Adrianne L Jenner1,2, Rosemary A Aogo3, Sofia Alfonso4, Vivienne Crowe5, Amanda P Smith3, Penelope A Morel6, Courtney L Davis7, Amber M Smith3, Morgan Craig1,2,4. 1. CHU Sainte-Justine Research Centre, Montréal, Québec, Canada. 2. Department of Mathematics and Statistics, Université de Montréal, Montréal, Québec, Canada. 3. Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA. 4. Department of Physiology, McGill University, Montréal, Québec, Canada. 5. Department of Mathematics and Statistics, Concordia University, Montréal, Québec, Canada. 6. Department of Immunology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 7. Natural Science Division, Pepperdine University, Malibu, California, USA.
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 indicate 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 that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY: Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.
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 indicate 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 that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. AUTHOR SUMMARY: Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.
Authors: Rudragouda Channappanavar; Anthony R Fehr; Jian Zheng; Christine Wohlford-Lenane; Juan E Abrahante; Matthias Mack; Ramakrishna Sompallae; Paul B McCray; David K Meyerholz; Stanley Perlman Journal: J Clin Invest Date: 2019-07-29 Impact factor: 14.808
Authors: Adam G Laing; Anna Lorenc; Irene Del Molino Del Barrio; Abhishek Das; Matthew Fish; Leticia Monin; Miguel Muñoz-Ruiz; Duncan R McKenzie; Thomas S Hayday; Isaac Francos-Quijorna; Shraddha Kamdar; Magdalene Joseph; Daniel Davies; Richard Davis; Aislinn Jennings; Iva Zlatareva; Pierre Vantourout; Yin Wu; Vasiliki Sofra; Florencia Cano; Maria Greco; Efstathios Theodoridis; Joshua D Freedman; Sarah Gee; Julie Nuo En Chan; Sarah Ryan; Eva Bugallo-Blanco; Pärt Peterson; Kai Kisand; Liis Haljasmägi; Loubna Chadli; Philippe Moingeon; Lauren Martinez; Blair Merrick; Karen Bisnauthsing; Kate Brooks; Mohammad A A Ibrahim; Jeremy Mason; Federico Lopez Gomez; Kola Babalola; Sultan Abdul-Jawad; John Cason; Christine Mant; Jeffrey Seow; Carl Graham; Katie J Doores; Francesca Di Rosa; Jonathan Edgeworth; Manu Shankar-Hari; Adrian C Hayday Journal: Nat Med Date: 2020-08-17 Impact factor: 87.241