Scientists have observed that every individual responds differently to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the causal agent of the coronavirus disease 2019 (COVID-19) pandemic. The evolution of SARS-CoV-2 due to genomic mutations has resulted in several variants, and each variant manifests the disease differently. Some variants cause severe infection, whereas others cause asymptomatic, mild, or moderate symptoms that do not require hospitalization.
Several studies have determined the abnormal immune responses in the case of severe SARS-CoV-2 infection. They reported increased inflammatory profiles (e.g., interleukin 1 (IL-1), interleukin 6 (IL-6), etc.) and uncharacteristic immune cell distribution that includes loss of resident alveolar macrophages, an elevated number of neutrophils, inflammatory monocyte-derived macrophages, and decrease in overall lymphocytes, in severely infected COVID-19 patients who required hospitalization.
Although several experiment-based studies associated with COVID-19 infection are available, the precise mechanism underlying severe illness is not well understood. Researchers stated that mathematical and computational model-based studies could provide an additional perspective to support the findings of experimental studies.
To date, most of the model-based studies associated with the within-host spread of infection have used ordinary differential equations (ODEs), and transmission dynamics studies have used SIR (Susceptible, Infectious, or Recovered) models. Researchers indicated that these models have provided important information regarding viral-induced immunopathology. However, one of the limitations of ODE-based models is that they fail to consider variations in space, i.e., the constraints of anatomical and physiological factors. In contrast, individual-based models (IBMs), also known as agent-based models (ABMs), are used to study spatially dependent systems.
A new study
A new study published on the bioRxiv* preprint server has focused on developing a multiscale, hybrid, individual-based model to understand the within-host spread of SARS-CoV-2 infection and consequent innate immune responses. In this study, the authors have specifically studied the interactions of epithelial cells, a subset of cytokines and macrophages.
To simulate the transmission of COVID-19 infection, scientists adapted and extended an established model that was primarily designed to understand the within-host spread of Mycobacterium tuberculosis infection. Some of the factors supported by the present model include extracellular virus diffusion, uniform single-layer of lung epithelial cells, virus-specific entry and replication pathways, subsequent innate immune response (e.g., macrophages), and cytokine signaling from epithelial and immune cells.
The present model elucidated the spread of COVID-19 infection over an epithelial monolayer. Researchers used this model to study the impact of initial viral deposition via an increasing multiplicity of infections (MOI). Further, they determined the impact of delayed IFN-I secretion and various levels of IFN secretion from epithelial cells by enhancing virus-dependent signal half-max and IFN-I secretion rate.
The current study revealed the highest value of MOI showed maximum infectiousness. Additionally, researchers demonstrated that increased infection is directly related to elevated chemokine, interferon, and cytokine levels. Surprisingly, scientists observed that when MOI was increased, a local maximum in the intracellular viral load per grid occurred in an early phase of the simulation. The authors stated that such an event occurs only when the export of the intracellular virions exceeds the production.
Another explanation for such occurrence is the removal of clustered infectious epithelial cells by either apoptosis or macrophage phagocytosis, or both. Interestingly, researchers observed marginal elevation in IFN-I levels when local maximum in intracellular viral load occurred. This result suggests that the event of local maximum occurred due to IFN-I inhibition of viral entry and replication.
Previous studies have reported that a delay in IFN-I activity causes severe COVID-19 infection. The current study revealed a delay in IFN-I secretion from epithelial cells enhances the spread of infection. However, the authors observed that the longest IFN-I secretion delay did not influence a local maximum in the intracellular viral load per grid. This result emphasizes the role of IFN-I in antiviral activity.
This study revealed that the deployment of resting macrophages on the site of infection is aided by IL-6, along with generic mononuclear phagocyte chemokine. Two parameters were considered to study the role of macrophages on the spread of infection, i.e., the macrophage activation half-max and macrophage virus internalization rate. Researchers reported that when the macrophage virus internalization rate was increased, the infection rate remained the same. Interestingly, the current model showed that large recruitment of resting macrophages occurred due to a greater infection rate and elevated IL-6 levels.
One of the limitations of this study is the consideration of a single type and phenotype of macrophages and limited sets of cytokines to keep the model complexity at the lowest. Researchers stated that other macrophage phenotypes, immune cells, and anti-inflammatory factors could directly influence the magnitude of cytokine, interferon, and chemokine. In the future, the model must be extended to include multiple immune cell populations and subpopulations and a larger set of chemokines and cytokines.
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.