Epistatically constrained mutations are fixed in human but not swine NP.

<p>All single mutations that occurred along the evolutionary trajectories were introduced individually into the Aichi/1968 (human NP) or swine/Wisconsin/1957 (swine NP), and the impact of the mutation on the total transcriptional activity of the influenza polymerase was measured experimentally. (A) The effect of the mutations to human NP, as originally reported in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen.1004328-Gong1" target="_blank">[34]</a>. (B) The effect of the mutations to swine NP. Individual mutations that are strongly deleterious are classified as “epistatically constrained,” since their fixation during natural evolution required additional secondary mutations to counteract the deleterious effects. Three epistatically constrained mutations fixed along the human NP trajectory, but no epistatically constrained mutations fixed along the swine NP trajectory. The epistatically constrained mutations are colored red in the plot. The numerical data in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen-1004328-g005" target="_blank">Figure 5A</a> are in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen.1004328-Gong1" target="_blank">[34]</a>; the numerical data in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen-1004328-g005" target="_blank">Figure 5B</a> are in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen.1004328.s004" target="_blank">Table S3</a>.</p

Plaque growth of influenza A/WSN/33 (H1N1) viruses carrying mutations in hemagglutinin.

<p>Results are for wildtype (WT), temperature-sensitive (ts), and ts virus with predicted stabilizing mutations. The plaques are scored as ++ for clear plaques, + for smaller or opaque plaques, +/− for barely distinguishable plaques, − for no plaques, and ND for not determined. The first mutation numbers are for sequential numbering of the A/WSN/33 hemagglutinin sequence beginning with zero at the N-terminal methionine, while the numbers in parentheses correspond to those used in the crystal structure with PDB code 1RVZ.</p

Phylogenetic tree of human and swine NP homologs.

<p>The human and swine NP lineages in this tree are descended from a virus closely related to the 1918 virus. Swine viruses are highlighted in yellow; all other viruses are human. In red are the lines of descent to the human H3N2 strains Aichi/1968 and Texas/2012 from their most-recent common ancestor. In green are the lines of descent to the swine H1N1 strains swine/Wisconsin/1957 and swine/Indiana/2012 from their most-recent common ancestor. Overall, this tree shows NPs from the following lineages: human seasonal H1N1, human H2N2, human H3N2, and North American swine viruses. The tree is a maximum clade credibility summary of a posterior distribution sampled from date-stamped protein sequences using BEAST <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen.1004328-Drummond1" target="_blank">[51]</a> with a JTT <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004328#pgen.1004328-Jones1" target="_blank">[66]</a> substitution model. See <a href="http://jbloom.github.io/mutpath/example_influenza_NP_1918_Descended.html" target="_blank">http://jbloom.github.io/mutpath/example_influenza_NP_1918_Descended.html</a> for code, input data, and detailed documentation.</p

Experimentally measured and predicted values for the 109-residue thioredoxin protein.

<p>The plots at left show the predictions made by the CUPSAT physicochemical modeling program, the consensus approach, and the PIPS phylogenetic inference program using the informative, regularizing, and hydrophobicity. To the right is the phylogenetic tree of 213 sequences that was utilized by the PIPS program. The values are the squared Pearson correlation coefficients.</p

An example phylogenetic tree .

<p>This tree shows the sequence data for five sequences at a single site . The amino acid codes at the tips of the branches () show the residue identities for the five sequences at this site. The variables at the internal nodes () are the amino acid identities at the site for the ancestral sequences, and must be inferred. The branch lengths () are proportional to the time since the divergence of the sequences.</p

Epistatically Interacting Substitutions Are Enriched during Adaptive Protein Evolution

