Trade-offs between and within scales: environmental persistence and within-host fitness of avian influenza viruses

Abstract

Trade-offs between different components of a pathogen's replication and transmission cycle are thought to be common. A number of studies have identified trade-offs that emerge across scales, reflecting the tension between strategies that optimize within-host proliferation and large-scale population spread. Most of these studies are theoretical in nature, with direct experimental tests of such cross-scale trade-offs still rare. Here, we report an analysis of avian influenza A viruses across scales, focusing on the phenotype of temperature-dependent viral persistence. Taking advantage of a unique dataset that reports both environmental virus decay rates and strain-specific viral kinetics from duck challenge experiments, we show that the temperature-dependent environmental decay rate of a strain does not impact within-host virus load. Hence, for this phenotype, the scales of within-host infection dynamics and between-host environmental persistence do not seem to interact: viral fitness may be optimized on each scale without cross-scale trade-offs. Instead, we confirm the existence of a temperature-dependent persistence trade-off on a single scale, with some strains favouring environmental persistence in water at low temperatures while others reduce sensitivity to increasing temperatures. We show that this temperature-dependent trade-off is a robust phenomenon and does not depend on the details of data analysis. Our findings suggest that viruses might employ different environmental persistence strategies, which facilitates the coexistence of diverse strains in ecological niches. We conclude that a better understanding of the transmission and evolutionary dynamics of influenza A viruses probably requires empirical information regarding both within-host dynamics and environmental traits, integrated within a combined ecological and within-host framework.

Keywords: avian influenza; environmental persistence; mathematical model; multi-scale analysis.

Figure 1.

Virus decay rate for different influenza strains as function of temperature. Symbols show data, and lines show best fit. Panels (a,b) show fits plotted with linear and logarithmic y-axes of the exponential decay equation to the data assuming normally distributed errors. Panels (c,d) shows fits assuming lognormally distributed errors. Among the seven virus strains, five were isolated from dabbling ducks and two from surface lake water (marked with an asterisk (*)). See Material and methods for more details. (Online version in colour.)

Figure 2.

Best fits of the two within-host models to the virus load data for each infected duck. (For one strain data for only four animals is available.) Solid and dashed lines are best fits to the ordinary differential equation (ODE) model and phenomenological model, respectively. Note that the two alternative models produce results for most hosts that are almost indistinguishable. (Online version in colour.)

Figure 3.

Fitness as a function of within-host virus decay rate, c_w, at a temperature of 40°C. Panel (a) shows fitness determined using the non-mechanistic within-host model and decay rate determined using a normal error assumption. Panel (b) uses the same within-host model as (a) and a lognormal error assumption for the decay rate fits. Panels (c,d) are as (a,b) only the within-host fitness being determined using the mechanistic ODE model. Colours and symbols for the different virus strains are as shown in the previous figures. (Online version in colour.)

Figure 4.

Fitness as a function of environmental virus decay rate, c_e, at a temperature of 5°C. Everything as explained in figure 3 caption. (Online version in colour.)

Figure 5.

Correlation between low- (α) and high- (γ) temperature persistence. Panels (a,c) show correlation between absolute values, (b,d) between ranks of the parameters. Panels (a,b) show results for fits assuming normally distributed errors, panels (c,d) assume lognormally distributed errors. Also shown are linear regression lines with 95% confidence intervals. (Online version in colour.)