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Bayesian phylogenetics aims at estimating phylogenetic trees together with evolutionary and population dynamic parameters based on genetic sequences. It has been noted that the clock rate, one of the evolutionary parameters, decreases with an increase in the sampling period of sequences. In particular, clock rates of epidemic outbreaks are often estimated to be higher compared with the long-term clock rate. Purifying selection has been suggested as a biological factor that contributes to this phenomenon, since it purges slightly deleterious mutations from a population over time. However, other factors such as methodological biases may also play a role and make a biological interpretation of results difficult. In this paper, we identify methodological biases originating from the choice of tree prior, that is, the model specifying epidemiological dynamics. With a simulation study we demonstrate that a misspecification of the tree prior can upwardly bias the inferred clock rate and that the interplay of the different models involved in the inference can be complex and nonintuitive. We also show that the choice of tree prior can influence the inference of clock rate on real-world Ebola virus (EBOV) datasets. While commonly used tree priors result in very high clock-rate estimates for sequences from the initial phase of the epidemic in Sierra Leone, tree priors allowing for population structure lead to estimates agreeing with the long-term rate for EBOV.

Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.

Tomato chlorosis virus (ToCV), first reported in Florida, USA, in 1998, has since emerged in multiple regions worldwide, posing a significant threat to global tomato production. However, its origin, migration patterns, and evolutionary history remain poorly understood. In this study, we used Bayesian phylogeographic analysis of coat protein gene sequences from 155 ToCV isolates to reconstruct its phylogeographic history. Our results show that ToCV evolves at a rate of 6.24 × 10−4 subs/site/year (95% credibility interval: 4.35 × 10−4–8.28 × 10−4), with the most recent common ancestor dating back to 1882. The maximum clade credibility (MCC) tree revealed three major clades, with Clade 1—whose most recent common ancestor dates to approximately 1975—comprising over 90% of the isolates. Although the exact origin of ToCV remains uncertain, we identified five distinct migration pathways: one from Europe to the Americas, one from Europe to South Asia, one from the Middle East to East Asia, one from East Asia to mainland China, and one from mainland China to Europe. These findings underscore the complex global spread of ToCV and suggest that multiple geographic areas have contributed to its ongoing evolution and dissemination.

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.

Background Enterovirus 71(EV71)-associated hand, foot and mouth disease (HFMD) decreased dramatically in Beijing from 2009 to 2019. This study was to investigate the epidemiological characteristics, evolutionary dynamics, geographic diffusion pathway, and other features of EV71 in Beijing, China. Methods We conducted a retrospective study of EV71-associated HFMD and its causative agent in Beijing, China, from 2009 to 2019. Phylogenetic and phylogeographic methods based on the EV71 genome were used to determine the evolution features, origin, and spatiotemporal dynamics. Positive selection sites in the VP1 gene were identified and exhibited in the tertiary structure. Bayesian birth-death skyline model was used to estimate the effective reproductive number (Re). Results EV71-associated HFMD decreased greatly in Beijing. From 2009 to 2019, EV71 strains prevalent in Beijing shared high homology in each gene segment and evolved with a rate of 4.99*10 − 3 substitutions per site per year. The genetic diversity of EV71 first increased and peaked in 2012 and then decreased with fluctuations. The time to the most recent common ancestor (TMRCA) of EV71 in Beijing was estimated around 2003 when the EV71 strains were transmitted to Beijing from east China. Beijing played a crucial role in seeding EV71 to central China as well. Two residues (E145Q/G, A293S) under positive selection were detected from both the VP1 dataset and the P1 dataset. They were embedded within the loop of the VP1 capsid and were exposed externally. Mean Re estimate of EV71 in Beijing was about 1.007. Conclusion In recent years, EV71 was not the primary causative agent of HFMD in Beijing. The low Re estimate of EV71 in Beijing implied that strategies for preventing and controlling HFMD were performed effectively. Beijing and east China played a crucial role in disseminating EV71 to other regions in China.

Tobacco curly shoot virus (TbCSV), a begomovirus, causes significant economic losses in tobacco and tomato crops across East, Southeast, and South Asia. Despite its agricultural importance, the evolutionary dynamics and emergence process of TbCSV remain poorly understood. This study analyzed the phylodynamics of TbCSV by examining its nucleotide sequences of the coat protein (CP) gene collected between 2000 and 2022. Using various combinations of priors, Bayes factor comparisons identified heterochronous datasets (3 × 100 million chains) generated from a strict molecular clock and Bayesian skyline tree priors as the most robust. The mean substitution rate of the CP gene was estimated at 6.50 × 10−4 substitutions/site/year (95% credibility interval: 4.74 × 10−4–8.50 × 10−4). TbCSV was inferred to have diverged around 1920 CE (95% credibility interval: 1887–1952), with its most probable origin in South Asia. These findings provide valuable insights for the phylogeography and evolutionary dynamics of TbCSV, and contribute to a broader understanding of begomovirus epidemiology.

