Molecular characterization of the meq oncogene of Marek's disease virus in vaccinated Brazilian poultry farms reveals selective pressure on prevalent strains

Marek's disease virus (MDV) has become an increasingly virulent pathogen in the poultry industry despite vaccination efforts to control it. Brazil has experienced a significant rise of Marek's disease (MD) outbreaks in recent years. Our study aimed to analyze the complete meq gene sequences to understand the molecular epidemiological basis of MD outbreaks in Brazilian vaccinated layer farms. We detected a high incidence rate of visceral MD (67.74%) and multiple circulating MDV strains. The most prevalent and geographically widespread genotype presented several clinical and molecular characteristics of a highly virulent strain and evolving under positive selective pressure. Phylogenetic and phylogeographic analysis revealed a closer relationship with strains from the USA and Japan. This study sheds light on the circulation of MDV strains capable of infecting vaccinated birds. We emphasize the urgency of adopting preventive measures to manage MDV outbreaks threatening the poultry farming industry.

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. Because 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 typically but not always 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 perception 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.

How fast are viruses spreading in the wild?

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 allowing to unveil the dispersal dynamics and evaluate the capacity to spread among hosts. Heterogeneous sampling intensity of genomic sequences can however impact the accuracy of dispersal insights gained through phylogeographic inference. In our study, we implement a simulation framework to evaluate the robustness of three dispersal metrics - a lineage dispersal velocity, a diffusion coefficient, and an isolation-by-distance signal metric - to the sampling effort. Our results reveal that both the diffusion coefficient and isolation-by-distance signal metrics appear to be robust to the number of samples considered for the phylogeographic reconstruction. We then use these two dispersal metrics to compare the dispersal pattern and capacity of various viruses spreading in animal populations. Our comparative analysis reveals a broad range of isolation-by-distance 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 lineage dispersal metrics to consider in future studies and illustrates their application to compare the spread of viruses in various settings.

Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Transmission dynamics of drug-resistant tuberculosis in Ningbo, China: an epidemiological and genomic analysis

Background

Tuberculosis (TB), particularly drug-resistant TB (DR-TB), remains a significant public health concern in Ningbo, China. Understanding its molecular epidemiology and spatial distribution is paramount for effective control.

Methods

From December 24, 2020, to March 12, 2023, we collected clinical Mycobacterium tuberculosis (MTB) strains in Ningbo, with whole-genome sequencing performed on 130 MTB strains. We analyzed DR-related gene mutations, conducted phylogenetic and phylodynamic analyses, identified recent transmission clusters, and assessed spatial distribution.

Results

Among 130 DR-TB cases, 41% were MDR-TB, 36% pre-XDR-TB, 19% RR-TB, and 3% HR-TB. The phylogenetic tree showed that 90% of strains were Lineage 2 (Beijing genotype), while remaining 10% were Lineage 4 (Euro-American genotype). The spatial analysis identified hotspots of DR-TB in Ningbo's northern region, particularly in traditional urban centers. 31 (24%) of the DR-TB cases were grouped into 7 recent transmission clusters with a large outbreak cluster containing 15 pre-XDR-TB patients. Epidemiological analyses suggested a higher risk of recent DR-TB transmission among young adult patients who frequently visited Internet cafes, game rooms, and factories.

Conclusion

Our study provides comprehensive insights into the epidemiology and genetics of DR-TB in Ningbo. The presence of genomic clusters highlights recent transmission events, indicating the need for targeted interventions. These findings are vital for informing TB control strategies in Ningbo and similar settings.

Phylodynamic evolution of HIV-1 A6 sub-subtype epidemics in Poland

The human immunodeficiency virus type 1 (HIV-1) A6 sub-subtype is highly prevalent in Eastern Europe. Over the past decade, the dissemination of the A6 lineage has been expanding in Poland. The recent Russian invasion of Ukraine may further escalate the spread of this sub-subtype. While evolutionary studies using viral sequences have been instrumental in identifying the HIV epidemic patterns, the origins, and dynamics of the A6 sub-subtype in Poland remain to be explored. We analyzed 1185 HIV-1 A6 pol sequences from Poland, along with 8318 publicly available sequences from other countries. For analyses, phylogenetic tree construction, population dynamics inference, Bayesian analysis, and discrete phylogeographic modeling were employed. Of the introduction events to Poland, 69.94% originated from Ukraine, followed by 29.17% from Russia. Most A6 sequences in Poland (53.16%) formed four large clades, with their introductions spanning 1993-2008. Central and Southern Polish regions significantly influenced migration events. Transmissions among men who have sex with men (MSM) emerged as the dominant risk group for virus circulation, representing 72.92% of migration events. Sequences from migrants were found primarily outside the large clades. Past migration from Ukraine has fueled the spread of the A6 sub-subtype and the current influx of war-displaced people maintains the growing national epidemic.

Investigating the rise of Omicron variant through genomic surveillance of SARS-CoV-2 infections in a highly vaccinated university population

Novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to emerge as the coronavirus disease 2019 (COVID-19) pandemic extends into its fourth year. Understanding SARS-CoV-2 circulation in university populations is vital for effective interventions in higher education settings and will inform public health policy during pandemics. In this study, we generated 793 whole-genome sequences collected over an entire academic year in a university population in Indiana, USA. We clearly captured the rapidity with which Delta variant was wholly replaced by Omicron variant across the West Lafayette campus over the length of two academic semesters in a community with high vaccination rates. This mirrored the emergence of Omicron throughout the state of Indiana and the USA. Further, phylogenetic analyses demonstrated that there was a more diverse set of potential geographic origins for Omicron viruses introduction into campus when compared to Delta. Lastly, statistics indicated that there was a more significant role for international and out-of-state migration in the establishment of Omicron variants at Purdue. This surveillance workflow, coupled with viral genomic sequencing and phylogeographic analyses, provided critical insights into SARS-CoV-2 transmission dynamics and variant arrival.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

The Global Evolutionary History of Orf Virus in Sheep and Goats Revealed by Whole Genomes Data

Orf virus (ORFV) belongs to the genus Parapoxvirus (Poxviridae family). It is the causative agent of contagious ecthyma (CE) that is an economically detrimental disease affecting small ruminants globally. Contagious ecthyma outbreaks are usually reported in intensive breeding of sheep and goats but they have also been reported in wildlife species. Notably, ORFV can infect humans, leading to a zoonotic disease. This study aims to elucidate the global evolutionary history of ORFV genomes in sheep and goats, including the first genomes from Central America in the analyses. In comparison to the last study on ORFV whole genomes, the database now includes 11 more sheep and goat genomes, representing an increase of 42%. The analysis of such a broader database made it possible to obtain a fine molecular dating of the coalescent time for ORFV S and G genomes, further highlighting the genetic structuring between sheep and goat genomes and corroborating their emergence in the latter half of 20th century.

