Biology subjects

Dellicour, S.

Publications and source records attributed to Dellicour, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Phylodynamic analysis reveals asymmetric and accelerating global spread of dengue virus

Dengue virus (DENV) continues to expand globally, with increasing incidence in both endemic and non-endemic countries. Leveraging a large global dataset of DENV genomes starting from the 1940s, we reconstructed global and regional patterns of DENV spread using phylogeographic and phylodynamic methods. Our analyses reveal that the current global diversity of urban DENV originated in Asia in the first half of the 20th century. During the more than 100 years since, we find that the highest density of between-country spread occurs within Asia and the region continues to be a global source, seeding transmission in Africa and the Americas. Global spread is, however, not balanced. Even though there is more travel to South America, we show that DENV lineages primarily enter North America (mostly from South Asia) through a link defined by seasonal synchrony across the Northern Hemisphere. Along with a substantial increase in global travel starting ~2010, the rate of spread to the Americas is increasing, establishing new lineages associated with large recent outbreaks. DENV lineages do not frequently spread back to Asia from the Americas or Africa, and we posit this is due to higher genetic diversity in Asia, leading to stiffer competition for invading lineages. Within the Americas, the network of DENV dispersal initiates in North America, primarily the Caribbean, and then is maintained by major hubs in South America. Overall, we find that travel volume, rather than climate factors, is mostly responsible for long-term patterns of DENV spread globally and within the Americas. Our results highlight key nodes in the global dynamics of DENV lineages, and suggest spread will continue to accelerate in a more connected world.

genomics↗

bioRxiv2026

Better data, better trees: GenBank-GISAID deduplication and source-specific artifact masking in viral genomics

GenBank and GISAID are the primary repositories for viral genomic data, but integrating records across them remains a challenge. The same sequence could be made available in both databases without any cross-reference linking the two entries. Consequently, there is no systematic way to identify this redundancy, which compromises the compilation of representative, non-redundant large-scale datasets. In parallel, the growth of viral genomic data has increased the risk of systematic technical artifacts introduced during sequencing or assembly. These artifacts can inflate substitution rate estimates and degrade temporal signal, biasing evolutionary rate estimates. To address both challenges, here we present a formal, reproducible workflow integrating two newly developed complementary tools: G2G matcher for cross-repository harmonization and Lab-Specific Bias FILTer (LSBFILT) for masking of laboratory-specific artifacts. Using the Eastern/Central/South African (ECSA) chikungunya virus lineage as a proof-of-concept, we demonstrate that our integrated workflow restores temporal signal and provides a robust, curated dataset for downstream phylodynamic analyses. Critically, restricting masking of homoplastic sites to specific sequences reduces the substitution rate estimate from an inflated 8.517 x 10-4 to 5.078 x 10-4 substitutions/site/year and increases the coefficient of determination (R2) of the root-to-tip regression analysis from 0.353 to 0.677. By enabling systematic cross-repository harmonization and source-specific artifact masking, we provide the molecular epidemiological community with scalable tools to reconcile fragmented genomic data and reduce technical biases, fostering more accurate and reproducible phylogenetic analysis. G2G matcher is available at https://github.com/andrezaleite/G2G-Matcher, and LSBFILT at https://github.com/khourious/LSBFILT.

bioinformatics↗

bioRxiv2026

Breeding pig transport drives the dispersal of swine influenza A virus across Europe

