SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics

Allometric comparison of viral dynamics in the nasal cavity-nasopharyngeal mucus layer of human and rhesus monkey by CFD-HCD approach

BACKGROUND AND OBJECTIVE

Viral respiratory infections stand as a considerable global health concern, presenting significant risks to the health of both humans and animals. This study aims to conduct a preliminary analysis of the time series of viral load in the nasal cavity-nasopharynx (NC-NP) of the human and rhesus macaque (RM).

METHODS

Taking into account the random uniform distribution of virus-laden droplets with a diameter of 10 μm in the mucus layer, this study applies the computational fluid dynamics-host cell dynamics (CFD-HCD) method to 3D-shell NC-NP models of human and RM, analyzing the impact of initial distribution of droplets on the viral dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), estimating parameters in the HCD model based on experimental data, integrating them into simulations to predict the time series of viral load and cell counts, and being visualized. The reproductive number (R0) are calculated to determine the occurrence of infection. The study also considers cross-parameter combinations and cross-experimental datasets to explore potential correlations between the human and RM.

RESULTS

The research findings indicate that the uniform distribution of virus-laden droplets throughout the whole NC-NP models of human and RM is reasonable for simulating and predicting viral dynamics. The visualization results offer dynamic insights into virus infection over a period of 20 days. Studies involving parameter and dataset exchanges between the two species underscore certain similarities in predicting virus infections between the human and RM.

CONCLUSIONS

This study lays the groundwork for further exploration into the parallels and distinctions in respiratory virus dynamics between humans and RMs, thus aiding in making more informed decisions in research and experimentation.

In silico identification of viral loads in cough-generated droplets - Seamless integrated analysis of CFPD-HCD-EWF

BACKGROUND AND OBJECTIVE

Respiratory diseases caused by respiratory viruses have significantly threatened public health worldwide. This study presents a comprehensive approach to predict viral dynamics and the generation of stripped droplets within the mucus layer of the respiratory tract during coughing using a larynx-trachea-bifurcation (LTB) model.

METHODS

This study integrates computational fluid-particle dynamics (CFPD), host-cell dynamics (HCD), and the Eulerian wall film (EWF) model to propose a potential means for seamless integrated analysis. The verified CFPD-HCD coupling model based on a 3D-shell model was used to characterize the severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2) dynamics in the LTB mucus layer, whereas the EWF model was employed to account for the interfacial fluid to explore the generation mechanism and trace the origin site of droplets exhaled during a coughing event of an infected host.

RESULTS

The results obtained using CFPD delineated the preferential deposition sites for droplets in the laryngeal and tracheal regions. Thus, the analysis of the HCD model showed that the viral load increased rapidly in the laryngeal region during the peak of infection, whereas there was a growth delay in the tracheal region (up to day 8 after infection). After two weeks of infection, the high viral load gradually migrated towards the glottic region. Interestingly, the EWF model demonstrated a high concentration of exhaled droplets originating from the larynx. The coupling technique indicated a concurrent high viral load in the mucus layer and site of origin of the exhaled droplets.

CONCLUSIONS

This interdisciplinary research underscores the seamless analysis from initial exposure to virus-laden droplets, the dynamics of viral infection in the LTB mucus layer, and the re-emission from the coughing activities of an infected host. Our efforts aimed to address the complex challenges at the intersection of viral dynamics and respiratory health, which can contribute to a more detailed understanding and targeted prevention of respiratory diseases.

COVID infection in 4 steps: Thermodynamic considerations reveal how viral mucosal diffusion, target receptor affinity and furin cleavage act in concert to drive the nature and degree of infection in human COVID-19 disease

We have developed a mechanistic model of SARS-CoV-2 and SARS-CoV infection, exploring the relationship between the viral diffusion in the mucosa and viral affinity for the angiotensin converting enzyme 2 (ACE2) target. Utilising the structural similarity of SARS-CoV and SARS-CoV-2 and a shared viral target receptor (ACE2), but a dramatic difference in upper or lower respiratory tract infectivity, we were able to generate insights into the linkage of mucosal diffusion and target receptor affinity in determining the pathophysiological pathways of these two viruses. Our analysis reveals that for SARS-CoV-2 the higher affinity of ACE2 binding, the faster and more complete the mucosal diffusion in its transport from the upper airway to the region of the ACE2 target on the epithelium. This diffusional process is essential for the presentation of this virus to the furin catalysed highly efficient entry and infection process in the upper respiratory tract epithelial cells. A failure of SARS-CoV to follow this path is associated with lower respiratory tract infection and decreased infectivity. Thus, our analysis supports the view that through tropism SARS-CoV-2 has evolved a highly efficient membrane entry process that can act in concert with a high binding affinity of this virus and its variants for its ACE2 which in turn promotes enhanced movement of the virus from airway to epithelium. In this way ongoing mutations yielding higher affinities of SARS-CoV-2 for the ACE2 target becomes the basis for higher upper respiratory tract infectivity and greater viral spread. It is concluded that SARS-CoV-2 is constrained in the extent of its activities by the fundamental laws of physics and thermodynamics. Laws that describe diffusion and molecular binding. Moreover it can be speculated that the very earliest contact of this virus with the human mucosa defines the pathogenesis of this infection.

Visual prediction and parameter optimization of viral dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis

BACKGROUND AND OBJECTIVE

Respiratory diseases caused by viruses are a major human health problem. To better control the infection and understand the pathogenesis of these diseases, this paper studied SARS-CoV-2, a novel coronavirus outbreak, as an example.

METHODS

Based on coupled computational fluid and particle dynamics (CFPD) and host-cell dynamics (HCD) analyses, we studied the viral dynamics in the mucus layer of the human nasal cavity-nasopharynx. To reproduce the effect of mucociliary movement on the diffusive and convective transport of viruses in the mucus layer, a 3D-shell model was constructed using CT data of the upper respiratory tract (URT) of volunteers. Considering the mucus environment, the HCD model was established by coupling the target cell-limited model with the convection-diffusion term. Parameter optimization of the HCD model is the key problem in the simulation. Therefore, this study focused on the parameter optimization of the viral dynamics model, divided the geometric model into multiple compartments, and used Monolix to perform the nonlinear mixed effects (NLME) of pharmacometrics to discuss the influence of factors such as the number of mucus layers, number of compartments, diffusion rate, and mucus flow velocity on the prediction results.

RESULTS

The findings showed that sufficient experimental data can be used to estimate the corresponding parameters of the HCD model. The optimized convection-diffusion case with a two-layer multi-compartment low-velocity model could accurately predict the viral dynamics.

CONCLUSIONS

Its visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.