Comparative evolution of influenza A virus H1 and H3 head and stalk domains across host species

IMPORTANCE

For decades, researchers have studied the rapid evolution of influenza A viruses for vaccine design and as a useful model system for the study of host/parasite evolution. By performing an exhaustive analysis of hemagglutinin protein (HA) sequences from 49 lineages independently evolving in birds, swine, canines, equines, and humans over the last century, our work uncovers surprising features of HA evolution. In particular, the canine H3 stalk, unlike human H3 and H1 stalk domains, is not evolving slowly, suggesting that evolution in the stalk domain is not universally constrained across all host species. Therefore, a broader multi-host perspective on HA evolution may be useful during the evaluation and design of stalk-targeted vaccine candidates.

Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data

We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.

Deep learning and likelihood approaches for viral phylogeography converge on the same answers whether the inference model is right or wrong

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. How- ever, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free infer- ence from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression which we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Fi- nally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the loca- tion of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on aver- age over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees

Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer such epidemiological parameters as the average number of secondary infections Re and the infectious time from a phylogenetic tree (a genealogy of pathogen sequences). The representatives of this model family focus on various aspects of pathogen epidemics. For instance, the birth-death exposed-infectious (BDEI) model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters. With constantly growing sequencing data, MTBD models should be extremely useful for unravelling information on pathogen epidemics. However, existing implementations of these models in a phylodynamic framework have not yet caught up with the sequencing speed. Computing time and numerical instability issues limit their applicability to medium data sets (≤ 500 samples), while the accuracy of estimations should increase with more data. We propose a new highly parallelizable formulation of ordinary differential equations for MTBD models. We also extend them to forests to represent situations when a (sub-)epidemic started from several cases (e.g., multiple introductions to a country). We implemented it for the BDEI model in a maximum likelihood framework using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in two minutes on a phylogenetic tree of 10,000 samples. Comparison to the existing implementations on simulated data shows that it is not only much faster but also more accurate. An application of our tool to the 2014 Ebola epidemic in Sierra-Leone is also convincing, with very fast calculation and precise estimates. As MTBD models are closely related to Cladogenetic State Speciation and Extinction (ClaSSE)-like models, our findings could also be easily transferred to the macroevolution domain.

Evolutionary Analysis of a Parrot Bornavirus 2 Detected in a Sulphur-Crested Cockatoo (Cacatua galerita) Suggests a South American Ancestor

Parrot bornavirus (PaBV) is an RNA virus that causes Proventricular Dilatation Disease (PDD), neurological disorders, and death in Psittaciformes. Its diversity in South America is poorly known. We examined a Cacatua galerita presenting neuropathies, PDD, and oculopathies as the main signs. We detected PaBV through reverse transcription polymerase chain reaction (RT-PCR) and partial sequencing of the nucleoprotein (N) and matrix (M) genes. Maximum likelihood and Bayesian phylogenetic inferences classified it as PaBV-2. The nucleotide identity of the sequenced strain ranged from 88.3% to 90.3% against genotype PaBV-2 and from 80.2% to 84.4% against other genotypes. Selective pressure analysis detected signs of episodic diversifying selection in both the N and M genes. No recombination events were detected. Phylodynamic analysis estimated the time to the most recent common ancestor (TMRCA) as the year 1758 for genotype PaBV-2 and the year 1049 for the Orthobornavirus alphapsittaciforme species. Substitution rates were estimated at 2.73 × 10-4 and 4.08 × 10-4 substitutions per year per site for N and M, respectively. The analysis of population dynamics showed a progressive decline in the effective population size during the last century. Timescale phylogeographic analysis revealed a potential South American ancestor as the origin of genotypes 1, 2, and 8. These results contribute to our knowledge of the evolutionary origin, diversity, and dynamics of PaBVs in South America and the world. Additionally, it highlights the importance of further studies in captive Psittaciformes and the potential impact on endangered wild birds.

Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

IMPORTANCE

Our study provides insights into the evolution of the coronavirus disease 2019 (COVID-19) pandemic in Malta, a highly connected and understudied country. We combined epidemiological and phylodynamic analyses to analyze trends in the number of new cases, deaths, tests, positivity rates, and evolutionary and dispersal patterns from August 2020 to January 2022. Our reconstructions inferred 173 independent severe acute respiratory syndrome coronavirus 2 introductions into Malta from various global regions. Our study demonstrates that characterizing epidemiological trends coupled with phylodynamic modeling can inform the implementation of public health interventions to help control COVID-19 transmission in the community.

Reconstructing the phylodynamic history and geographic spread of the CRF01_AE-predominant HIV-1 epidemic in the Philippines from PR/RT sequences sampled from 2008 to 2018

The Philippines has had a rapidly growing human immunodeficiency virus (HIV) epidemic with a shift in the prevalent subtype from B to CRF01_AE. However, the phylodynamic history of CRF01_AE in the Philippines has yet to be reconstructed. We conducted a descriptive retrospective study reconstructing the history of HIV-1 CRF01_AE transmissions in the Philippines through molecular epidemiology. Partial polymerase sequences (n = 1144) collected between 2008 and 2018 from three island groups were collated from the Research Institute for Tropical Medicine drug resistance genotyping database. Estimation of the time to the most recent common ancestor (tMRCA), effective reproductive number (Re), effective viral population size (Ne), relative migration rates, and geographic spread of CRF01_AE was performed with BEAST. Re and Ne were compared between CRF01_AE and B. Most CRF01_AE sequences formed a single clade with a tMRCA of June 1996 [95 per cent highest posterior density (HPD): December 1991, October 1999]. An increasing CRF01_AE Ne was observed from the tMRCA to 2013. The CRF01_AE Re reached peaks of 2.46 [95 per cent HPD: 1.76, 3.27] in 2007 and 2.52 [95 per cent HPD: 1.83, 3.34] in 2015. A decrease of CRF01_AE Re occurred in the intervening years of 2007 to 2011, reaching as low as 1.43 [95 per cent HPD: 1.06, 1.90] in 2011, followed by a rebound. The CRF01_AE epidemic most likely started in Luzon and then spread to the other island groups of the country. Both CRF01_AE and Subtype B exhibited similar patterns of Re fluctuation over time. These results characterize the subtype-specific phylodynamic history of the largest CRF01_AE cluster in the Philippines, which contextualizes and may inform past, present, and future public health measures toward controlling the HIV epidemic in the Philippines.