Pigs serve as reservoirs of former human influenza A virus (IAV) H1N1 and H3N2 lineages and act as mixing vessels for diverse strains, facilitating the emergence of novel IAVs. Understanding the spread and evolution of swine IAVs (swIAVs) is therefore crucial to assess the risk of strains with zoonotic potential emerging. This study uses a phylogeographic framework to investigate the predictors of swIAV dispersal across Europe. All publicly available swIAV genomic sequences were retrieved and subsampled for the ten largest European pig-producing countries. Discrete phylogeographic reconstructions were conducted for H1, H3, N1, N2 encoding genes and all internal gene segments. Our analyses indicate that viral dispersal predominantly occurred from north-western to southern and eastern Europe, with frequent long-distance transitions between non-adjacent countries. We also extended the discrete phylogeographical analyses with generalized linear models to test the association between viral movement and potential predictors, such as live pig trade, pork trade, pig densities, farm sizes, or the geographic distance between key pig production zones. We find that breeding pig trade is the only consistently well-supported predictor of between-country transition events, whereas pork trade and geographic distance were not supported. This highlights that farms importing breeding pigs from multiple countries could act as hotspots for reassortment of diverse swIAV strains. Strengthening external biosecurity on farms with emphasis on quarantining breeding pigs, limiting long-distance transport, and implementing a One Health surveillance system for earlier detection of emerging strains, could help curb the rapid spread and evolution of swIAV in Europe. Author summaryInfluenza A viruses that originally came from humans are now circulating in pigs. This is a public health concern because these viruses continue to evolve in pigs and may eventually return to humans in new forms that could cause more severe disease. To better prevent this risk, we need to understand the dispersal dynamics of influenza viruses infecting pigs. For this purpose, we retrieved all publicly available European swine influenza A virus genomic sequences. Using this genomic dataset, we reconstructed how the virus has spread across Europe and investigated the external factors that may have driven the spread. Our results indicate that swine influenza A viruses frequently spread between European countries, in particular from north-western to southern and eastern Europe, and that the trade of pigs intended for breeding is strongly associated with these patterns of spread. Consequently, measures such as quarantining of breeding pigs, could help preventing swine influenza variants from spreading between and becoming established in new regions. We also find major gaps in available genomic data from several European countries. We therefore recommend stronger European surveillance programs that include monitoring influenza viruses in both animals and humans to improve early detection of new emerging viral variants.

genomics↗

bioRxiv2026

Evolutionary history of Jamestown Canyon virus disentangles complex multi-vector ecology

Jamestown Canyon virus (JCV) is a re-emerging mosquito-borne virus of increasing concern in North America. It has been historically understudied, leading to significant gaps in our understanding of its evolutionary history, ecological maintenance, and transmission dynamics. Here, we generated 658 whole-genome JCV sequences from the Northeast United States, including 84% (500/597) of all JCV-positive mosquitoes detected in Connecticut from 1997-2022. Then we applied phylodynamic methods to demonstrate how mosquito phenology and host interaction structure the persistence and spread of JCV. Our phylogenetic analyses estimate that JCV was introduced in the Northeast by at least the early 1700s and the primary introductions of lineages A and B into Connecticut occurred during the mid-1800s to mid-1900s. Further, we estimate that JCV evolves at a rate of [~]3 x 10-5 s/s/y, making it one of the slowest evolving known RNA viruses, because the virus spends [~]10 months per year in evolutionary stasis while over-wintering in mosquito eggs. To investigate ecological drivers of JCV spread in Connecticut, we paired discrete trait and continuous phylogeographic reconstructions with mosquito surveillance data. We estimate that JCV has a low diffusion rate of [~]30-60 km2/year, which is more similar to slow-moving tick-borne viruses than other mosquito-borne viruses. We found that univoltine Aedes mosquitoes were likely to maintain the virus across years through overwintering in eggs, accounting for its slow evolution and dispersal, while multivoltine mosquitoes contribute to periodic bursts of spatial diffusion and amplification within seasons. By characterizing seasonal dynamics of JCV, we demonstrate the utility of dense sequencing and phylodynamics to disentangle complex transmission cycles, offering a framework to rapidly advance our evolutionary and ecological knowledge of understudied viruses.

ecology↗

bioRxiv2025

Emergence of D1.1 reassortant H5N1 avian influenza viruses in North America

Since 2021, highly pathogenic avian influenza viruses (HPAIVs) belonging to H5N1 clade 2.3.4.4b have circulated widely in North American wild birds and repeatedly spilled over into mammals. In 2025, the first H5N1-associated deaths in humans were recorded in the Western hemisphere, raising questions about how the ongoing evolution of the virus in wild birds impacts spillover risk. Here, our analysis of 21,471 H5N1 genomes identified an evolutionary shift in mid-2024, driven by interhemispheric migration from Asia and reassortment with new antigens. The genotypes that dominated the early years of North Americas H5N1 epizootic traced their ancestry back to Europe, but Asia was the source of new "D1.1" genotype viruses that (a) spread faster, (b) have higher reassortment potential, (c) a broader host range, (d) repeatedly spill over to bovines, and (e) cause severe disease in humans, including non-farm workers.

