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PhyloNetworks is a Julia package for the inference, manipulation, visualization, and use of phylogenetic networks in an interactive environment. Inference of phylogenetic networks is done with maximum pseudolikelihood from gene trees or multi-locus sequences (SNaQ), with possible bootstrap analysis. PhyloNetworks is the first software providing tools to summarize a set of networks (from a bootstrap or posterior sample) with measures of tree edge support, hybrid edge support, and hybrid node support. Networks can be used for phylogenetic comparative analysis of continuous traits, to estimate ancestral states or do a phylogenetic regression. The software is available in open source and with documentation at https://github.com/crsl4/PhyloNetworks.jl

Genomic data collected from viral outbreaks can be exploited to reconstruct the dispersal history of viral lineages in a two-dimensional space using continuous phylogeographic inference. These spatially explicit reconstructions can subsequently be used to estimate dispersal metrics that can be informative of the dispersal dynamics and the capacity to spread among hosts. Heterogeneous sampling efforts of genomic sequences can however impact the accuracy of phylogeographic dispersal metrics. While the impact of spatial sampling bias on the outcomes of continuous phylogeographic inference has previously been explored, the impact of sampling intensity (i.e., sampling size) when aiming to characterise dispersal patterns through continuous phylogeographic reconstructions has not yet been thoroughly evaluated. In our study, we use simulations to evaluate the robustness of 3 dispersal metrics — a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance (IBD) signal metric — to the sampling intensity. Our results reveal that both the diffusion coefficient and IBD signal metrics appear to be the most robust to the number of samples considered for the phylogeographic reconstruction. We then use these 2 dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of IBD patterns and diffusion coefficients mostly reflecting the dispersal capacity of the main infected host species but also, in some cases, the likely signature of rapid and/or long-distance dispersal events driven by human-mediated movements through animal trade. Overall, our study provides key recommendations for the use of lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has substantially impacted public, veterinary, and wildlife health. We apply an analytical workflow to a comprehensive WNV genome collection to test the impact of environmental factors on the dispersal of viral lineages and on viral population genetic diversity through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. By contrasting inference with simulation, we find no evidence for viral lineages to preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient. Classical epidemiological approaches have been limited in their ability to formally test hypotheses. Here, Dellicour et al. illustrate how phylodynamic and phylogeographic analyses can be leveraged for hypothesis testing in molecular epidemiology using West Nile virus in North America as an example.

Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference.

Molecular mechanisms determining pathogenicity of the Dutch elm disease fungus Ophiostoma novo-ulmi are poorly understood. Prior identification of the pathogenicity locus pat1 prompted a chromosome walking approach to elucidate gene function in this region. Among 17 identified genes, ONUg0282 (amtA) was predicted to encode a high-affinity ammonium transporter. In silico analyses confirmed the presence of four additional amt genes (amtB, amtC, amtD, and amtE) in both O. novo-ulmi and the less aggressive O. ulmi and that amtA and amtB belong to the Saccharomyces cerevisiae mep2 clade. The predicted amtA gene product showed features of Mep2-type transceptors, including amino acid residues corresponding to His-168 and His-318 in Escherichia coli AmtB protein, 11 transmembrane helices, and a conserved 22 amino acid motif immediately downstream of the last transmembrane helix. A knockdown amtA mutant with 25% residual expression was significantly less aggressive than wild-type O. novo-ulmi strain H327 when infecting Ulmus americana × U. parvifolia saplings. Predicted AmtA transporters from two CRISPR-Cas9 knockout mutants contained only five intact transmembrane helices. The ΔamtA mutants retained several wild-type phenotypic traits, including yeast–mycelium dimorphism, but were significantly less aggressive than H327 towards U. americana saplings. We concluded that ONUg0282 is an important determinant of aggressiveness in O. novo-ulmi.

Rice yellow mottle virus (RYMV) is a major pathogen of rice in Africa. RYMV has a narrow host range limited to rice and a few related poaceae species. We explore the links between the spread of RYMV in East Africa and rice history since the second half of the 19 th century. The phylogeography of RYMV in East Africa was reconstructed from coat protein gene sequences (ORF4) of 335 isolates sampled over two million square kilometers between 1966 and 2020. Dispersal patterns obtained from ORF2a and ORF2b, and full-length sequences converged to the same scenario. The following imprints of rice cultivation on RYMV epidemiology were unveiled. RYMV emerged in the middle of the 19 th century in the Eastern Arc Mountains where slash-and-burn rice cultivation was practiced. Several spillovers from wild hosts to cultivated rice occurred. RYMV was then rapidly introduced into the nearby large rice growing Kilombero valley and Morogoro region. Harvested seeds are contaminated by debris of virus infected plants that subsist after threshing and winnowing. Long-distance dispersal of RYMV is consistent (i) with rice introduction along the caravan routes from the Indian Ocean Coast to Lake Victoria in the second half of the 19 th century, (ii) seed movement from East Africa to West Africa at the end of the 19 th century, from Lake Victoria to the north of Ethiopia in the second half of the 20 th century and to Madagascar at the end of the 20 th century, (iii) and, unexpectedly, with rice transport at the end of the First World War as a troop staple food from the Kilombero valley towards the South of Lake Malawi. Overall, RYMV dispersal was associated to a broad range of human activities, some unsuspected. Consequently, RYMV has a wide dispersal capacity. Its dispersal metrics estimated from phylogeographic reconstructions are similar to those of highly mobile zoonotic viruses.