Predicting Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data

Robust sampling methods are foundational to many inference problems in the phylodynamic field, yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications, is not well understood. To investigate and quantify how this non-uniform sampling method influences recovered phylogenetic tree structure, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examine a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. SEEPS is available at github.com/MolEvolEpid/SEEPS.

Genomic epidemiology sheds light on the emergence and spread of Mycobacterium bovis Eu2 Clonal Complex in Portugal

ABSTRACTAnimal tuberculosis (TB) remains a serious concern for animal and human health. Mycobacterium bovis circulates in multi-host systems, dominated by the European 2 clonal complex (Eu2) in Iberia. In this work, we use genomic epidemiology to infer the emergence, spread, and spatiotemporal patterns of Eu2 in the official epidemiological risk area of animal TB in Portugal. Phylogenetic analysis of 144 M. bovis whole-genome sequences from cattle, wild boar, and red deer, representing the 2002-2021 period, distinguished three Eu2 clades that evolved independently. The major Eu2 clade underwent phylodynamic inferences to estimate the time and location of outbreaks, host transitions, and spatial diffusion as well. The origin of this Eu2 clade was attributed to the red deer population in the Castelo Branco district, near the border with Spain. Most host transitions were intraspecific (80%), while interspecific transmissions between wildlife species (wild boar-red deer), and between wild boar and cattle, were highly supported. Phylogeographic reconstruction evidenced that most transitions (82%) occur within municipalities, highlighting local transmission corridors.Our study indicates that M. bovis continues to spread at the cattle-wildlife interface within the animal TB hotspot area, possibly driven by the foraging behaviour of wild boar near agricultural lands. Red deer seems to be an important driver of TB within wildlife hosts, while the wild boar links the multi-host wildlife community and livestock. This work highlights the value of combining genomic epidemiology with phylodynamic inference to resolve host jumps and spatial patterns of M. bovis, providing real-time clues about points of intervention.

Identification of a HIV-1 circulating BF1 recombinant form (CRF75_BF1) of Brazilian origin that also circulates in Southwestern Europe

Introduction

The high recombinogenic potential of HIV-1 has resulted in the generation of countless unique recombinant forms (URFs) and around 120 reported circulating recombinant forms (CRFs). Here we identify through analyses of near full-length genomes (NFLG) a new HIV-1 CRF derived from subtypes B and F1.

Methods

HIV-1 protease-reverse transcriptase (Pr-RT) sequences were obtained by RT-PCR amplification from plasma RNA. Near full-length genome sequences were obtained after amplification by RT-PCR in 5 overlapping fragments. Phylogenetic sequence analyses were performed via maximum likelihood. Mosaic structures were analyzed by bootscanning and phylogenetic analyses of genome segments. Temporal and geographical estimations of clade emergence were performed with a Bayesian coalescent method.

Results

Through phylogenetic analyses of HIV-1 Pr-RT sequences obtained by us from samples collected in Spain and downloaded from databases, we identified a BF1 recombinant cluster segregating from previously reported CRFs comprising 52 viruses, most from Brazil (n = 26), Spain (n = 11), and Italy (n = 9). The analyses of NFLG genomes of 4 viruses of the cluster, 2 from Spain and 2 from Italy, allowed to identify a new CRF, designated CRF75_BF1, which exhibits a complex mosaic structure with 20 breakpoints. All 4 patients harboring CRF75_BF1 viruses studied by us had CD4+ T-cell lymphocyte counts below 220/mm3 less than one year after diagnosis, a proportion significantly higher (p = 0.0074) than the 29% found in other patients studied in Spain by us during the same period. The origin of the clade comprising CRF75_BF1 and related viruses was estimated around 1984 in Brazil, with subsequent introduction of CRF75_BF1 in Italy around 1992, and migration from Italy to Spain around 1999.

Conclusion

A new HIV-1 CRF, designated CRF75_BF1, has been identified. CRF75_BF1 is the 6th CRF of South American origin initially identified in Western Europe, reflecting the increasing relationship of South American and European HIV-1 epidemics. The finding of low CD4+ T-cell lymphocyte counts early after diagnosis in patients harboring CRF75_BF1 viruses warrants further investigation on the virulence of this variant.

Accumulated metagenomic studies reveal recent migration, whole genome evolution, and undiscovered diversity of orthomyxoviruses

IMPORTANCE

The number of known virus species has increased dramatically through metagenomic studies, which search genetic material sampled from a host for non-host genes. Here, we focus on an important viral family that includes influenza viruses, the Orthomyxoviridae, with over 100 recently discovered viruses infecting hosts from humans to fish. We find that one virus called Wǔhàn mosquito virus 6, discovered in mosquitoes in China, has spread across the globe very recently. Surface proteins used to enter cells show signs of rapid evolution in Wǔhàn mosquito virus 6 and its relatives which suggests an ability to infect vertebrate animals. We compute the rate at which new orthomyxovirus species discovered add evolutionary history to the tree of life, predict that many viruses remain to be discovered, and discuss what appropriately designed future studies can teach us about how diseases cross between continents and species.

Molecular Epidemiology of Rotavirus A in Calves: Evolutionary Analysis of a Bovine G8P[11] Strain and Spatio-Temporal Dynamics of G6 Lineages in the Americas

Rotavirus A (RVA) causes diarrhea in calves and frequently possesses the G6 and P[5]/P[11] genotypes, whereas G8 is less common. We aimed to compare RVA infections and G/P genotypes in beef and dairy calves from major livestock regions of Argentina, elucidate the evolutionary origin of a G8 strain and analyze the G8 lineages, infer the phylogenetic relationship of RVA field strains, and investigate the evolution and spatio-temporal dynamics of the main G6 lineages in American countries. Fecal samples (n = 422) from diarrheic (beef, 104; dairy, 137) and non-diarrheic (beef, 78; dairy, 103) calves were analyzed by ELISA and semi-nested multiplex RT-PCR. Sequencing, phylogenetic, phylodynamic, and phylogeographic analyses were performed. RVA infections were more frequent in beef (22.0%) than in dairy (14.2%) calves. Prevalent genotypes and G6 lineages were G6(IV)P[5] in beef (90.9%) and G6(III)P[11] (41.2%) or mixed genotypes (23.5%) in dairy calves. The only G8 strain was phylogenetically related to bovine and artiodactyl bovine-like strains. Re-analyses inside the G8 genotype identified G8(I) to G8(VIII) lineages. Of all G6 strains characterized, the G6(IV)P[5](I) strains from "Cuenca del Salado" (Argentina) and Uruguay clustered together. According to farm location, a clustering pattern for G6(IV)P[5] strains of beef farms was observed. Both G6 lineage strains together revealed an evolutionary rate of 1.24 × 10-3 substitutions/site/year, and the time to the most recent common ancestor was dated in 1853. The most probable ancestral locations were Argentina in 1981 for G6(III) strains and the USA in 1940 for G6(IV) strains. The highest migration rates for both G6 lineages together were from Argentina to Brazil and Uruguay. Altogether, the epidemiology, genetic diversity, and phylogeny of RVA in calves can differ according to the production system and farm location. We provide novel knowledge about the evolutionary origin of a bovine G8P[11] strain. Finally, bovine G6 strains from American countries would have originated in the USA nearly a century before its first description.

