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. 2014 Feb 25;9(2):e88817.
doi: 10.1371/journal.pone.0088817. eCollection 2014.

Neutrality, cross-immunity and subtype dominance in avian influenza viruses

Affiliations

Neutrality, cross-immunity and subtype dominance in avian influenza viruses

Vicki L Brown et al. PLoS One. .

Abstract

Avian influenza viruses (AIVs) are considered a threat for their potential to seed human influenza pandemics. Despite their acknowledged importance, there are significant unknowns regarding AIV transmission dynamics in their natural hosts, wild birds. Of particular interest is the difference in subtype dynamics between human and bird populations-in human populations, typically only two or three subtypes cocirculate, while avian populations are capable of simultaneously hosting a multitude of subtypes. One species in particular-ruddy turnstones (Arenaria interpres)--has been found to harbour a very wide range of AIV subtypes, which could make them a key player in the spread of new subtypes in wild bird populations. Very little is known about the mechanisms that drive subtype dynamics in this species, and here we address this gap in our knowledge. Taking advantage of two independent sources of data collected from ruddy turnstones in Delaware Bay, USA, we examine patterns of subtype diversity and dominance at this site. We compare these patterns to those produced by a stochastic, multi-strain transmission model to investigate possible mechanisms that are parsimonious with the observed subtype dynamics. We find, in agreement with earlier experimental work, that subtype differences are unnecessary to replicate the observed dynamics, and that neutrality alone is sufficient. We also evaluate the role of subtype cross-immunity and find that it is not necessary to generate patterns consistent with observations. This work offers new insights into the mechanisms behind subtype diversity and dominance in a species that has the potential to be a key player in AIV dynamics in wild bird populations.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Prevalence plots of datasets 1 and 2.
(a) A stacked bar chart of the approximate prevalence of HA subtypes. (b) The prevalence time series from both datasets for the four selected hemagglutinin (HA) subtypes. In both cases, the two datasets are separated by a gap on the x-axis. For dataset 1, a total of 4266 fecal or cloacal samples were collected over the time period 1985–2000 ; for dataset 2 the average annual sample size was 400.
Figure 2
Figure 2. Schematic of the model with two-subtypes depicted for illustration.
Hosts are born susceptible to both subtypes (formula image) and their subsequent status with respect to both subtypes is tracked. Infection events are represented by solid arrows while loss of immunity is depicted by dotted arrows.
Figure 3
Figure 3. Plots showing analyses of dominance and diversity patterns from datasets 1 and 2.
The Simpson’s diversity index for the four subtypes of interest from both datasets is presented in (a). Absolute change in rank against rank ((b) and (c)), change in rank against rank ((d) and (e)) and rank-abundance curves ((f) and (g)) are presented for each dataset - panel letters given refer to datasets 1 and 2 respectively. Panels (h)-(k) show correlations in the data, with (h)-(i) showing any significant correlations between subtype presence/absence for the complete datasets, and (j)-(k) showing any correlations between prevalence levels for the 4 subtypes of interest.
Figure 4
Figure 4. Model predicted prevalence curves for all three hosts for a variety of different parameter sets.
Panel (a) shows the prevalence curves for migratory ducks, resident ducks and ruddy turnstones for no cross-immunity and low transmission and consumption rates. Panel (b) shows the prevalence curves for all species for a cross-immunity rate of 0.5, with a higher transmission rate than in (a) and with a low consumption rate. Panel (c) shows the case with no cross-immunity, low transmission rate and an increased consumption rate (over (a) and (b)). Finally, panel (d) shows the prevalence curves when the cross-immunity rate is 0.5, transmission rate is low and consumption rate is greatly increased. In each panel, the histogram next to the ruddy turnstone (RUTU) prevalence curve is the histogram of the Simpson’s diversity index (SDI), as averaged over all simulations with the given parameter set. Simpson’s diversity index is calculated from a sample of the true prevalence, as calculated while the birds are present in Delaware Bay. See model description for more details on sampling. Note that "Mig" here stands for migrating ducks and "Res" denotes resident ducks.
Figure 5
Figure 5. Plots showing the Kolmogorov-Smirnov test results from comparing histograms of Simpson’s diversity index from the datasets and the model.
The histograms consist of 10 equal sized bins between 0 and 4 for both the datasets and the model. Panels (a) and (b) show whether or not the null hypothesis (that the histograms come from the same distribution) can be rejected -the result is one if it can be rejected, 0 otherwise. Panel (a) shows the results for varying cross-immunity and direct transmission rate in ruddy turnstones; (b) shows the results for varying cross-immunity and consumption rate. For each parameter set, the model histogram was constructed from the mean Simpson’s diversity index, as calculated from 10 model simulations.
Figure 6
Figure 6. Synthetic likelihoods comprising of change in rank vs. rank and rank-abundance curves between the model and data.
The heat maps in panels (a) and (b) depict 2-D likelihood profiles, with single pararmeter profiles shown in upper and left panels. The plots show the normalised SSEs for varying cross-immunity and either the ruddy turnstone transmission rate (a) or the consumption rate (b) against the metrics absolute change in rank vs. rank and rank-abundance. White space in both plots is the result of subtype extinctions leading to much reduced fits to the data (for more information on average number of subtypes coexisting for different parameter sets, see File S1). Panels (c) and (d) give the absolute change in rank vs rank (c) and rank-abundance curves (d) for the best fit estimate (as judged from the SSEs). Lighter lines (grey in (c), blue in (d)) show the results from individual realisations and darker lines (black in (c), red in (d)) show averages.
Figure 7
Figure 7. The Cartesian distance between Barycentric coordinates for both the model and data, and their respective autocorrelations.
Figures showing the Cartesian distance between Barycentric coordinates for both the data (a) and a model simulation using the best-fit parameter set (c). The autocorrelations for both of these are shown in figures (b) and (d) respectively. The 95% significance levels are not shown as their threshold is much larger than the calculated values.

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