The Ascomycete Ophiostoma novo-ulmi threatens elm populations worldwide. The molecular mechanisms underlying its pathogenicity and virulence are still largely uncharacterized. As part of a collaborative study of the O. novo-ulmi-elm interactome, we analyzed the O. novo-ulmi ssp. americana transcriptomes obtained by deep sequencing of messenger RNAs recovered from Ulmus americana saplings from one resistant (Valley Forge, VF) and one susceptible (S) elm genotypes at 0 and 96 h post-inoculation (hpi). Transcripts were identified for 6424 of the 8640 protein-coding genes annotated in the O. novo-ulmi nuclear genome. A total of 1439 genes expressed in planta had orthologs in the PHI-base curated database of genes involved in host-pathogen interactions, whereas 472 genes were considered differentially expressed (DEG) in S elms (370 genes) and VF elms (102 genes) at 96 hpi. Gene ontology (GO) terms for processes and activities associated with transport and transmembrane transport accounted for half (27/55) of GO terms that were significantly enriched in fungal genes upregulated in S elms, whereas the 22 GO terms enriched in genes overexpressed in VF elms included nine GO terms associated with metabolism, catabolism and transport of carbohydrates. Weighted gene co-expression network analysis identified three modules that were significantly associated with higher gene expression in S elms. The three modules accounted for 727 genes expressed in planta and included 103 DEGs upregulated in S elms. Knockdown- and knockout mutants were obtained for eight O. novo-ulmi genes. Although mutants remained virulent towards U. americana saplings, we identified a large repertoire of additional candidate O. novo-ulmi pathogenicity genes for functional validation by loss-of-function approaches.

To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein–Uhlenbeck (OU) process is sometimes preferred to the simple Brownian motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence of shifts, phylogenetic Principal Component Analysis fails to decorrelate traits efficiently, so that any method aiming at finding shifts needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU, which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a rescaled tree to our multivariate framework. We describe an Expectation–Maximization (EM) algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method (ℓ1ou) on a wide range of simulated scenarios and use this new framework to reanalyze recently gathered data sets on New World Monkeys and Anolis lizards.

Background The highly aggressive pathogenic fungus Ophiostoma novo-ulmi continues to be a serious threat to the American elm ( Ulmus americana ) in North America. Extensive studies have been conducted in North America to understand the mechanisms of virulence of this introduced pathogen and its evolving population structure, with a view to identifying potential strategies for the control of Dutch elm disease. As part of a larger study to examine the genomes of economically important Ophiostoma spp. and the genetic basis of virulence, we have constructed an expressed sequence tag (EST) library using total RNA extracted from the yeast-like growth phase of O. novo-ulmi (isolate H327). Results A total of 4,386 readable EST sequences were annotated by determining their closest matches to known or theoretical sequences in public databases by BLASTX analysis. Searches matched 2,093 sequences to entries found in Genbank, including 1,761 matches with known proteins and 332 matches with unknown (hypothetical/predicted) proteins. Known proteins included a collection of 880 unique transcripts which were categorized to obtain a functional profile of the transcriptome and to evaluate physiological function. These assignments yielded 20 primary functional categories (FunCat), the largest including Metabolism (FunCat 01, 20.28% of total), Sub-cellular localization (70, 10.23%), Protein synthesis (12, 10.14%), Transcription (11, 8.27%), Biogenesis of cellular components (42, 8.15%), Cellular transport, facilitation and routes (20, 6.08%), Classification unresolved (98, 5.80%), Cell rescue, defence and virulence (32, 5.31%) and the unclassified category, or known sequences of unknown metabolic function (99, 7.5%). A list of specific transcripts of interest was compiled to initiate an evaluation of their impact upon strain virulence in subsequent studies. Conclusions This is the first large-scale study of the O. novo-ulmi transcriptome. The expression profile obtained from the yeast-like growth phase of this species will facilitate a multigenic approach to gene expression studies to assess their role in the determination of pathogenicity for this species. The identification and evaluation of gene targets in such studies will be a prerequisite to the development of biological control strategies for this pathogen.

The goal of phylogenetic comparative methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species’ traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. One natural extension of the BM is to use a weighted average model for the trait of a hybrid, at a reticulation point. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel’s λ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a data set of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.