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1. Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

   https://doi.org/10.3390/v14050906  

   Ackerman, Emily E.; Weaver, Jordan J.; Shoemaker, Jason E. (May 2022, Viruses)

   The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains. 

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2. Sex-biased infections scale to population impacts for an emerging wildlife disease

   https://doi.org/10.1098/rspb.2023.0040  

   Kailing, Macy J.; Hoyt, Joseph R.; White, J. Paul; Kaarakka, Heather M.; Redell, Jennifer A.; Leon, Ariel E.; Rocke, Tonie E.; DePue, John E.; Scullon, William H.; Parise, Katy L.; et al (March 2023, Proceedings of the Royal Society B: Biological Sciences)

   Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects. 

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3. Understanding Neutrophil Dynamics during COVID-19 Infection

   https://doi.org/10.3390/app13042409  

   Murphy, Quiyana M.; Ciupe, Stanca M. (February 2023, Applied Sciences)

   Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in varied clinical outcomes, with virus-induced chronic inflammation and tissue injury being associated with enhanced disease pathogenesis. To determine the role of tissue damage on immune populations recruitment and function, a mathematical model of innate immunity following SARS-CoV-2 infection has been proposed. The model was fitted to published longitudinal immune marker data from patients with mild and severe COVID-19 disease and key parameters were estimated for each clinical outcome. Analytical, bifurcation, and numerical investigations were conducted to determine the effect of parameters and initial conditions on long-term dynamics. The results were used to suggest changes needed to achieve immune resolution. 

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4. Disease diagnostics using machine learning of B cell and T cell receptor sequences

   https://doi.org/10.1126/science.adp2407  

   Zaslavsky, Maxim E; Craig, Erin; Michuda, Jackson K; Sehgal, Nidhi; Ram-Mohan, Nikhil; Lee, Ji-Yeun; Nguyen, Khoa D; Hoh, Ramona A; Pham, Tho D; Röltgen, Katharina; et al (February 2025, Science)

   Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndromecoronavirus2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses. 

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5. Mathematical Modeling of RNA Virus Sensing Pathways Reveals Paracrine Signaling as the Primary Factor Regulating Excessive Cytokine Production

   https://doi.org/10.3390/pr8060719  

   Weaver, Jordan J.; Shoemaker, Jason E. (June 2020, Processes)

   null (Ed.)

   RNA viruses, such as influenza and Severe Acute Respiratory Syndrome (SARS), invoke excessive immune responses; however, the kinetics that regulate inflammatory responses within infected cells remain unresolved. Here, we develop a mathematical model of the RNA virus sensing pathways, to determine the intracellular events that primarily regulate interferon, an important protein for the activation and management of inflammation. Within the ordinary differential equation (ODE) model, we incorporate viral replication, cell death, interferon stimulated genes’ antagonistic effects on viral replication, and virus sensor protein (TLR and RIG-I) kinetics. The model is parameterized to influenza infection data using Markov chain Monte Carlo and then validated against infection data from an NS1 knockout strain of influenza, demonstrating that RIG-I antagonism significantly alters cytokine signaling trajectory. Global sensitivity analysis suggests that paracrine signaling is responsible for the majority of cytokine production, suggesting that rapid cytokine production may be best managed by influencing extracellular cytokine levels. As most of the model kinetics are host cell specific and not virus specific, the model presented provides an important step to modeling the intracellular immune dynamics of many RNA viruses, including the viruses responsible for SARS, Middle East Respiratory Syndrome (MERS), and Coronavirus Disease (COVID-19). 

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