Antigenic and molecular characterization of isolates of the Brazilian genotype BR-I (GI-11) of infectious bronchitis virus supports its recognition as BR-I serotype

The antigenic and molecular characteristics of BR-I infectious bronchitis viruses (IBVs) isolated from Brazil are reported. IBVs isolated from commercial flocks with different clinical manifestations between 2003 and 2019 were submitted to antigenic and molecular characterization. The complete S1 glycoprotein gene of 11 field isolates was amplified and sequenced. The virus neutralization (VN) test showed 94.75% neutralization with a BR-I isolate and 30% or less against other worldwide reference strains. The nucleotide and amino acid sequence analyses revealed 84.3-100% and 83.5-100% identity among them, respectively. The identity values ranged from 57.1 to 82.6% for nucleotides and from 46.6-84.4% for amino acids compared with those of other genotypes. By phylogenetic tree analysis, the Brazilian isolates were branched into the BR-I genotype (lineage GI-11), which was differentiated from foreign reference strains. Selective pressure analyses of BR-I IBVs revealed evolution under purifying selection (negative pressure) for the complete S1 gene but four specific sites (87, 121, 279, and 542) under diversifying selection (positive pressure). Profiles of cleavage sites and potential N-glycosylation sites differed from those of other genotypes. The low molecular relationship among the Brazilian viruses and foreign serotypes was concordant with the VN test results. The low antigenic relatedness (ranging from 5.3-30% between Brazilian genotype BR-I and reference IBV serotypes of North America, Europe, and Asia) indicates that the BR-I genotype is a different serotype, referred to for the first time and hereafter as serotype BR-I. RESEARCH HIGHLIGHTSStrains of the BR-I genotype presented robust antigenic and molecular similarity.BR-I strains evolved under purifying selection mode (negative pressure).The BR-I genotype originated in Brazil and dispersed to other countries.BR-I genotype viruses can be referred to as the BR-I serotype.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Genetic and structural analyses reveal the low potential of the SARS-CoV-2 EG.5 variant

The severe acute respiratory syndrome coronavirus 2 EG.5 lineage is the latest variant under monitoring, and it is generating significant concern due to its recent upward trend in prevalence. Our aim was to gain insights into this emerging lineage and offer insights into its actual level of threat. Both genetic and structural data indicate that this novel variant presently lacks substantial evidence of having a high capacity for widespread transmission. Their viral population sizes expanded following a very mild curve and peaked several months after the earliest detected sample. Currently, neither the viral population size of EG.5 nor that of its first descendant is increasing. The genetic variability appear to be flattened, as evidenced by its relatively modest evolutionary rate (9.05 × 10-4 subs/site/year). As has been observed with numerous prior variants, attributes that might theoretically provide advantages seem to stem from genetic drift, enabling the virus to continually adjust to its host, albeit without a clear association with enhanced dangerousness. These findings further underscore the necessity for ongoing genome-based monitoring, ensuring preparedness and a well-documented understanding of the unfolding situation.

Integrative Genome-Based Survey of the SARS-CoV-2 Omicron XBB.1.16 Variant

The XBB.1.16 SARS-CoV-2 variant, also known as Arcturus, is a recent descendant lineage of the recombinant XBB (nicknamed Gryphon). Compared to its direct progenitor, XBB.1, XBB.1.16 carries additional spike mutations in key antigenic sites, potentially conferring an ability to evade the immune response compared to other circulating lineages. In this context, we conducted a comprehensive genome-based survey to gain a detailed understanding of the evolution and potential dangers of the XBB.1.16 variant, which became dominant in late June. Genetic data indicates that the XBB.1.16 variant exhibits an evolutionary background with limited diversification, unlike dangerous lineages known for rapid changes. The evolutionary rate of XBB.1.16, which amounts to 3.95 × 10-4 subs/site/year, is slightly slower than that of its direct progenitors, XBB and XBB.1.5, which have been circulating for several months. A Bayesian Skyline Plot reconstruction suggests that the peak of genetic variability was reached in early May 2023, and currently, it is in a plateau phase with a viral population size similar to the levels observed in early March. Structural analyses indicate that, overall, the XBB.1.16 variant does not possess structural characteristics markedly different from those of the parent lineages, and the theoretical affinity for ACE2 does not seem to change among the compared variants. In conclusion, the genetic and structural analyses of SARS-CoV-2 XBB.1.16 do not provide evidence of its exceptional danger or high expansion capability. Detected differences with previous lineages are probably due to genetic drift, which allows the virus constant adaptability to the host, but they are not necessarily connected to a greater danger. Nevertheless, continuous genome-based monitoring is essential for a better understanding of its descendants and other lineages.

Dengue Fever Surveillance in Mato Grosso do Sul: Insights from Genomic Analysis and Implications for Public Health Strategies

Since its discovery in early 1916, dengue fever, a common vector-borne illness in Brazil, has resulted in extensive urban outbreaks and poses a serious threat to the public's health. Understanding the dynamics of Dengue Virus (DENV) serotypes circulating in different regions of Brazil is essential for implementing effective disease control and prevention measures. In response to this urgent need, we conducted an on-site training program in genomic surveillance in collaboration with the Central Laboratory of Health and the Secretary of Health of the Mato Grosso do Sul state. This initiative resulted in the generation of 177 DENV genome sequences collected between May 2021 and May 2022, a period during which over 11,391 dengue fever cases were reported in the state. Through this approach, we were able to identify the co-circulation of two different dengue serotypes (DENV1 and DENV2) as well as the existence of diverse viral lineages within each genotype, suggesting that multiple introduction events of different viral strains occurred in the region. By integrating epidemiological data, our findings unveiled temporal fluctuations in the relative abundance of different serotypes throughout various epidemic seasons, highlighting the complex and changing dynamics of DENV transmission throughout time. These findings demonstrate the value of ongoing surveillance activities in tracking viral transmission patterns, monitoring viral evolution, and informing public health actions.

