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Abstract Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. Here, we present TreeTime, a Python-based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The runtime of TreeTime scales linearly with dataset size.

H11 avian influenza viruses (AIVs) circulate globally among wild bird populations, yet their evolutionary pathways and risks for zoonotic transmission remain insufficiently characterized. In the current study, analysis of 1,179 H11 hemagglutinin (HA) sequences revealed that dispersal patterns closely follow Eurasian migratory flyways, with Xinjiang identified as a seasonal transit and amplification node. From 2,674 wild-bird samples, two viruses-K77/H11N2 and BL0/H11N3-were isolated (2/2674, approximately 0.07%). Genomic analyses positioned both viruses within Eurasian wild-bird lineages, exhibiting broad relatedness to AIVs from South Asia, East Asia, Europe, and Antarctica. HA profiles indicated a predominant avian receptor affinity; notably, K77/H11N2 exhibited prominent α2,3 binding as well as weaker, concentration-dependent α2,6 binding, and harbored putative mammalian-adaptive markers PB2-I292 V and NP-52H/313F, suggesting potential for enhanced viral fitness in mammalian cells. These markers were absent in BL0/H11N3. In vitro and in vivo analyses revealed greater HA stability, moderate replication in A549 cells, and localized lung inflammation in mice for K77/H11N2, while BL0/H11N3 demonstrated limited replication and minimal pathogenicity. Both viruses achieved systemic dissemination and direct-contact transmission among chickens, corresponding to high poultry seroprevalence rates (up to 65%). Antibodies detected in cattle (3.8%) and camels (∼2%) on farms near migratory-bird habitats provide rare evidence of natural H11 exposure in mammals. Collectively, this study presents the first comprehensive genomic and phenotypic characterization of H11 viruses detected in Xinjiang, underscoring the emergence of an H11N2 strain with limited mammalian adaptation and highlighting the need for intensified surveillance at the wildlife-poultry-livestock interface.

Mpox is an infectious disease caused by the Monkeypox virus, which is divided into two main genetic clades: Clade I and Clade II. A large-scale outbreak linked to Clade IIb emerged in 2022 and rapidly spread to more than 100 countries worldwide. Here, we describe the first and only documented Mpox outbreak in Cambodia (2023-2024), the public health outbreak response efforts and analysis, and integrating epidemiological and genomic approaches.To investigate the outbreak, samples from suspect cases were confirmed using qPCR before virus whole genome sequences were obtained for phylogenomic analyses.Epidemiological investigation revealed transmission primarily through intimate contact within socially connected networks, exclusively among men who have sex with men. None of the confirmed cases reported recent international travel or zoonotic exposure. Phylogenomic analysis showed that all Cambodian Mpox genomes belonged to lineage C.1, nested within Clade IIb. Bayesian analysis of publicly available C.1 genomes indicated that the most closely related sequence was from Thailand. Monophyletic clustering of Cambodian sequences, alongside a high proportion of APOBEC3 mutations, indicates localized human-to-human transmission after introduction.Altogether, these results illustrate the risk of regional lineages like C.1 introducing Mpox into previously unaffected countries, where socially connected human networks can sustain outbreaks despite control efforts.

Inferring past population dynamics over time from heterochronous molecular sequence data is often achieved using the Bayesian Skygrid model, a nonparametric coalescent model that estimates the effective population size over time. Available in BEAST, a cross-platform program for Bayesian analysis of molecular sequences using Markov chain Monte Carlo, this coalescent model is often estimated in conjunction with a molecular clock model to produce time-stamped phylogenetic trees. We here provide a practical guide to using BEAST and its accompanying applications for the purpose of drawing inference under these models. We focus on best practices, potential pitfalls, and recommendations that can be generalized to other software packages for Bayesian inference. This protocol shows how to use TempEst, BEAUti, and BEAST 1.10 (http://beast.community/; last accessed July 29, 2019), LogCombiner as well as Tracer in a complete workflow.

Abstract Since 2016, A(H5Nx) high pathogenic avian influenza (HPAI) virus of clade 2.3.4.4b has become one of the most serious global threats not only to wild and domestic birds, but also to public health. In recent years, important changes in the ecology, epidemiology, and evolution of this virus have been reported, with an unprecedented global diffusion and variety of affected birds and mammalian species. After the two consecutive and devastating epidemic waves in Europe in 2020-2021 and 2021-2022, with the second one recognized as one of the largest epidemics recorded so far, this clade has begun to circulate endemically in European wild bird populations. This study used the complete genomes of 1,956 European HPAI A(H5Nx) viruses to investigate the virus evolution during this varying epidemiological outline. We investigated the spatiotemporal patterns of A(H5Nx) virus diffusion to/from and within Europe during the 2020-2021 and 2021-2022 epidemic waves, providing evidence of ongoing changes in transmission dynamics and disease epidemiology. We demonstrated the high genetic diversity of the circulating viruses, which have undergone frequent reassortment events, providing for the first time a complete overview and a proposed nomenclature of the multiple genotypes circulating in Europe in 2020-2022. We described the emergence of a new genotype with gull adapted genes, which offered the virus the opportunity to occupy new ecological niches, driving the disease endemicity in the European wild bird population. The high propensity of the virus for reassortment, its jumps to a progressively wider number of host species, including mammals, and the rapid acquisition of adaptive mutations make the trend of virus evolution and spread difficult to predict in this unfailing evolving scenario.

