferon pretreatment. bioRxiv Prepr Serv Biol [Internet]. 2020 [cited 2020 Aug 4];179:104811. Available from: https://doi.org/10.1101/2020.03.07.982264
15. Qiao L, Phipps-Yonas H, Hartmann B, Moran TM, Sealfon SC, Hayot F. Immune response modeling of interferon beta-pretreated influenza virus-infected human dendritic cells. Biophys J [Internet]. 2010 Feb 17 [cited 2019 Sep 9];98(4):505–14. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20159146
16. Zhou Q, Chen V, Shannon CP, Wei X-S, Xiang X, Wang X, et al. Interferon-α2b Treatment for COVID-19. Front Immunol [Internet]. 2020 May 15 [cited 2020 Sep 18];11:1061. Available from: https://www.frontiersin.org/article/10.3389/fimmu.2020.01061/full
17. Covid: Large trial of new treatment begins in UK - BBC News [Internet]. [cited 2021 Jan 15]. Available from: https://www.bbc.com/news/health-55639096
18. Smith AM. Validated models of immune response to virus infection. Vol. 12, Current Opinion in Systems Biology. Elsevier Ltd; 2018. p. 46–52.
19. Gregg RW, Sarkar SN, Shoemaker JE. Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback. J Theor Biol [Internet]. 2019 Feb 7 [cited 2019 Jun 28];462:148–57. Available from: https://www.sciencedirect.com/science/article/pii/S0022519318305484
20. Pawelek KA, Huynh GT, Quinlivan M, Cullinane A, Rong L, Perelson AS. Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses. Antia R, editor. PLoS Comput Biol [Internet]. 2012 Jun 28 [cited 2020 Dec 15];8(6):e1002588. Available from: https://dx.plos.org/10.1371/journal.pcbi.1002588
21. Bocharov GA, Romanyukha AA. Mathematical Model of Antiviral Immune Response III. Influenza A Virus Infection. J Theor Biol [Internet]. 1994 Apr [cited 2019 May 30];167(4):323–60. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022519384710745
22. Holder BP, Liao LE, Simon P, Boivin G, Beauchemin CAA. Design considerations in building in silico equivalents of common experimental influenza virus assays. Autoimmunity [Internet]. 2011 Jun 19 [cited 2020 Aug 5];44(4):282–93. Available from: http://www.tandfonline.com/doi/full/10.3109/08916934.2011.523267
23. Saenz RA, Quinlivan M, Elton D, MacRae S, Blunden AS, Mumford JA, et al. Dynamics of Influenza Virus Infection and Pathology. J Virol [Internet]. 2010 Apr 15 [cited 2020 Dec 15];84(8):3974–83. Available from: http://jvi.asm.org/
24. Hancioglu B, Swigon D, Clermont G. A dynamical model of human immune response to influenza A virus infection. J Theor Biol. 2007 May 7;246(1):70–86.
25. Swat MH, Thomas GL, Belmonte JM, Shirinifard A, Hmeljak D, Glazier JA. Multi-Scale Modeling of Tissues Using CompuCell3D. In: Methods in Cell Biology. Academic Press Inc.; 2012. p. 325–66.