Phylogenetic and Phylodynamic Analysis of Delta Strains Circulating in Italy

The hepatitis delta virus (HDV) exhibits high genetic and evolutionary variability and is classified into eight genotypes (HDV-1 to -8). HDV-1 is the most widespread genotype worldwide and includes several subtypes. It predominates mainly in Europe, the Middle East, North America, and Northern Africa, and is associated with both severe and mild forms of liver disease. In this study, we performed phylogenetic and phylodynamic analyses of HDV strains circulating in Regione Lazio, Italy, to understand when these strains were introduced into the Lazio region and to define their genetic variability in Italy. Fifty HDV RNA positive patient samples were amplified using a nested RT-PCR approach targeting the HDV R0 region and sequenced. A phylogenetic tree of patient-derived sequences and reference sequences representing HDV-1 to -8 was constructed using the GTRGAMMA model in RAxML v8. The results indicated that HDV-1 was the predominant genotype with HDV-1d being the most frequently inferred subtype. HDV-1 sequences clustering with subtypes 1b and 1e were also identified. A phylodynamic analysis of HDV-1 sequences employing a Bayesian birth-death model inferred a clock rate of 3.04 × 10-4 substitutions per site per million years, with a 95% Highest Posterior Density (HPD) interval of 3.45 × 10-5 to 5.72 × 10-4. A Bayesian birth-death analysis with tree calibration based on a sample dating approach indicated multiple original sources of infection (from the late 1950s to late 1980s). Overall, these results suggest that HDV sequences from the native Italian and non-Italian patients analyzed in this study represent multiple lineages introduced across a wide period. A common ancestral origin should be excluded.

Update on the Phylodynamic and Genetic Variability of Marburg Virus

The COVID-19 pandemic has not only strained healthcare systems in Africa but has also intensified the impact of emerging and re-emerging diseases. Specifically in Equatorial Guinea, mirroring the situation in other African countries, unique zoonotic outbreaks have occurred during this challenging period. One notable resurgence is Marburg virus disease (MVD), which has further burdened the already fragile healthcare system. The re-emergence of the Marburg virus amid the COVID-19 pandemic is believed to stem from a probable zoonotic spill-over, although the precise transmission routes remain uncertain. Given the gravity of the situation, addressing the existing challenges is paramount. Though the genome sequences from the current outbreak were not available for this study, we analyzed all the available whole genome sequences of this re-emerging pathogen to advocate for a shift towards active surveillance. This is essential to ensure the successful containment of any potential Marburg virus outbreak in Equatorial Guinea and the wider African context. This study, which presents an update on the phylodynamics and the genetic variability of MARV, further confirmed the existence of at least two distinct patterns of viral spread. One pattern demonstrates a slower but continuous and recurring virus circulation, while the other exhibits a faster yet limited and episodic spread. These results highlight the critical need to strengthen genomic surveillance in the region to effectively curb the pathogen's dissemination. Moreover, the study emphasizes the importance of prompt alert management, comprehensive case investigation and analysis, contact tracing, and active case searching. These steps are vital to support the healthcare system's response to this emerging health crisis. By implementing these strategies, we can better arm ourselves against the challenges posed by the resurgence of the Marburg virus and other infectious diseases.

Genomic Detection of the Emerging, Highly Pathogenic HIV-1 Subtype D in Bahia, Northeast Brazil

(1) Background: The HIV subtype D is generally associated with a faster decline in CD4+ T cell counts, a higher viral load, and a faster progression to AIDS. However, it is still poorly characterized in Brazil. In this study, we used genomics and epidemiological data to investigate the transmission dynamics of HIV subtype D in the state of Bahia, Northeast Brazil. (2) Methods: To achieve this goal, we obtained four novel HIV-1 subtype D partial pol genome sequences using the Sanger method. To understand the emergence of this novel subtype in the state of Bahia, we used phylodynamic analysis on a dataset comprising 3704 pol genome sequences downloaded from the Los Alamos database. (3) Results: Our analysis revealed three branching patterns, indicating multiple introductions of the HIV-1 subtype D in Brazil from the late 1980s to the late 2000s and a single introduction event in the state of Bahia. Our data further suggest that these introductions most likely originated from European, Eastern African, Western African, and Southern African countries. (4) Conclusion: Understanding the distribution of HIV-1 viral strains and their temporal dynamics is crucial for monitoring the real-time evolution of circulating subtypes and recombinant forms, as well as for designing novel diagnostic and vaccination strategies. We advocate for a shift to active surveillance, to ensure adequate preparedness for future epidemics mediated by emerging viral strains.

Comparing the evolutionary dynamics of predominant SARS-CoV-2 virus lineages co-circulating in Mexico

Over 200 different SARS-CoV-2 lineages have been observed in Mexico by November 2021. To investigate lineage replacement dynamics, we applied a phylodynamic approach and explored the evolutionary trajectories of five dominant lineages that circulated during the first year of local transmission. For most lineages, peaks in sampling frequencies coincided with different epidemiological waves of infection in Mexico. Lineages B.1.1.222 and B.1.1.519 exhibited similar dynamics, constituting clades that likely originated in Mexico and persisted for >12 months. Lineages B.1.1.7, P.1 and B.1.617.2 also displayed similar dynamics, characterized by multiple introduction events leading to a few successful extended local transmission chains that persisted for several months. For the largest B.1.617.2 clades, we further explored viral lineage movements across Mexico. Many clades were located within the south region of the country, suggesting that this area played a key role in the spread of SARS-CoV-2 in Mexico.

The tumour is in the detail: Local phylogenetic, population and epidemiological dynamics of a transmissible cancer in Tasmanian devils

Infectious diseases are a major threat for biodiversity conservation and can exert strong influence on wildlife population dynamics. Understanding the mechanisms driving infection rates and epidemic outcomes requires empirical data on the evolutionary trajectory of pathogens and host selective processes. Phylodynamics is a robust framework to understand the interaction of pathogen evolutionary processes with epidemiological dynamics, providing a powerful tool to evaluate disease control strategies. Tasmanian devils have been threatened by a fatal transmissible cancer, devil facial tumour disease (DFTD), for more than two decades. Here we employ a phylodynamic approach using tumour mitochondrial genomes to assess the role of tumour genetic diversity in epidemiological and population dynamics in a devil population subject to 12 years of intensive monitoring, since the beginning of the epidemic outbreak. DFTD molecular clock estimates of disease introduction mirrored observed estimates in the field, and DFTD genetic diversity was positively correlated with estimates of devil population size. However, prevalence and force of infection were the lowest when devil population size and tumour genetic diversity was the highest. This could be due to either differential virulence or transmissibility in tumour lineages or the development of host defence strategies against infection. Our results support the view that evolutionary processes and epidemiological trade-offs can drive host-pathogen coexistence, even when disease-induced mortality is extremely high. We highlight the importance of integrating pathogen and population evolutionary interactions to better understand long-term epidemic dynamics and evaluating disease control strategies.