Cherry leaf roll virus (CLRV) is an important plant pathogen that causes severe and detrimental effects on cherry and other fruit plants. Despite recent progress in plant pathology, molecular biology, and population genetics of CLRV, the spatiotemporal spread of this virus remains poorly studied. In this study, we employed a Bayesian phylodynamics framework to investigate the spatial diffusion patterns of CLRV by analyzing the coat protein gene sequences of 81 viral isolates collected from five different countries. Consistent with the trade of cherry, our Bayesian phylodynamic analyses pointed to viral origins in New Zealand and identified multiple migration pathways between Germany and other countries, suggesting that Germany has played an important role in CLRV transmission. The results of our study will be useful in developing sustainable management strategies to control this pathogen.

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.

Subterranean rodents of the genus Ctenomys have experienced an explosive radiation and rapidly colonized the southern cone of South America. The torquatus group, one of the main groups of the genus, comprises several species and species complexes which inhabit the eastern part of the distribution of Ctenomys including southern Brazil, northern and central Uruguay and north-eastern Argentina. This group has undergone a high chromosomal diversification with diploid numbers varying from 41 to 70. The aim of this study was to investigate the origins of the torquatus group as well as its diversification patterns in relation to geography and cladogenesis. Based on mitochondrial cytochrome b nucleotide sequences we conducted a Bayesian multi-calibrated relaxed clock analysis to estimate the ages of the torquatus group and its main lineages. Using the estimated evolutionary rate we performed a continuous phylogeographic analysis, using a relaxed random walk model to reconstruct the geographic diffusion of the torquatus group in a temporal frame. The torquatus group originated during the early Pleistocene between 1.25 and 2.32 million years from the present in a region that includes the northwest of Uruguay and the southeast of the Brazilian state of Río Grande do Sul. Most lineages have dispersed early towards their present distribution areas going through subsequent range expansions in the last 800,000 – 700,000 years. Ctenomys torquatus went through a rapid range expansion for the last 200,000 years, becoming the most widespread species of the group. The colonization of the Corrientes and Entre Ríos Argentinean provinces supposes at least two crossing events across the Uruguay River between 1.0 and 0.5 million years before the present, in the context of a cold and dry paleoenvironment. The resulting temporal and geographic frame enables the comprehension of the incidence of both, the amplitude of distribution areas and divergence times into the patterns of chromosomal diversification found in the group.

Enzootic bovine leukosis, a neoplastic disease caused by the bovine leukemia virus (BLV), was the primary cancer affecting cattle in China before 1985. Although its prevalence decreased significantly between 1986 and 2000, enzootic bovine leukosis has been re-emerging since 2000. This re-emergence has been largely overlooked, possibly due to the latent nature of BLV infection or the perceived lack of sufficient evidence. This study investigated the molecular epidemiology of BLV infections in dairy cattle in Henan province, Central China. Blood samples from 668 dairy cattle across nine farms were tested using nested polymerase chain reaction assays targeting the partial envelope (env) gene (gp51 fragment). Twenty-three samples tested positive (animal-level prevalence of 3.4%; 95% confidence interval: 2.2, 5.1). The full-length env gene sequences from these positive samples were obtained and phylogenetically analyzed, along with previously reported sequences from the GenBank database. The sequences from positive samples were clustered into four genotypes (1, 4, 6, and 7). The geographical annotation of the maximum clade credibility trees suggested that the two genotype 1 strains in Henan might have originated from Japan, while the genotype 7 strain is likely to have originated from Moldova. Subsequent Bayesian stochastic search variable selection analysis further indicated a strong geographical association between the Henan strains and Japan, as well as Moldova. The estimated substitution rate for the env gene ranged from 4.39 × 10−4 to 2.38 × 10−3 substitutions per site per year. Additionally, codons 291, 326, 385, and 480 were identified as positively selected sites, potentially associated with membrane fusion, epitope peptide vaccine design, and transmembrane signal transduction. These findings contribute to the broader understanding of BLV epidemiology in Chinese dairy cattle and highlight the need for measures to mitigate further BLV transmission within and between cattle herds in China.