<div><p>Most experimental studies of epistasis in evolution have focused on adaptive changes—but adaptation accounts for only a portion of total evolutionary change. Are the patterns of epistasis during adaptation representative of evolution more broadly? We address this question by examining a pair of protein homologs, of which only one is subject to a well-defined pressure for adaptive change. Specifically, we compare the nucleoproteins from human and swine influenza. Human influenza is under continual selection to evade recognition by acquired immune memory, while swine influenza experiences less such selection due to the fact that pigs are less likely to be infected with influenza repeatedly in a lifetime. Mutations in some types of immune epitopes are therefore much more strongly adaptive to human than swine influenza—here we focus on epitopes targeted by human cytotoxic T lymphocytes. The nucleoproteins of human and swine influenza possess nearly identical numbers of such epitopes. However, mutations in these epitopes are fixed significantly more frequently in human than in swine influenza, presumably because these epitope mutations are adaptive only to human influenza. Experimentally, we find that epistatically constrained mutations are fixed only in the adaptively evolving human influenza lineage, where they occur at sites that are enriched in epitopes. Overall, our results demonstrate that epistatically interacting substitutions are enriched during adaptation, suggesting that the prevalence of epistasis is dependent on the underlying evolutionary forces at play.</p></div

Performance of the phylogenetic inference approach as a function of the number of sequences used.

<p>The PIPS predictions using informative priors were run using subsets of all of the available protein sequences. The resulting predictions were then correlated with the experimental values (top) or the PIPS predictions obtained using all available sequences (bottom). The values are the squared Pearson correlation coefficients. For each number of sequences used, the PIPS predictions were made using 10 different random sequence subsets, and the displayed values are the average correlations over these 10 subsets. For cold shock protein, the subsets were made at intervals of 20 sequences, while for ribonuclease HI and thioredoxin they were made at intervals of 10 sequences.</p

Human NP exhibits increased evolution in CTL epitopes relative to swine NP.

<p>The number of CTL epitopes per site for all sites in NP versus those that substituted along the evolutionary trajectories for (A) human and (B) swine influenza. In human influenza, the substituted sites contain more epitopes than average sites – but in swine influenza, the substituted sites contribute to fewer epitopes than average sites. The P-values on the plots are the fraction of random subsets of all sites that contain as many (human NP) or as few (swine NP) total epitopes as the sites that actually substituted during the natural evolution of that homolog. The hypothesis of greatest interest is whether the substituted sites in the human NP contain more epitopes than do substituted sites in the swine NP. To test this hypothesis, we drew paired random subsets of sites from the human and swine NP homolog of the same size as the actual numbers of substituted sites for each homolog, and determined the fraction of these paired random subsets in which the number of epitopes for the human NP exceeded that for the swine NP by at least as much as for the actual data. This test gives a P-value of 0.008, supporting the hypothesis that human NP exhibits an increased rate of evolution in epitopes relative to swine NP. See <a href="http://jbloom.github.io/epitopefinder/example_NP_CTL_epitopes_H3N2_and_swine.html" target="_blank">http://jbloom.github.io/epitopefinder/example_NP_CTL_epitopes_H3N2_and_swine.html</a> for code, input data, and detailed documentation.</p

Prior distributions, , over the values.

<p>The “regularizing priors” are peaked at the moderately destabilizing value of to capture the general knowledge that most mutations are destabilizing. The “hydrophobic priors” capture the knowledge that mutations that cause large changes in hydrophobicity are often more destabilizing. These priors are peaked at a value equal the the absolute value of the difference in amino acid hydrophobicity (as defined by the widely used Kyte-Doolittle scale <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000349#pcbi.1000349-Kyte1" target="_blank">[81]</a>). For example, the prior for a mutation from hydrophobic valine (V) to similarly hydrophobic leucine (L) is peaked near zero, while that for mutation from valine to charged lysine (K) is peaked at a much more destabilizing value. The “informative priors” are peaked at the values predicted by the state-of-the-art physicochemically based program CUPSAT <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000349#pcbi.1000349-Parthiban1" target="_blank">[8]</a>, and so are designed to leverage extensive pre-existing knowledge about values. All the priors are fairly loose to make the values responsive to their effect on the likelihood. The priors also help regularize <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000349#pcbi.1000349-Chen1" target="_blank">[80]</a> the predictions by biasing them towards a reasonable range.</p

Experimentally measured and predicted values for the 156-residue ribonuclease HI protein.

<p>The plots at left show the predictions made by the CUPSAT physicochemical modeling program, the consensus approach, and the PIPS phylogenetic inference program using the informative, regularizing, and hydrophobicity priors. To the right is the phylogenetic tree of 239 sequences that was utilized by the PIPS program. The values are the squared Pearson correlation coefficients.</p