Most new pathogens of humans and animals arise via switching events from distinct host species. However, our understanding of the evolutionary and ecological drivers of successful host adaptation, expansion, and dissemination are limited. Staphylococcus aureus is a major bacterial pathogen of humans and a leading cause of mastitis in dairy cows worldwide. Here we trace the evolutionary history of bovine S. aureus using a global dataset of 10,254 S. aureus genomes including 1,896 bovine isolates from 32 countries in 6 continents. We identified 7 major contemporary endemic clones of S. aureus causing bovine mastitis around the world and traced them back to 4 independent host-jump events from humans that occurred up to 2,500 y ago. Individual clones emerged and underwent clonal expansion from the mid-19th to late 20th century coinciding with the commercialization and industrialization of dairy farming, and older lineages have become globally distributed via established cattle trade links. Importantly, we identified lineage-dependent differences in the frequency of host transmission events between humans and cows in both directions revealing high risk clones threatening veterinary and human health. Finally, pangenome network analysis revealed that some bovine S. aureus lineages contained distinct sets of bovine-associated genes, consistent with multiple trajectories to host adaptation via gene acquisition. Taken together, we have dissected the evolutionary history of a major endemic pathogen of livestock providing a comprehensive temporal, geographic, and gene-level perspective of its remarkable success.

Epidemiology has been transformed by the advent of Bayesian phylodynamic models that allow researchers to infer the geographic history of pathogen dispersal over a set of discrete geographic areas (1, 2). These models provide powerful tools for understanding the spatial dynamics of disease outbreaks, but contain many parameters that are inferred from minimal geographic information (i.e., the single area in which each pathogen was sampled). Consequently, inferences under these models are inherently sensitive to our prior assumptions about the model parameters. Here, we demonstrate that the default priors used in empirical phylodynamic studies make strong and biologically unrealistic assumptions about the underlying geographic process. We provide empirical evidence that these unrealistic priors strongly (and adversely) impact commonly reported aspects of epidemiological studies, including: 1) the relative rates of dispersal between areas; 2) the importance of dispersal routes for the spread of pathogens among areas; 3) the number of dispersal events between areas, and; 4) the ancestral area in which a given outbreak originated. We offer strategies to avoid these problems, and develop tools to help researchers specify more biologically reasonable prior models that will realize the full potential of phylodynamic methods to elucidate pathogen biology and, ultimately, inform surveillance and monitoring policies to mitigate the impacts of disease outbreaks.

In a striking result, Louca and Pennell [S. Louca, M.W. Pennell, Nature 580, 502–505 (2020)] recently proved that a large class of phylogenetic birth–death models is statistically unidentifiable from lineage-through-time (LTT) data: Any pair of sufficiently smooth birth and death rate functions is “congruent” to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth–death models to study evolution. In this paper, we qualify their finding by proving that an alternative and widely used class of birth–death models is indeed identifiable. Specifically, we show that piecewise constant birth–death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant timetree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth–death model class can be arbitrarily closely approximated by a class of identifiable models.The sampling requirements needed for our results to hold are explicit and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic.

Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the “Taming the Beast” (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.

Phylodynamic methods provide a coherent framework for the inference of population parameters directly from genetic data. They are an important tool for understanding both the spread of epidemics as well as long-term macroevolutionary trends in speciation and extinction. In particular, phylodynamic methods based on multitype birth–death models have been used to infer the evolution of discrete traits, the movement of individuals or pathogens between geographic locations or host types, and the transition of infected individuals between disease stages. In these models, population heterogeneity is treated by assigning individuals to different discrete types. Typically, methods which allow inference of parameters under multitype birth–death models integrate over the possible birth–death trajectories (i.e. the type-specific population size functions) to reduce the computational demands of the inference. As a result, it has not been possible to use these methods to directly infer the dynamics of trait-specific population sizes, infected host counts or other such demographic quantities. In this article, we present a method which infers these multitype trajectories with minimal additional computational cost beyond that of existing methods. We demonstrate the practicality of our approach by applying it to a previously published set of Middle East respiratory syndrome coronavirus genomes, inferring the numbers of human and camel cases through time, together with the number and timing of spillovers from the camel reservoir. This application highlights the multitype population trajectory’s ability to elucidate properties of the population which are not directly ancestral to its sampled members.