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1. Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets

   https://doi.org/10.3390/v13081635  

   Vaidya, Naveen K.; Bloomquist, Angelica; Perelson, Alan S. (August 2021, Viruses)

   null (Ed.)

   The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies. 

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

   https://doi.org/10.3390/v16010069  

   Zhang, Leyi; Cao, Han; Medlin, Karen; Pearson, Jason; Aristotelous, Andreas C; Chen, Alexander; Wessler, Timothy; Forest, M Gregory (January 2024, Viruses)

   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. 

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3. Ecological and Evolutionary Insights About Emerging Infectious Diseases from the COVID-19 Pandemic

   https://doi.org/10.1146/annurev-ecolsys-102320-101234  

   Kilpatrick, A Marm (November 2023, Annual Review of Ecology, Evolution, and Systematics)

   The coronavirus disease 2019 (COVID-19) pandemic challenged the workings of human society, but in doing so, it advanced our understanding of the ecology and evolution of infectious diseases. Fluctuating transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demonstrated the highly dynamic nature of human social behavior, often without government intervention. Evolution of SARS-CoV-2 in the first two years following spillover resulted primarily in increased transmissibility, while in the third year, the globally dominant virus variants had all evolved substantial immune evasion. The combination of viral evolution and the buildup of host immunity through vaccination and infection greatly decreased the realized virulence of SARS-CoV-2 due to the age dependence of disease severity. The COVID-19 pandemic was exacerbated by presymptomatic, asymptomatic, and highly heterogeneous transmission, as well as highly variable disease severity and the broad host range of SARS-CoV-2. Insights and tools developed during the COVID-19 pandemic could provide a stronger scientific basis for preventing, mitigating, and controlling future pandemics. 

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4. SARS-CoV-2 nucleocapsid protein forms condensates with viral genomic RNA

   https://doi.org/10.1371/journal.pbio.3001425  

   Jack, Amanda; Ferro, Luke S.; Trnka, Michael J.; Wehri, Eddie; Nadgir, Amrut; Nguyenla, Xammy; Fox, Douglas; Costa, Katelyn; Stanley, Sarah; Schaletzky, Julia; et al (October 2021, PLOS Biology)

   Cimarelli, Andrea (Ed.)

   The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection causes Coronavirus Disease 2019 (COVID-19), a pandemic that seriously threatens global health. SARS-CoV-2 propagates by packaging its RNA genome into membrane enclosures in host cells. The packaging of the viral genome into the nascent virion is mediated by the nucleocapsid (N) protein, but the underlying mechanism remains unclear. Here, we show that the N protein forms biomolecular condensates with viral genomic RNA both in vitro and in mammalian cells. While the N protein forms spherical assemblies with homopolymeric RNA substrates that do not form base pairing interactions, it forms asymmetric condensates with viral RNA strands. Cross-linking mass spectrometry (CLMS) identified a region that drives interactions between N proteins in condensates, and deletion of this region disrupts phase separation. We also identified small molecules that alter the size and shape of N protein condensates and inhibit the proliferation of SARS-CoV-2 in infected cells. These results suggest that the N protein may utilize biomolecular condensation to package the SARS-CoV-2 RNA genome into a viral particle. 

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5. Hydroxypropyl-Beta Cyclodextrin Barrier Prevents Respiratory Viral Infections: A Preclinical Study

   https://doi.org/10.3390/ijms25042061  

   Lu, Angela; Ebright, Brandon; Naik, Aditya; Tan, Hui L; Cohen, Noam A; Bouteiller, Jean-Marie C; Lazzi, Gianluca; Louie, Stan G; Humayun, Mark S; Asante, Isaac (February 2024, International Journal of Molecular Sciences)

   The emergence and mutation of pathogenic viruses have been occurring at an unprecedented rate in recent decades. The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has developed into a global public health crisis due to extensive viral transmission. In situ RNA mapping has revealed angiotensin-converting enzyme 2 (ACE2) expression to be highest in the nose and lower in the lung, pointing to nasal susceptibility as a predominant route for infection and the cause of subsequent pulmonary effects. By blocking viral attachment and entry at the nasal airway using a cyclodextrin-based formulation, a preventative therapy can be developed to reduce viral infection at the site of entry. Here, we assess the safety and antiviral efficacy of cyclodextrin-based formulations. From these studies, hydroxypropyl beta-cyclodextrin (HPBCD) and hydroxypropyl gamma-cyclodextrin (HPGCD) were then further evaluated for antiviral effects using SARS-CoV-2 pseudotypes. Efficacy findings were confirmed with SARS-CoV-2 Delta variant infection of Calu-3 cells and using a K18-hACE2 murine model. Intranasal pre-treatment with HPBCD-based formulations reduced viral load and inflammatory signaling in the lung. In vitro efficacy studies were further conducted using lentiviruses, murine hepatitis virus (MHV), and influenza A virus subtype H1N1. These findings suggest HPBCD may be used as an agnostic barrier against transmissible pathogens, including but not limited to SARS-CoV-2. 

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