Phylodynamic analysis revealed that human mobility and vaccination were correlated to the local spread of SARS-CoV-2 in Republic of Korea

Following the global emergence of the SARS-CoV-2 Alpha variant of concern (VOC) in 2020, the Delta variant triggered another wave in 2021. The AY.69 lineage, a Delta VOC, was particularly prevalent in Republic of Korea (South Korea) from May 2021 to January 2022, despite the synchronized implementation of vaccination programs and non-pharmaceutical interventions (NPIs) such as social distancing. In this study, we used phylogeographic analysis combined with a generalized linear model (GLM) to examine the impact of human movement and vaccination on viral transmission. Our findings indicated that transmission primarily originated in South Korea's metropolitan areas, and a positive correlation was observed between total human mobility (tracked by GPS on mobile phones and estimated through credit card consumption) and viral spread. The phylodynamic analysis further revealed that non-vaccinated individuals were the primary transmitters of the virus during the study period, even though vaccination programs had commenced three months prior to the AY.69 outbreak. Our study emphasizes the need to focus on controlling SARS-CoV-2 transmission in metropolitan regions and among unvaccinated populations. Furthermore, the positive correlation between mobility data and viral dissemination could contribute to the development of more accurate predictive models for local spread of pandemics.

Dynamics of eastern equine encephalitis virus during the 2019 outbreak in the Northeast United States

Eastern equine encephalitis virus (EEEV) causes a rare but severe disease in horses and humans and is maintained in an enzootic transmission cycle between songbirds and Culiseta melanura mosquitoes. In 2019, the largest EEEV outbreak in the United States for more than 50 years occurred, centered in the Northeast. To explore the dynamics of the outbreak, we sequenced 80 isolates of EEEV and combined them with existing genomic data. We found that, similar to previous years, cases were driven by multiple independent but short-lived virus introductions into the Northeast from Florida. Once in the Northeast, we found that Massachusetts was important for regional spread. We found no evidence of any changes in viral, human, or bird factors which would explain the increase in cases in 2019, although the ecology of EEEV is complex and further data is required to explore these in more detail. By using detailed mosquito surveillance data collected by Massachusetts and Connecticut, however, we found that the abundance of Cs. melanura was exceptionally high in 2019, as was the EEEV infection rate. We employed these mosquito data to build a negative binomial regression model and applied it to estimate early season risks of human or horse cases. We found that the month of first detection of EEEV in mosquito surveillance data and vector index (abundance multiplied by infection rate) were predictive of cases later in the season. We therefore highlight the importance of mosquito surveillance programs as an integral part of public health and disease control.

Respiratory syncytial virus surge in 2022 caused by lineages already present before the COVID-19 pandemic

In 2022, Austria experienced a severe respiratory syncytial virus (RSV) epidemic with an earlier-than-usual start (Weeks 35/2021-45/2022) and increased numbers of pediatric patients in emergency departments. This surge came 2 years after a season with no cases detected as a result of coronavirus disease 2019 nonpharmaceutical interventions. We analyzed epidemiologic patterns and the phylodynamics of RSV based on approximately 30 800 respiratory specimens collected year-round over 10 years from ambulatory and hospitalized patients from 248 locations in Austria. Genomic surveillance and phylogenetic analysis of 186 RSV-A and 187 RSV-B partial glycoprotein sequences collected from 2018 to 2022 revealed that the 2022/2023 surge was driven by RSV-B in contrast to the surge in the 2021/2022 season that was driven by RSV-A. Whole-genome sequencing and phylodynamic analysis indicated that the RSV-B strain GB5.0.6a was the predominant genotype in the 2022/2023 season and emerged in late 2019. The results provide insight into RSV evolution and epidemiology that will be applicable to future monitoring efforts with the advent of novel vaccines and therapeutics.

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Recombination smooths the time signal disrupted by latency in within-host HIV phylogenies

Within-host Human immunodeficiency virus (HIV) evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is reactivation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in a bifurcating tree. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an ancestral recombination graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time signals or population bottlenecks. Furthermore, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an approximate Bayesian computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.

Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Phylogenetic Reconstructions Reveal the Circulation of a Novel Dengue Virus-1V Clade and the Persistence of a Dengue Virus-2 III Genotype in Northeast Brazil

Dengue fever is among the most significant public health concerns in Brazil. To date, the highest number of Dengue notifications in the Americas has been reported in Brazil, with cases accounting for a total number of 3,418,796 reported cases as of mid-December 2022. Furthermore, the northeastern region of Brazil registered the second-highest incidence of Dengue fever in 2022. Due to the alarming epidemiological scenario, in this study, we used a combination of portable whole-genome sequencing, phylodynamic, and epidemiological analyses to reveal a novel DENV-1 genotype V clade and the persistence of DENV-2 genotype III in the region. We further report the presence of non-synonymous mutations associated with non-structural domains, especially the NS2A (non-structural protein 2A), as well as describe synonymous mutations in envelope and membrane proteins, distributed differently between clades. However, the absence of clinical data at the time of collection and notification, as well as the impossibility of monitoring patients in order to observe worsening or death, restricts our possibility of correlating mutational findings with possible clinical prognoses. Together, these results reinforce the crucial role of genomic surveillance to follow the evolution of circulating DENV strains and understand their spread across the region through inter-regional importation events, likely mediated by human mobility, and also the possible impacts on public health and outbreak management.

Tracking the emergence of antigenic variants in influenza A virus epidemics in Brazil

Influenza A virus (IAV) circulation patterns differ in North America and South America, with influenza seasons often characterized by different subtypes and strains. However, South America is relatively undersampled considering the size of its population. To address this gap, we sequenced the complete genomes of 220 IAVs collected between 2009 and 2016 from hospitalized patients in southern Brazil. New genetic drift variants were introduced into southern Brazil each season from a global gene pool, including four H3N2 clades (3c, 3c2, 3c3, and 3c2a) and five H1N1pdm clades (clades 6, 7, 6b, 6c, and 6b1). In 2016, H1N1pdm viruses belonging to a new 6b1 clade caused a severe influenza epidemic in southern Brazil that arrived early and spread rapidly, peaking mid-autumn. Inhibition assays showed that the A/California/07/2009(H1N1) vaccine strain did not protect well against 6b1 viruses. Phylogenetically, most 6b1 sequences that circulated in southern Brazil belong to a single transmission cluster that rapidly diffused across susceptible populations, leading to the highest levels of influenza hospitalization and mortality seen since the 2009 pandemic. Continuous genomic surveillance is needed to monitor rapidly evolving IAVs for vaccine strain selection and understand their epidemiological impact in understudied regions.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

Model misspecification misleads inference of the spatial dynamics of disease outbreaks

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.

