Joint Inference of Migration and Reassortment Patterns for Viruses with Segmented Genomes

The Number and Pattern of Viral Genomic Reassortments are not Necessarily Identifiable from Segment Trees

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Since large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the "first-infection tree," a potentially counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our observation of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment, but also the four additional situations that can complicate the inference of reassortment.

MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

Phylodynamic approaches to studying avian influenza virus

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to new viral variants. Large-scale genome rearrangements, such as reassortment, have the potential to quickly generate new phenotypes, making the understanding of viral reassortment important to both evolutionary biology and public health research. In this paper, we argue that reassortment cannot be reliably inferred from incongruities between segment phylogenies using the established remove-and-rejoin or coalescent approaches. We instead show that reassortment must be considered in the context of a broader population process that includes the dynamics of the infected hosts. Using illustrative examples and simulation we identify four types of evolutionary events that are difficult or impossible to reconstruct with incongruence-based methods. Further, we show that these specific situations are very common and will likely occur even in small samples. Finally, we argue that existing methods can be augmented or modified to account for all the problematic situations that we identify in this paper. Robust assessment of the role of reassortment in viral evolution is difficult, and we hope to provide conceptual clarity on some important methodological issues that can arise in the development of the next generation of tools for studying reassortment.

State-dependent evolutionary models reveal modes of solid tumour growth

Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.

Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination

Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungus Aspergillus flavus sampled across the United States to explore patterns of recombination and migration across the genome.

Phylogeographic methods are widely used to reconstruct the historical movement of individuals between different populations. When applied to infectious pathogens, these methods are often used to reconstruct the origin or source of novel pathogen lineages. Most existing phylogeographic methods reconstruct movement based on a single phylogenetic tree, which is assumed to reflect the genetic ancestry of all sampled individuals. However in populations undergoing recombination, genetic material can be exchanged between lineages such that individuals may inherit different regions of their genome from different ancestors. In this case, phylogenetic relationships among individuals can only be captured by a reticulated network rather than any single tree. Ancestral Recombination Graphs (ARGs) provide one way of capturing these reticulate relationships and we develop new models that allow for demographic inference of historical population sizes, recombination rates and migration rates between subpopulations from ARGs. By accounting for recombination, our models not only allow for accurate demographic inference, but can take full advantage of the additional information contained in ARGs about how ancestry varies across genomes to more precisely reconstruct the movement of genetic material between populations.