(648a) Systems medicine for revealing immunopathogenic mechanisms of age-dependent respiratory infection severity

Influenza and other respiratory viruses remain a large global threat, as seasonal influenza epidemics result in 3-5 million cases and between 290,000 and 650,000 deaths each year, and the recent COVID-19 pandemic has led to over 229 million confirmed cases and 4.7 million deaths to date^1,2. With a median efficacy of 69% for influenza vaccines, there is an increasing need for an improved understanding of the innate and adaptive immune response to viral infections³. Most influenza cases result in mild to moderate respiratory infections, with severe outcomes of influenza infection typically reserved for high-risk patient groups: infants, young children, adults over the age of 65 and individuals with compromised immune systems⁴. Respiratory viruses have been repeatedly associated with acute lower respiratory infections (ALRI) in young children. These ALRI, which include pneumonia and bronchiolitis, are a leading cause of morbidity and mortality in children with approximately 156 million cases and 1.5 million deaths globally in 2008⁵. Unfortunately, the host factors in infection and mortality are not well understood and thus, it is imperative to develop a model describing childhood immune response to viral respiratory infections.

The aim of this project is to determine the rates of activation associated with immunity that differ across age groups, using mathematical models of the innate immunity. The innate immune system is the body’s first line of defense against infection that detects pathogens and leads to the production of type 1 interferons (IFNs) and inflammatory cytokines. In addition to activating the adaptive immune system, these molecules help to contain viral infection by inhibiting viral replication and assembly⁶. Additionally, children do not have a fully developed adaptive immune systems and thus rely more on their innate immunity to thwart off threats while their adaptive immunity develops. However, an underdeveloped adaptive immunity alone does not sufficiently account for differences in host response to infection⁷. For these reasons, our project focuses on the differences in age-related dynamics of the innate immune response. Prevailing mathematical models of the innate immune system, such as Saenz et. al. model and Hancioglu et. al. model, do not account for differences in host factors related to age^8,9. While some mathematical models examine the differences in host response to infection due to age, such as the Hernandez-Vargas et. al. model, they tend to focus on the elderly and do not study the host dynamics of children¹⁰. For this project, we will first thoroughly examine the prevailing mathematical models that contain common elements of the innate immune response to influenza infection. We will identify key cytokines and biomarkers as potential host factors and use this analysis to probe the dynamics of our models of interest in order to reveal the mechanisms behind age-related severity. We expect to observe higher levels of inflammation as numerous studies correlate elevations in the production of pro-inflammatory cytokine, IL-6 with the severity of symptoms in pediatric cases, as well as a diminished CD4+ T cell response due to elevated IL-10 levels. We will also examine the production levels of IFN- α, IFN- γ, and IL-12; all of which are expected to be decreased in children versus adults but whose impact on childhood immune response has not be thoroughly examined⁷. Ultimately, we will use these insights to create a mathematical model of innate immune signaling that can predict disease progression and severity for young children.

References

[1] Influenza (seasonal). (2018). Retrieved April 02, 2021, from https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal)

[2] WHO coronavirus (COVID-19) Dashboard. (2021). Retrieved September 24, 2021, from https://covid19.who.int/

[3] Osterhaus, A. (2014). Efficacy and effectiveness of influenza vaccines: A systematic review and meta-analysis. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. doi:10.3410/f.13470966.793495076

[4] Mochan, E., Ackerman, E. E. & Shoemaker, J. E. A systems and treatment perspective of models of influenza virus-induced host responses. Processes 6, 1–19 (2018).

[5] Nair, H. et al. (2011). Global burden of respiratory infections due to seasonal influenza in young children: A systematic review and meta-analysis. Lancet 378, 1917–1930

[6] Sun, L., Liu, S., & Chen, Z. J. (2010). Snapshot: Pathways of antiviral innate immunity. Cell, 140(3). doi:10.1016/j.cell.2010.01.041

[7] Coates, B. M., Staricha, K. L., Wiese, K. M. & Ridge, K. M. (2015). Influenza a virus infection, innate immunity, and childhood. JAMA Pediatr. 169, 956–963

[8] Saenz, R.A., et al (2010). Dynamics of influenza infection and pathology. J. Virol 84, 3974-3983

[9] Hancioglu, B., Swigon, D., Clermont, G. (2007). A dynamical model of human immune response to influenza A virus infection. J. Theor. Biol. 246, 70-86

[10] Hernandez-Vargas, et. al. (2014). Effects of Aging on Influenza Virus Infection Dynamics. Journal of Virology. 88, 4123-4131.