CASE STUDY 3 (INFLUENZA)

Sheep granuloma
Sheep granuloma; K. Nygaard

Connecting scales via complex immunity-transmission relationships.

Influenza infection in humans can cause severe morbidity and accounts for tens of thousands of deaths annually in the US alone [1]. As demonstrated for other viruses (e.g., HIV [2, 3]), quantitative, mechanistic within-host models might inform efforts to limit both the severity and transmission of influenza infections. In contrast to HIV models, however, the descriptive and thus predictive power of influenza models has been hampered by the limited dynamical information content in data from human hosts and uncertainty in the applicability of animal models (see [4, 5]). Nevertheless, connecting models to time-courses of virus load and components of the immune response has led to insights at both within- and between-host scales (e.g., [6, 7]).

Connecting models to time-courses of virus load and components of the immune response has led to insights at both within- and between-host scales.

The next challenge is to strive for accurate prediction of transmission potential based on the within-host dynamics. This will require incorporation of at least two forms of cross-scale complexity: immunopathology and tissue tropism as predictors of transmission efficiency.

The innate immune response is the first line of host defense, and associated cytokines, including interferons and interleukins, can reduce host susceptibility and virus production by individual cells. However, immunopathology resulting from excessive production of these molecules is a significant predictor of mortality risk during influenza infection [8, 9]. Additionally, the destruction of target cells, whether directly by the virus or via the immune response, exacerbates disease. Virus load is therefore a rather poor predictor of disease severity (e.g.,[10, 11]).

When we explicitly relate different immune populations and drug interventions to the dynamics of infected cells, integrity of mucosal surfaces and viral shedding, we will be better able to understand the selective pressures on the virus.

K. Paaijmans
Malaria sporozoites; K. Paaijmans

All of this poses difficulty for cross-scale modeling. To connect immunity to disease severity, previous models have related symptom scores or other severity measures to interferons [12], to peak and total viral loads plus the duration of symptomatic infection [13], a combination of target cell death and interferons [14], or simply to the total number of infected cells [15]. While reasonable, these connections are not fully validated. To connect immunity to transmission, theoretical studies to date have used virus load, innate immune response strength (which can trigger transmission-promoting symptoms such as sneezing), and combinations of the two (e.g.,[14, 16, 17]), but validated within-host models will allow us to test whether such measures are indeed related to virus densities in expelled mucus. When we explicitly relate different immune populations and drug interventions to the dynamics of infected cells, integrity of mucosal surfaces and viral shedding, we will be better able to understand the selective pressures on the virus.

Incorporating these crucial complexities demands close collaboration between multiple theorists and experimentalists, in the context of an interactive network such as our RCN.

References:

  1. Thompson, W.W., et al., Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA, 2003. 289(2): p. 179-86.
  2. Perelson, A.S., Modelling viral and immune system dynamics. Nat Rev Immunol, 2002. 2: p. 28-36.
  3. Pinkerton, S.D., HIV transmission rate modeling: a primer, review, and extension. AIDS Behav, 2012. 16(4): p. 791-6.
  4. Beauchemin, C.A. and A. Handel, A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead. BMC Public Health, 2011. 11 Suppl 1: p. S7.
  5. Dobrovolny, H.M., et al., Assessing mathematical models of influenza infections using features of the immune response. PLoS One, 2013. 8(2): p. e57088.
  6. Beauchemin, C.A., et al., Modeling amantadine treatment of influenza A virus in vitro. J Theor Bio, 2008. 254(2): p. 439-51.
  7. Handel, A., I.M. Longini, Jr., and R. Antia, Neuraminidase inhibitor resistance in influenza: assessing the danger of its generation and spread. PLoS Comput Bio, 2007. 3(12): p. e240.
  8. Wang, S., et al., Influenza virus-cytokine-protease cycle in the pathogenesis of vascular hyperpermeability in severe influenza. J Infect Dis, 2010. 202(7): p. 991-1001.
  9. Van Reeth, K., S. Van Gucht, and M. Pensaert, Correlations Between Lung Proinflammatory Cytokine Levels, Virus Replication, and Disease after Swine Influenza Virus Challenge of Vaccination-Immune Pigs. Viral Immunol, 2002. 15: p. 583-594.
  10. Lee, C.K., et al., Comparison of pandemic (H1N1) 2009 and seasonal influenza viral loads, Singapore. Emerg Infect Dis, 2011. 17(2): p. 287-91.
  11. Lu, P.X., et al., Relationship between respiratory viral load and lung lesion severity: a study in 24 cases of pandemic H1N1 2009 influenza A pneumonia. J Thoracic Dis, 2012. 4(4): p. 377-83.
  12. Canini, L. and F. Carrat, Population modeling of influenza A/H1N1 virus kinetics and symptom dynamics. J Virol, 2011. 85(6): p. 2764-70.
  13. Dobrovolny, H.M., et al., Exploring cell tropism as a possible contributor to influenza infection severity. PLoS One, 2010. 5(11): p. e13811.
  14. Reperant, L.A., et al., Linking influenza virus tissue tropism to population-level reproductive fitness. PLoS One, 2012. 7(8): p. e43115.
  15. Saenz, R.A., et al., Dynamics of influenza virus infection and pathology. J Virol, 2010. 84(8): p. 3974-83.
  16. Handel, A., et al., A multi-scale analysis of influenza A virus fitness trade-offs due to temperature-dependent virus persistence. PLoS Comput Biol, 2013. 9(3): p. e1002989.
  17. Ferguson, N.M., et al., Strategies for mitigating an influenza pandemic. Nature, 2006. 442(7101): p. 448-52.