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A predictive fitness model for influenza

Abstract

The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

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Figure 1: Evolution of influenza clades.
Figure 2: Year-to-year predictions of HA evolution.
Figure 3: Vaccine selection.
Figure 4: Adaptation map of influenza.

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Acknowledgements

We acknowledge discussions with B. D. Greenbaum, B. Grenfell, C. Illingworth, A. Levine, J. W. McCauley, V. Mustonen, S. Pompei and R. Rabadan. This work has been partially supported by Deutsche Forschungsgemeinschaft grant SFB 680 and by German Federal Ministry of Education and Research grant 0315893-Sybacol. Part of this work was performed at the Kavli Institute of Theoretical Physics (Santa Barbara), which has been supported by National Science Foundation grant PHY05-51164.

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Both authors designed research, developed methods, analysed data, interpreted results and wrote the paper.

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Correspondence to Michael Lässig.

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Extended data figures and tables

Extended Data Figure 1 Statistical errors of predicted and posterior Wrightian fitness.

The frequency ratio plot (, ) of Fig. 2a is shown together with the standard deviation of the predicted ratio in the ensemble of reconstructed trees (vertical bars) and the standard deviation of the posterior ratio due to sampling fluctuations of population frequencies (horizontal bars). See sections 1, 3 and 4 of Methods.

Extended Data Figure 2 Fitness predictions for human influenza A/H1N1.

a, Wrightian fitness: the predicted frequency ratio is plotted against the posterior ratio for 81 HA clades with initial frequency  > 0.1. To be compared with Fig. 2a. b, Dynamics of the influenza strain tree: for each clade, the ancestor node is coloured according to the maximum of the predicted frequency changes, . To be compared with Fig. 2c. The predictions are based on a sample of 2,136 unique HA1 genotypes observed between 1977 and 2009. We restrict predictions to years when this sample contains at least 12 unique HA1 strains in the winter seasons t and t + 1, which are the years 1990, 1995–1998 and 2005–2008 (see Methods, section 4). These predictions are statistically significant (P < 10−18) but somewhat more noisy than for influenza A/H3N2 (clade growth is correctly predicted in 88% of the cases, decline in 63% of the cases). The reasons include a significantly smaller and more biased strain sample and a less comprehensive knowledge of antigenic epitope sites26. The prediction of influenza A/H1N1 evolution illustrates the broader applicability of our method and highlights the determinants of predictive power.

Extended Data Figure 3 Fitness flux in the evolution of influenza.

a, Fitness flux measures adaptation (schematic, adapted from ref. 29). The cumulative flux Φ(t), as defined in equations (48) and (49), is an aggregate measure of fitness changes due to frequency changes in a population’s history up to a given clade ν at a given time t (shown by uphill arrows)28,29,58. Left: in a static fitness landscape F(x), the flux Φ(t) equals the fitness difference between the initial point and the final point of this history. Right: in a time-dependent fitness seascape F(x, t), the flux Φ(t) is still a typically positive, increasing function of time, even if the population fitness does not increase. b, Mean cumulative fitness flux Φ(t) as given by equation (48) for influenza from 1993 up to season t. The mean flux inferred from our fitness model (black line) shows a continuous increase. The flux for a null model with scrambled clade fitness values (grey lines) fluctuates around 0. Vertical bars indicate the root mean squared fitness (flux) in each year’s strain sample, , as given by equation (51).

Extended Data Figure 4 Strain tree with mutations.

ac, Four classes of HA sequence mutations are mapped onto individual branches of the influenza strain tree: synonymous mutations (a, blue), nonsynonymous epitope mutations (b, green) and nonsynonymous non-epitope mutations (c, red). Each nonsynonymous mutation marks the origination of a clade in the population; each fixed clade (highlighted by bright colours) has an origination on the trunk of the tree (shown as thick line). The fixation probability, that is, the ratio of the number of fixations and the number of originations, is seen to be reduced for informative non-epitope changes and enhanced for nonsynonymous epitope changes compared to the baseline of synonymous changes; cf. Extended Data Fig. 5. The underlying tree (shown here from 1993 to 2012) is a sample from our ensemble of strain trees, which are constructed by maximum likelihood from the HA sequence of 3,944 strains (other equiprobable trees differ only in peripheral branches). The horizontal coordinate D of a node is its mutational distance from the root of the tree. The trunk of the tree (thick line) is the single lineage connecting past and future on timescales beyond the coalescence time.

Extended Data Figure 5 Selection on epitope and non-epitope changes.

The frequency propagator ratio9 g(X), as defined in equation (7), is shown for several classes of nonsynonymous HA mutations: mutations in epitopes A–D (green bullets), mutations in epitope E (green circles), mutations in sialic receptor binding sites (green diamonds), informative non-epitope mutations (red bullets) and non-informative non-epitope mutations (red circles). Error bars indicate sampling uncertainties. Mutations in epitopes A–D, including those in epitopic receptor binding sites, reach values g(X) ≥ 2.5 for large frequencies, signalling substantial positive selection. Mutations in epitope E are under weaker positive selection, with g(X) ≈ 1.5 for large frequencies. Informative non-epitope changes drop to g(X) = 0.6, signalling predominantly negative selection. Non-informative non-epitope changes evolve near the neutral baseline (g = 1, blue line), indicating weak or heterogeneous selection. See section 2 of Methods.

Extended Data Figure 6 Evolution of glycosylation.

a, Number of epitopic glycosylation sites, nep, in the influenza A/H3N2 strain sample between 1968 and 2012 (green lines): population mean value (thin line), root mean squared variation (error bars), and value for trunk lineages (thick line). The same data are shown for non-epitope glycosylation sites (red lines). Epitope sites show substantial changes with a net increase in number and substantial natural variation in some years, whereas non-epitope sites had only one fixation of a glycosylation site. b, Evolution of nep on the influenza strain tree between 1993 and 2012. Trunk strains show a rapid increase to nep = 5 between 1995 and 1997 and maintain this value in later years; the mean nep shows a slower increase between 1995 and 2001. Off-trunk clades drop below nep = 5 also in later years, and there are compensatory mutations back to nep = 5. The data suggest an adaptive increase of nep up to a saturation value nep = 5 after 1996. These observations inform the glycosylation fitness component (equation (22)), which is used to test the predictive value of glycosylation. See section 2 of Methods.

Extended Data Table 1 Ranking of fitness models

Supplementary information

Supplementary Data

This file contains the GenBank and Gisaid accession numbers of the influenza strains used in the study. (TXT 80 kb)

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Łuksza, M., Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014). https://doi.org/10.1038/nature13087

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