Within-host modelling of SARS-CoV-2

Morgan Craig

(Sainte-Justine University Hospital Research Centre/Université de Montréal, Canada)

" The impact of viral variants on immunopathology in COVID-19"

As SARS-CoV-2 continues its spread, the emergence of new variants has attracted increased attention, particularly as vaccination efforts ramped up. Throughout the pandemic, there has been a considerable effort to understand the genomic evolution of the virus. A quantitative picture of the evolution of SARS-CoV-2 in response to within-host pressures and their influence on the immunological response to infection is a crucial component to understanding and predicting COVID-19 outcomes. We have previously developed a mechanistic mathematical model of the immunological response to SARS-CoV-2 infection. Leveraging this framework, here we studied how viral variants influence immunopathology in COVID-19. Merging within-host SARS-CoV-2 evolutionary data and our cohort of realistic virtual patients, we predicted the combined effects of spike proteins and interferon-evading mutations on COVID-19 severity. Our results suggest that an individual’s immune response and their potential propensity for severe COVID-19 are the key factors distinguishing COVID-19 disease courses and outcomes.

Ashlee N. Ford Versypt

(University at Buffalo, The State University of New York, USA)

"Multiscale Simulation of Lung Fibrosis Induced by SARS-CoV-2 Infection and Acute Respiratory Distress Syndrome"

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge about immune system-virus-tissue interactions and how these can result in low-level infections in some cases and acute respiratory distress syndrome (ARDS) and other tissue damage in others is limited. We are developing an open-source, multi-scale tissue simulator that can be used to investigate mechanisms of intracellular viral replication, infection of epithelial cells, host immune response, and tissue damage. Our model can simulate fibroblast-mediated collagen deposition to account for the fibrosis at the damaged site in response to immune-response-induced tissue injury. The severity of infection and collagen deposition depends on the anti-inflammatory cytokine secretion rate, multiplicity of infection, and contact time for a CD8+ T cell to kill an infected cell. Additionally, the change in the ACE2 receptor concentration from the multiscale model has been used in a separate model of renin-angiotensin system to predict the change in ANGII, which is a biomarker for hypertension, pro-inflammation, and pro-fibrosis.

Paul Macklin

(Indiana University, USA)

"Community-driven multiscale model of SARS-CoV-2 dynamics and immune response"

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, in-tracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable “choke points” for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Adrianne Jenner

(Queensland University of Technology, Australia)

"Virtual patient cohort reveals immune mechanism driving COVID-19 disease outcomes"

Manifestations of SARS-CoV-2 infection are heterogeneous, and a large proportion of people experience asymptomatic or mild infections that do not require hospitalization. In severe cases, patients develop coronavirus disease (COVID-19), which is frequently accompanied by a myriad of inflammatory indicators and hospitalization. To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mathematical model (system of delay differential equations) and from that interpolated a virtual patient cohort. Our results indicate that virtual patients with low production rates of IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the concentration of interleukin-6 (IL-6) was also a major predictor of CD8+ T cell depletion (a known marker of disease severity in hospitalised patients). 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.