Widespread Historical Contingency in Influenza Viruses

Abstract

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

Mutation and Epistasis in Influenza Virus Evolution

“…Comparative DMS of NP and HA in H3N2 and H1N1 backgrounds have found that both short-range physical interactions and long-range functional interactions within these proteins are common [ 19 , 37 ]. Phylogenetic studies also find many long-range epistatic interactions [ 59 , 65 ]. Additionally, sites exhibiting epistasis cluster with each other, which can be explained by structural changes affecting a particular region of the protein [ 19 , 37 ].…”

“…The epistatic interactions in these antigenic clusters can lead to historical contingency. Mutations involved in a cluster transition also interact with mutations involved in the subsequent cluster transition, forming chains of interacting mutations [ 65 ]. These chains suggest that the fixation of each substitution is contingent on the fixation of prior substitutions.…”

Viral outbreaks involve destabilized evolutionary networks: evidence from Ebola, Influenza and Zika

“…Recently, in an attempt to understand the genomic determinants of antigenic properties and drug resistance in influenza viruses, we described a novel algorithm to uncover pair of amino acids in a protein that evolve in a correlated manner 8 . We found that influenza A viruses show extensive evidence for correlated evolution, to such an extent that some amino acids evolve correlatively with more than one other site, hereby forming dense (undirected) networks (see also 9 ).…”

Kullback Leibler divergence in complete bacterial and phage genomes

“…Such properties can be correlated with functional aspects encoded by the genome and can shed the light on its natural history, allowing the study of the organism’s evolution ( Adami, 2012 ; Gautier, 2000 ; Nasrallah & Huelsenbeck, 2013 ). For example, epistatic parameters and their statistical analysis gave clues on the evolution of influenza A virus ( Nshogozabahizi, Dench & Aris-Brosou, 2017 ).…”