Phylodynamics and migration data help describe HIV transmission dynamics in internally displaced people who inject drugs in Ukraine

Internally displaced persons are often excluded from HIV molecular epidemiology surveillance due to structural, behavioral, and social barriers in access to treatment. We test a field-based molecular epidemiology framework to study HIV transmission dynamics in a hard-to-reach and highly stigmatized group, internally displaced people who inject drugs (IDPWIDs). We inform the framework by Nanopore generated HIV pol sequences and IDPWID migration history. In June-September 2020, we recruited 164 IDPWID in Odesa, Ukraine, and obtained 34 HIV sequences from HIV-infected participants. We aligned them to publicly available sequences (N = 359) from Odesa and IDPWID regions of origin and identified 7 phylogenetic clusters with at least 1 IDPWID. Using times to the most recent common ancestors of the identified clusters and times of IDPWID relocation to Odesa, we infer potential post-displacement transmission window when infections likely to happen to be between 10 and 21 months, not exceeding 4 years. Phylogeographic analysis of the sequence data shows that local people in Odesa disproportionally transmit HIV to the IDPWID community. Rapid transmissions post-displacement in the IDPWID community might be associated with slow progression along the HIV continuum of care: only 63% of IDPWID were aware of their status, 40% of those were in antiviral treatment, and 43% of those were virally suppressed. Such HIV molecular epidemiology investigations are feasible in transient and hard-to-reach communities and can help indicate best times for HIV preventive interventions. Our findings highlight the need to rapidly integrate Ukrainian IDPWID into prevention and treatment services following the dramatic escalation of the war in 2022.

Circulation of HIV-1 A6 variant in the East border of European Union - dynamics of the virus transmissions between Poland and Ukraine

BACKGROUND

The Human Immunodeficiency Virus-1 (HIV-1) A6 variant is dominating in high-prevalence Eastern European countries with increasing prevalence over the remaining regions of Europe. The recent war in Ukraine may contribute to further introductions of this A6 lineage. Our aim was to model the transmission dynamics of the HIV-1 A6 variant between Poland and Ukraine.

METHODS

HIV-1 A6 partial pol sequences originating from Poland (n=1,185) and Ukraine (n=653) were combined with publicly available sequences (n=7,675) from 37 other countries. We used maximum likelihood-based tree estimation followed by a Bayesian inference strategy to characterize the putative transmission clades. Asymmetric discrete phylogeographic analysis was used to identify the best-supported virus migration events across administrative regions of Poland and Ukraine.

RESULTS

We identified 206 clades (n=1,362 sequences) circulating in Poland or Ukraine (63 binational, 79 exclusively Polish and containing only 64 Ukrainian sequences). Cross-border migrations were almost exclusively unidirectional (from Ukraine to Poland, 99.4%), mainly from East and South Ukraine (Donetsk - 49.7% and Odessa - 17.6% regions) to Central (Masovian - 67.3%, Lodz - 18.2%), and Western Pomeranian (10.1%) districts of Poland. The primary sources of viral dispersal were the Eastern regions of Ukraine, long-time affected by armed conflict and large population centers in Poland.

CONCLUSIONS

The Polish outbreak of the A6 epidemic was fueled by complex viral migration patterns across the country, together with cross-border transmissions from Ukraine. There is an urgent need to include war-displaced people in the national HIV prevention and treatment programs to reduce the further spread of transmission networks.

H9N2 avian influenza virus dispersal along Bangladeshi poultry trading networks

Avian influenza virus subtype H9N2 is endemic in Bangladesh's poultry population. The subtype affects poultry production and poses a potential zoonotic risk. Insufficient understanding of how the poultry trading network shapes the dissemination of avian influenza viruses has hindered the design of targeted interventions to reduce their spread. Here, we use phylodynamic analyses of haemagglutinin sequences to investigate the spatial spread and dispersal patterns of H9N2 viruses in Bangladesh's poultry population, focusing on its two largest cities (Dhaka and Chattogram) and their poultry production and distribution networks. Our analyses suggest that H9N2 subtype avian influenza virus lineage movement occurs relatively less frequently between Bangladesh's two largest cities than within each city. H9N2 viruses detected in single markets are often more closely related to viruses from other markets in the same city than to each other, consistent with close epidemiological connectivity between markets. Our analyses also suggest that H9N2 viruses may spread more frequently between chickens of the three most commonly sold types (sunali-a cross-bred of Fayoumi hen and Rhode Island Red cock, deshi-local indigenous, and exotic broiler) in Dhaka than in Chattogram. Overall, this study improves our understanding of how Bangladesh's poultry trading system impacts avian influenza virus spread and should contribute to the design of tailored surveillance that accommodates local heterogeneity in virus dispersal patterns.

Deciphering the Tangible Spatio-Temporal Spread of a 25-Year Tuberculosis Outbreak Boosted by Social Determinants

Outbreak strains of Mycobacterium tuberculosis are promising candidates as targets in the search for intrinsic determinants of transmissibility, as they are responsible for many cases with sustained transmission; however, the use of low-resolution typing methods and restricted geographical investigations represent flaws in assessing the success of long-lived outbreak strains. We can now address the nature of outbreak strains by combining large genomic data sets and phylodynamic approaches. We retrospectively sequenced the whole genome of representative samples assigned to an outbreak circulating in the Canary Islands (the GC strain) since 1993, which accounts for ~20% of local tuberculosis cases. We selected a panel of specific single nucleotide polymorphism (SNP) markers for an in-silico search for additional outbreak-related sequences within publicly available tuberculosis genomic data. Using this information, we inferred the origin, spread, and epidemiological parameters of the GC strain. Our approach allowed us to accurately trace the historical and more recent dispersion of the GC strain. We provide evidence of a highly successful nature within the Canarian archipelago but limited expansion abroad. Estimation of epidemiological parameters from genomic data disagree with a distinctive biology of the GC strain. With the increasing availability of genomic data allowing for the accurate inference of strain spread and critical epidemiological parameters, we can now revisit the link between Mycobacterium tuberculosis genotypes and transmission, as is routinely carried out for SARS-CoV-2 variants of concern. We demonstrate that social determinants rather than intrinsically higher bacterial transmissibility better explain the success of the GC strain. Importantly, our approach can be used to trace and characterize strains of interest worldwide. IMPORTANCE Infectious disease outbreaks represent a significant problem for public health. Tracing outbreak expansion and understanding the main factors behind emergence and persistence remain critical to effective disease control. Our study allows researchers and public health authorities to use Whole-Genome Sequencing-based methods to trace outbreaks, and shows how available epidemiological information helps to evaluate the factors underpinning outbreak persistence. Taking advantage of all the freely available information placed in public repositories, researchers can accurately establish the expansion of an outbreak beyond original boundaries, and determine the potential risk of a strain to inform health authorities which, in turn, can define target strategies to mitigate expansion and persistence. Finally, we show the need to evaluate strain transmissibility in different geographic contexts to unequivocally associate spread to local or pathogenic factors, an important lesson taken from genomic surveillance of SARS-CoV-2.

