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1. Lytic Cell Death Mechanisms in Human Respiratory Syncytial Virus-Infected Macrophages: Roles of Pyroptosis and Necroptosis

   https://doi.org/doi: 10.3390/v12090932  

   Bedient, Lori; Pokharel, Swechha Mainali; Chiok, Kin R; Mohanty, Indira; Beach, Sierra S; Miura, Tanya A; Bose, Santanu (August 2020, Viruses)

   null (Ed.)

   Human respiratory syncytial virus (RSV) is the most common cause of viral bronchiolitis and pneumonia in infants and children worldwide. Inflammation induced by RSV infection is responsible for its hallmark manifestation of bronchiolitis and pneumonia. The cellular debris created through lytic cell death of infected cells is a potent initiator of this inflammation. Macrophages are known to play a pivotal role in the early innate immune and inflammatory response to viral pathogens. However, the lytic cell death mechanisms associated with RSV infection in macrophages remains unknown. Two distinct mechanisms involved in lytic cell death are pyroptosis and necroptosis. Our studies revealed that RSV induces lytic cell death in macrophages via both of these mechanisms, specifically through the ASC (Apoptosis-associated speck like protein containing a caspase recruitment domain)-NLRP3 (nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3) inflammasome activation of both caspase-1 dependent pyroptosis and receptor-interacting serine/threonine-protein kinase 3 (RIPK3), as well as a mixed lineage kinase domain like pseudokinase (MLKL)-dependent necroptosis. In addition, we demonstrated an important role of reactive oxygen species (ROS) during lytic cell death of RSV-infected macrophages. 

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2. Identifiability and Parameter Estimation of Within-Host Model of HIV with Immune Response

   https://doi.org/10.3390/math12182837  

   Liyanage, Yuganthi R; Mirsaleh_Kohan, Leila; Martcheva, Maia; Tuncer, Necibe (September 2024, Mathematics)

   This study examines the interactions between healthy target cells, infected target cells, virus particles, and immune cells within an HIV model. The model exhibits two equilibrium points: an infection-free equilibrium and an infection equilibrium. Stability analysis shows that the infection-free equilibrium is locally asymptotically stable when R0<1. Further, it is unstable when R0>1. The infection equilibrium is locally asymptotically stable when R0>1. The structural and practical identifiabilities of the within-host model for HIV infection dynamics were investigated using differential algebra techniques and Monte Carlo simulations. The HIV model was structurally identifiable by observing the total uninfected and infected target cells, immune cells, and viral load. Monte Carlo simulations assessed the practical identifiability of parameters. The production rate of target cells (λ), the death rate of healthy target cells (d), the death rate of infected target cells (δ), and the viral production rate by infected cells (π) were practically identifiable. The rate of infection of target cells by the virus (β), the death rate of infected cells by immune cells (Ψ), and antigen-driven proliferation rate of immune cells (b) were not practically identifiable. Practical identifiability was constrained by the noise and sparsity of the data. Analysis shows that increasing the frequency of data collection can significantly improve the identifiability of all parameters. This highlights the importance of optimal data sampling in HIV clinical studies, as it determines the best time points, frequency, and the number of sample points required to accurately capture the dynamics of the HIV infection within a host. 

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3. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

   https://doi.org/10.1371/journal.pcbi.1009735  

   Moses, Melanie E.; Hofmeyr, Steven; Cannon, Judy L.; Andrews, Akil; Gridley, Rebekah; Hinga, Monica; Leyba, Kirtus; Pribisova, Abigail; Surjadidjaja, Vanessa; Tasnim, Humayra; et al (December 2021, PLOS Computational Biology)

   Smith, Amber M (Ed.)

   A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. 

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4. 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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5. TGF-β blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo

   https://doi.org/10.1038/s41467-024-45555-x  

   Kim, Jinhee; Bose, Deepanwita; Araínga, Mariluz; Haque, Muhammad R; Fennessey, Christine M; Caddell, Rachel A; Thomas, Yanique; Ferrell, Douglas E; Ali, Syed; Grody, Emanuelle; et al (December 2024, Nature Communications)

   Abstract HIV-1 persistence during ART is due to the establishment of long-lived viral reservoirs in resting immune cells. Using an NHP model of barcoded SIVmac239 intravenous infection and therapeutic dosing of anti-TGFBR1 inhibitor galunisertib (LY2157299), we confirm the latency reversal properties of in vivo TGF-β blockade, decrease viral reservoirs and stimulate immune responses. Treatment of eight female, SIV-infected macaques on ART with four 2-weeks cycles of galunisertib leads to viral reactivation as indicated by plasma viral load and immunoPET/CT with a⁶⁴Cu-DOTA-F(ab’)₂-p7D3-probe. Post-galunisertib, lymph nodes, gut and PBMC exhibit lower cell-associated (CA-)SIV DNA and lower intact pro-virus (PBMC). Galunisertib does not lead to systemic increase in inflammatory cytokines. High-dimensional cytometry, bulk, and single-cell (sc)RNAseq reveal a galunisertib-driven shift toward an effector phenotype in T and NK cells characterized by a progressive downregulation in TCF1. In summary, we demonstrate that galunisertib, a clinical stage TGF-β inhibitor, reverses SIV latency and decreases SIV reservoirs by driving T cells toward an effector phenotype, enhancing immune responses in vivo in absence of toxicity. 

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