evolutionary biology↗

bioRxiv2025

Escalating human exposure to tropical mosquito-borne viruses in Europe

Human-induced climate change has multiple public health impacts, including the expansion of the geographical range of vector-borne diseases. Pathogens such as dengue, chikungunya and Zika viruses, transmitted by Aedes mosquitos, can cause severe health outcomes ranging from acute febrile illness, chronic joint pain, to birth defects and even death. Evaluating the future risk of human population exposure is therefore crucial as large outbreaks could overwhelm healthcare systems. Europe, one of the fastest warming regions globally, harbours the competent mosquito vector Aedes albopictus in over 20 countries, making tropical Aedes-borne viruses an increasing threat to the continent, which has already experienced local outbreaks over the past two decades. Here we use an ecological niche modelling approach to assess past, present, and future risk of human population exposure to dengue, chikungunya, and Zika viruses in Europe. Our results show that recent climate change has already increased the potential exposure to these viruses, particularly across the Mediterranean basin, which is a current hotspot for local outbreaks. Major metropolitan areas in Spain, France, Italy, and Croatia are by now located in at-risk areas, and this risk is projected to intensify and expand northward by mid-century. Under a high greenhouse gas emissions scenario, European areas ecologically suitable for Aedes-borne virus circulation could increase by up to [~]70%, leading to an additional [~]50 million people living in areas at risk by the end of the century. These findings underscore the urgent need for strengthened vector and epidemiological surveillance, as well as preparedness strategies across newly suitable regions to anticipate future public health threats associated with these arboviral diseases.

ecology↗

bioRxiv2025

The Winners Take It All? Evolutionary Success ofH5Nx Reassortants in the 2020-2024 Panzootic

Avian influenza viruses undergo frequent genetic reassortment, which can coincide with phenotypic changes in transmission, pathogenicity, and host species niche. Since 2020, clade 2.3.4.4b H5 high pathogenicity avian influenza viruses (HPAIVs) have driven a global panzootic, causing mass mortality in wild birds, poultry, and, for the first time, repeated spillover infections in a variety of mammalian species. This worldwide resurgence of H5 HPAIV has coincided with a dramatic increase in the number of circulating reassortant strains; however, the scale, impact and drivers of these reassortants remain unclear. Here, we combined statistical and phylodynamic modelling to reconstruct the global evolutionary dynamics of H5Nx viruses across four epizootic seasons (2020-2024). We identified 209 genetically distinct reassortants, stratified into three transmission categories based on their phylogenetic and epidemiological profiles. Accounting for sampling depth and HPAIV incidence, we estimated that reassortants emerged most frequently from Asia, but major reassortants associated with increased host range, inter-seasonal persistence, and long-range dissemination, more frequently emerged from Europe. Altogether, reassortant emergence followed an episodic pattern in which most reassortants were transient, but 2% seeded large clusters of secondary reassortants soon after their own emergence. Statistical modelling revealed that reassortant success was strongly shaped by ecological factors, including sustained circulation in specific wild bird orders and detection across a wider range of host niches. Collectively, our findings uncover global reassortment dynamics in H5 HPAIVs and identify key virological and ecological drivers underpinning the emergence and spread of successful reassortants. These insights support the importance of enhanced surveillance to track evolution of H5 HPAIV and identify traits relevant for consideration in pandemic risk assessment.

evolutionary biology↗

bioRxiv2025

Navigating sampling bias in discrete phylogeographic analysis: assessing the performance of an adjusted Bayes factor

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modelling.

evolutionary biology↗

bioRxiv2025

Comparative performance of novel viral landscape phylogeography approaches

The fast rate of evolution in RNA viruses implies that their evolutionary and ecological processes occur on the same time scale. Genome sequences of these pathogens can therefore contain information about the processes that govern their transmission and dispersal. In particular, landscape phylogeographic approaches use phylogeographic reconstructions to investigate the impact of environmental factors and variables on the spatial spread of viruses. Here, we extend and improve existing approaches and develop three novel landscape phylogeographic methods that can test the impact of continuous environmental factors on the diffusion velocity of viral lineages. In order to evaluate the different methods, we also implemented two simulation frameworks to test and compare their statistical performance. The results enable us to formulate clear guidelines for the use of three complementary landscape phylogeographic approaches that have sufficient statistical power and low rates of false positives. Our open-source methods are available to the scientific community and can be used to investigate the drivers of viral spread, with potential benefits for understanding virus epidemiology and designing tailored intervention strategies.

