Mucociliary transport deficiency and disease progression in Syrian hamsters with SARS-CoV-2 infection

ACE-2 Blockade & TMPRSS2 Inhibition Mitigate SARS-CoV-2 Severity Following Cigarette Smoke Exposure in Airway Epithelial Cells In Vitro

Cigarette smoking is associated with COVID-19 prevalence and severity, but the mechanistic basis for how smoking alters SARS-CoV-2 pathogenesis is unknown. A potential explanation is that smoking alters the expression of the SARS-CoV-2 cellular receptor and point of entry, angiotensin converting enzyme-2 (ACE-2), and its cofactors including transmembrane protease serine 2 (TMPRSS2). We investigated the impact of cigarette smoking on the expression of ACE-2, TMPRSS2, and other known cofactors of SARS-CoV-2 infection and the resultant effects on infection severity in vitro. Cigarette smoke extract (CSE) exposure increased ACE-2 and TMPRSS2 mRNA expression compared to air control in ferret airway cells, Calu-3 cells, and primary human bronchial epithelial (HBE) cells derived from normal and COPD donors. CSE-exposed ferret airway cells inoculated with SARS-CoV-2 had a significantly higher intracellular viral load versus vehicle-exposed cells. Likewise, CSE-exposure increased both SARS-CoV-2 intracellular viral load and viral replication in both normal and COPD HBE cells over vehicle control. Apoptosis was increased in CSE-exposed, SARS-CoV-2-infected HBE cells. Knockdown of ACE-2 via an antisense oligonucleotide (ASO) reduced SARS-CoV-2 viral load and infection in CSE-exposed ferret airway cells that was augmented by co-administration of camostat mesylate to block TMPRSS2 activity. Smoking increases SARS-CoV-2 infection via upregulation of ACE2 and TMPRSS2.

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

Mucociliary clearance augmenting drugs block SARS-CoV-2 replication in human airway epithelial cells

The coronavirus disease (COVID-19) pandemic, caused by SARS-CoV-2 coronavirus, is devastatingly impacting human health. A prominent component of COVID-19 is the infection and destruction of the ciliated respiratory cells, which perpetuates dissemination and disrupts protective mucociliary transport (MCT) function, an innate defense of the respiratory tract. Thus, drugs that augment MCT could improve the barrier function of the airway epithelium and reduce viral replication and, ultimately, COVID-19 outcomes. We tested five agents known to increase MCT through distinct mechanisms for activity against SARS-CoV-2 infection using a model of human respiratory epithelial cells terminally differentiated in an air/liquid interphase. Three of the five mucoactive compounds tested showed significant inhibitory activity against SARS-CoV-2 replication. An archetype mucoactive agent, ARINA-1, blocked viral replication and therefore epithelial cell injury; thus, it was further studied using biochemical, genetic, and biophysical methods to ascertain the mechanism of action via the improvement of MCT. ARINA-1 antiviral activity was dependent on enhancing the MCT cellular response, since terminal differentiation, intact ciliary expression, and motion were required for ARINA-1-mediated anti-SARS-CoV2 protection. Ultimately, we showed that the improvement of cilia movement was caused by ARINA-1-mediated regulation of the redox state of the intracellular environment, which benefited MCT. Our study indicates that intact MCT reduces SARS-CoV-2 infection, and its pharmacologic activation may be effective as an anti-COVID-19 treatment.