Molecular Basis of the Toxigenic Vibrio cholerae O1 Serotype Switch from Ogawa to Inaba in Haiti

Toxigenic Vibrio cholerae O1 serotype Ogawa was introduced involuntarily into Haiti in October 2010, and virtually all of the clinical strains isolated during the first 5 years of the epidemic were Ogawa. Inaba strains were identified intermittently prior to 2015, with diverse mutations resulting in a common phenotype. In 2015, the percentage of clinical infections due to the Inaba serotype began to rapidly increase, with Inaba supplanting Ogawa as the dominant serotype during the subsequent 4 years. We investigated the molecular basis of the serotype switch and confirmed that all Inaba strains had the same level of mRNA expression of the wbeT genes, as well as the same translation levels for the truncated WbeT proteins in the V. cholerae Inaba isolates. Neither wbeT gene expression levels, differential mutations, or truncation size of the WbeT proteins appeared to be responsible for the successful Inaba switch in 2015. Our phylodynamic analysis demonstrated that the V. cholerae Inaba strains in Haiti evolved directly from Ogawa strains and that a significant increase of diversifying selection at the population level occurred at the time of the Ogawa-Inaba switch. We conclude that the emergence of the Inaba serotype was driven by diversifying selection, independent of the mutational pattern in the wbeT gene. IMPORTANCE Our phylodynamic analysis demonstrated that Vibrio cholerae Inaba strains in Haiti evolved directly from Ogawa strains. Our results support the hypothesis that after an initial Ogawa-dominated epidemic wave, V. cholerae Inaba was able to become the dominant strain thanks to a selective advantage driven by ongoing diversifying selection, independently from the mutational pattern in the wbeT gene.

Infectious bursal disease virus in Western Europe: the rise of reassortant strains as the dominant field threat

Infectious bursal disease virus (IBDV) is a highly contagious birnavirus causing a burdensome immunosuppressive disease in chickens. IBDV features a remarkable antigenic, pathogenic and genetic heterogeneity, with significant implications on disease manifestation, control measures and diagnostic approaches. The recent proposals of comprehensive phylogenetic classification systems offered the ideal platform for large-scale molecular surveys, which are crucial to gather epidemiological data and inform control efforts. In this study, the IBDV scenario was investigated in most of Western Europe by considering the results of diagnostic activities performed internationally throughout 2021. In total, 470 bursal samples from nine different countries were analysed by RT-PCR targeting the VP2. When a field virus was identified, the VP1 was also characterized. Most of the 132 detected field viruses were highly homologous reassortants featuring a very virulent-like VP2 and a classical-like VP1 (genotype A3B1). Despite emerging recently, these reassortants were already signalled in several countries in North-Western Europe and associated with subclinical infections. Here, we report their further spread in the region, where they currently represent the dominant field threat. Two other IBDV types were found, one in Italy, where all the identified viruses clustered in a clade of the A3B1 genotype previously reported only in Russia and the Middle East, and the other in Portugal, where the recently characterized A9B1 genotype was confirmed to circulate. The obtained data suggest the recent occurrence of a major shift in the Western European epidemiological landscape of IBDV, stressing the importance of steady monitoring and sharing of information among different countries and laboratories.RESEARCH HIGHLIGHTS The IBDV scenario in Western Europe seems to have radically changed in recent years.IBDV reassortants were found to be the dominant field type in the region.Local circulation of two other IBDV types was detected in Italy and Portugal.

Early Genomic Surveillance and Phylogeographic Analysis of Getah Virus, a Reemerging Arbovirus, in Livestock in China

Getah virus (GETV) mainly causes disease in livestock and may pose an epidemic risk due to its expanding host range and the potential of long-distance dispersal through animal trade. Here, we used metagenomic next-generation sequencing (mNGS) to identify GETV as the pathogen responsible for reemerging swine disease in China and subsequently estimated key epidemiological parameters using phylodynamic and spatially-explicit phylogeographic approaches. The GETV isolates were able to replicate in a variety of cell lines, including human cells, and showed high pathogenicity in a mouse model, suggesting the potential for more mammal hosts. We obtained 16 complete genomes and 79 E2 gene sequences from viral strains collected in China from 2016 to 2021 through large-scale surveillance among livestock, pets, and mosquitoes. Our phylogenetic analysis revealed that three major GETV lineages are responsible for the current epidemic in livestock in China. We identified three potential positively selected sites and mutations of interest in E2, which may impact the transmissibility and pathogenicity of the virus. Phylodynamic inference of the GETV demographic dynamics identified an association between livestock meat consumption and the evolution of viral genetic diversity. Finally, phylogeographic reconstruction of GETV dispersal indicated that the sampled lineages have preferentially circulated within areas associated with relatively higher mean annual temperature and pig population density. Our results highlight the importance of continuous surveillance of GETV among livestock in southern Chinese regions associated with relatively high temperatures. IMPORTANCE Although livestock is known to be the primary reservoir of Getah virus (GETV) in Asian countries, where identification is largely based on serology, the evolutionary history and spatial epidemiology of GETV in these regions remain largely unknown. Through our sequencing efforts, we provided robust support for lineage delineation of GETV and identified three major lineages that are responsible for the current epidemic in livestock in China. We further analyzed genomic and epidemiological data to reconstruct the recent demographic and dispersal history of GETV in domestic animals in China and to explore the impact of environmental factors on its genetic diversity and its diffusion. Notably, except for livestock meat consumption, other pig-related factors such as the evolution of live pig transport and pork production do not show a significant association with the evolution of viral genetic diversity, pointing out that further studies should investigate the potential contribution of other host species to the GETV outbreak. Our analysis of GETV demonstrates the need for wider animal species surveillance and provides a baseline for future studies of the molecular epidemiology and early warning of emerging arboviruses in China.

Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam

Bayesian space-time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space-time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial-temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data.