Mapping the Evolutionary Potential of RNA Viruses

Abstract

The deterministic force of natural selection and stochastic influence of drift shape RNA virus evolution. New deep-sequencing and microfluidics technologies allow us to quantify the effect of mutations and trace the evolution of viral populations with single-genome and single-nucleotide resolution. Such experiments can reveal the topography of the genotype-fitness landscapes that shape the path of viral evolution. By combining historical analyses, like phylogenetic approaches, with high-throughput and high-resolution evolutionary experiments, we can observe parallel patterns of evolution that drive important phenotypic transitions. These developments provide a framework for quantifying and anticipating potential evolutionary events. Here, we examine emerging technologies that can map the selective landscapes of viruses, focusing on their application to pathogenic viruses. We identify areas where these technologies can bolster our ability to study the evolution of viruses and to anticipate and possibly intervene in evolutionary events and prevent viral disease.

Keywords: deep mutational scanning; evolutionary dynamics; experimental evolution; gain-of-function experiments; microfluidics; phylogenetics; virus evolution.

Figure 1. Example genotype-fitness landscapes

A.) The network of genotypes corresponding to a simple 5-locus, 2-mutation system, showing all 32 possible genotype combinations (2⁵) connected by single nucleotide substitutions. The likelihood of traversing a given link (acquiring a mutation) is shown as the line weight. Interactions between mutations (epistasis) shapes the complexity of the network. B.) From a starting point (grey dot), populations explore the topography of the genotype-fitness landscape by acquiring mutations. The slope of the landscape represents the change in fitness associated with genotypic change. Natural selection drives populations (grey lines) up the fitness gradient to local fitness maxima.

Figure 2. Experimental techniques to study viral evolutionary paths and aid in prediction

A. A 2D genotype-fitness landscape underlying a hypothetical phenotypic transition (from purple to gold). B. Traversal of a viral population through such a landscape can be examined in a number of experimental ways. Phylogenetic reconstruction examines the evolutionary history of a viral population. Experimental evolution techniques measure the precise trajectories a given viral population takes to navigate a particular selective environment. Library-based screening allows further manipulation of the viral population, by exploring the local topography of the genotype-fitness landscape. C.) High resolution sequencing, such as Circular Sequencing (CirSeq), allows for the tracking of most single nucleotide mutations in a viral population, even at low frequencies. The error correction of CirSeq relies on the generation of head-to-tail cDNA repeats from circularized, fragmented RNA. Only mutations occurring in a majority of these repeats are identified as true mutations. If combined with serial passaging, such high resolution sequencing experiments can be used to estimate fitness effects across the genome and describe the evolutionary landscapes of viruses. D.) Deep mutational scanning (DMS) uses successive rounds of mutagenic PCR to generate a library of all possible codon or nucleotide substitutions of a given sequence. This library is then cloned back into the viral sequence for rescue and creation of mutant viruses. This population undergoes some form of selection (e.g. antibody selection), and the frequency of codon mutants postselection is determined by deep sequencing. A variety of methods can be used to determine amino acid preference at individual site across the gene of interest. Such preferences can be used to better inform phylogenetic reconstruction of viral evolution. E.) Microfluidics allow for massively parallel evolutionary experimentation and fine control of evolutionary and selective parameters influencing the evolution of viral populations. The ability to observe individual host cells, and individual virus particles and genomes, enables the quantification of important biological heterogeneity and dynamics underlying the infection process.

Figure 3. Challenges in predicting viral evolution

A) The fitness landscapes encountered by viruses are dynamic, making the maximal fitness a moving target. B.) Contour plot showing the movement of the optimal genotype in response to the shifting landscape in A. C.) Changes in the selective environment change the fitness effects of specific mutations. D.) Bottleneck effect. In transmitting a population of mutants, smaller samples will not accurately represent the distribution of mutations in the parental population and will result in the loss of rare beneficial mutations in the most-fit class. E.) When small populations are transmitted, the mean fitness of the transmitted population is highly variable. F.) Epistatic interactions create historical contingency in the path of evolution and constrain populations evolution (0 = wild-type locus; 1 = mutant). Mutation at the second position (mutant B) is lethal on its own, but viable when epistatic with a mutation at the third position (mutants C and D).