Phylogeographic Approaches to Characterize the Emergence of Plant Pathogens

Abstract

Phylogeography combines geographic information with phylogenetic and population genomic approaches to infer the evolutionary history of a species or population in a geographic context. This approach has been instrumental in understanding the emergence, spread, and evolution of a range of plant pathogens. In particular, phylogeography can address questions about where a pathogen originated, whether it is native or introduced, and when and how often introductions occurred. We review the theory, methods, and approaches underpinning phylogeographic inference and highlight applications providing novel insights into the emergence and spread of select pathogens. We hope that this review will be useful in assessing the power, pitfalls, and opportunities presented by various phylogeographic approaches.

HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations

“…We also demonstrated that HantaNet could be used for hantavirus genomic surveillance with the rapid creation of dashboards useful to further explore the spatiotemporal relationships of strain variants and potential human exposure hotspots based on viral sequence similarity. HantaNet and other MicrobeTrace-related applications could be modified for phylogeographic studies by integrating Markov chain (discrete or continuous trait models) and coalescent models for ancestral reconstruction [77], commonly used in the Bayesian Evolutionary Analysis Sampling Trees (BEAST) method [78][79][80] and Nextstrain platform [65,81,82] for phylogenetic inference. Bayesian inference methods can be timeconsuming, but more efficient approaches to build time-scaled phylogenies like TreeTime are available as open-source code [83] and could be integrated into HantaNet and Microbe-Trace.…”

Herbarium Specimen Sequencing Allows Precise Datation ofXanthomonas citripv.citriDiversification History

“…First, although with 163 genomes our dataset displayed the best representation of pathotype A genetic diversity published to date, the reconstructed phylogenetic tree exhibits high level of imbalance (with only 17 and 4 A* and A W genomes, respectively), a property previously shown to lead to reduced accuracy or precision of phylogenetic timescale estimates (Duchêne et al 2015a). Bias in representation of populations, such as overrepresentation of one compared to the others or the absence of representants from the true founder lineage, can also lead to the reconstruction of ancestral state tending to correspond to the oversampled population rather than the true founder lineage (Rasmussen et al 2021). Although this feature arises from the fact that Xci worldwide expansion mostly involved pathotype A strains (Pruvost et al 2014), future work should aim to better characterize the genomic diversity of A* and A W strains.…”

Genetic structure of Plasmopara halstedii populations across Europe and South Russia

“…Collection of genetic data and samples of obligate biotrophic pathogens distributed over large geographic areas is a logistically difficult task, but can provide valuable information about evolutionary history, taxonomy, population connectivity and genetic variation present in natural populations. Such phylogeographic studies can provide evidence of how the spread of pathogens is linked to human movement and trade (Molinero‐Ruiz, 2022; Rasmusen & Grünwald, 2021).…”