Genotype-phenotype landscapes for immune-pathogen coevolution

Abstract

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Keywords: deep mutational scanning; directed evolution; immune repertoire sequencing; mutagenesis.

Figure 1.. Inferring fitness from observational data.

(A) Muller diagram showing changes in frequency of lineages within a population over time. Each color refers to a specific genotype with a given fitness (corresponding to the phylogeny in (B); trajectories illustrate the changes in frequency of each lineage through time). These frequency changes can be used to infer fitness of each genotype (though as noted in the text, this may be confounded by population structure, sampling biases, and other factors). (B) For illustration purposes, a hypothetical reconstructed phylogeny from a population sample collected at a single timepoint is shown. The structure of this phylogeny can be used to infer the fitness effect of each mutation.

Figure 2.. High-throughput phenotypic measurements.

(A) Large libraries of protein sequence variants can be assayed for various phenotypes of interest by conducting some assay, grouping variants based on the result of this assay (e.g. selecting using flow cytometry based on binding to a fluorescently labeled antibody as in (B), or based on some other experimental selection pressure as in (C)), and sequencing to determine the relative enrichment of each variant within each group. (B) A FACS-based method to measure phenotypes such as binding or protein expression, in which the protein of interest is labeled with fluorescent tags for FACS-based cell sorting is shown. (C) A direct passaging method, in which viral variants are incubated with target cells and allowed to replicate is shown. After some time, successfully replicating (i.e., infectious) variants are isolated (95,136,137). NGS: next-generation sequencing.

Key Figure, Figure 3.. Strategies for exploring sequence space.

We show an example of a short 15-residue peptide (with genotype space consisting of 15²⁰ = 3×10²³ total possible sequence variants), which has evolved from a wildtype sequence to a mutant with five mutations at the sites indicated in blue. Shown are several types of variant libraries that explore different subsets of the sequence space (left) and lead to different observables (right). (A) Local Exploration. These methods focus on the local mutational landscape around a specific focal genotype (here the wildtype or mutant sequences) using saturating mutagenesis (i.e. constructing all genotypes one mutation away from the focal genotype, 20*L total variants) or random mutagenesis. Sequence logo plots (right) are commonly used to summarize the impact (in the focal genotype) of all possible mutations at each site on the phenotype. This approach surveys both negative and positive mutational effects but does not provide information on epistasis. (B) Combinatorial Exploration. Here the library contains all possible combinations of N mutations separating the mutant from the wildtype sequence (2^N total variants); both positive and negative mutational effects as well as epistatic interactions can be analyzed. All possible trajectories from wildtype to mutant sequences are shown on the right, with lines indicating the probability of each based on an evolutionary model given the measured phenotypes corresponding to each genotype (right). These probabilities can be used to define “pathway accessibility,” which refers to the likelihood of each possible pathway given a particular evolutionary model of natural selection and genetic drift. (C) Random exploration with selection. These libraries are generated with multiple rounds of random mutagenesis and selection; this approach is suited primarily to studying mutations with positive effects. The resulting population dynamics are shown on a Muller diagram, which shows the changes in frequency over time of each sequence variant (right).