evolutionary biology↗

bioRxiv2024

Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward

Avian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds, posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks, particularly those caused by H5N1 and its variants, have surged since their first occurrence in 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded their geographical reach, affecting numerous countries, diverse avian species, and now wild and domestic mammals. Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with the increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches, and predict new areas suitable for local viral circulation. Focusing on H5N1 and H5Nx strains, we have developed ecological niche models for HPAI cases in both wild and domestic birds while considering two distinct periods: 2015-2020 and 2020-2022. Key environmental predictors include chicken and duck population density, human density, distance to water bodies, and several land cover variables. Post-2020, we observe a notable increase in the relative influence of some of these predictors, such as intensive chicken population density and cultivated vegetation. The resulting risk maps reveal notable ecological suitability for local HPAI H5 circulation in Europe, Asia, as well as North and South America, with notable expansions of the areas at risk post-2020. The spatial distribution of HPAI H5 occurrences in wild birds appears to be primarily correlated with urban areas and open water regions. Overall, global risk maps derived from our models identify regions at risk where surveillance and control measures should be prioritised. Finally, our analyses also highlight a potential shift in the diversity of wild bird species affected by HPAI outbreaks, with a higher variety of avian species, particularly sea birds, being impacted post-2020. This increased diversity could suggest that ecological shifts in HPAI H5 circulation may be accompanied by a broader range of susceptible species. Overall, these results further contribute to the understanding of HPAI epidemiology.

ecology↗

bioRxiv2024

Modeling the spatio-temporal annual changes in human tick-borne encephalitis (TBE) risk in Europe

IntroductionTick-borne encephalitis (TBE), caused by tick-borne encephalitis virus (TBEV), is a zoonotic disease that can cause severe neurological symptoms. Given the increasing number of reported human TBE cases in Europe, a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels was developed. MethodsThe distribution of human TBE cases at the regional (NUTS-3) level during the period 2017-2022, was derived by using data provided by the European surveillance system (TESSy, ECDC), and at the municipal level by using data from Austria, Finland, Italy, Lithuania, and Slovakia. The probability of presence of human TBE cases at the regional and municipal levels for the period 2017-2024 was modeled with a boosted regression trees model, including covariates that affect both the natural hazard of virus circulation and human exposure to tick bites. ResultsAreas with the highest probability of human TBE infections are primarily located in central-eastern Europe, the Baltic states, and along the coastline of Nordic countries up to the Bothnian Bay. Our results also highlight a statistically significant rising trend in the probability of human TBE infections not only in north-western, but also in south-western European countries, offering a spatio-temporal predictive framework for the assessment of areas where human TBE infection are most likely to occur. The model showed good predictive performance, with a mean AUC of 0.85 (SD = 0.02), sensitivity of 0.82, and specificity of 0.80 at the regional level, and a mean AUC of 0.82 (SD = 0.03), sensitivity of 0.80, and specificity of 0.69 at the municipal level. DiscussionWith ongoing climate and land use changes, the number of human TBE cases is likely to increase and expand into new areas, as trends are already indicating. This underscores the need for predictive models that can help prioritize intervention efforts. The approach adopted, by leveraging lagged covaries, enables timely one-year-ahead predictions, thus supporting surveillance, prevention, and control of human TBE infections by public health authorities. StatementsO_ST_ABSEthical statementC_ST_ABSEthical approval was not needed. Funding statementThis project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 874850 and is catalogued as MOOD 081. The contents of this publication are the sole responsibility of the authors and dont necessarily reflect the views of the European Commission. Conflict of interestNone. Authors contributionsFrancesca Dagostin: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing - Original Draft. Diana Erazo: Conceptualization, Methodology, Writing - Review & Editing. Giovanni Marini: Conceptualization, Methodology, Writing - Review & Editing. Daniele Da Re: Conceptualization, Methodology, Writing - Review & Editing. Valentina Tagliapietra: Conceptualization, Methodology, Writing - Review & Editing. Maria Avdicova: Resources, Writing - Review & Editing. Tatjana Av[s]i[c] - [Z]upanc: Resources, Writing - Review & Editing. Timothee Dub: Conceptualization, Resources, Writing - Review & Editing. Nahuel Fiorito: Resources, Writing - Review & Editing. Nata[s]a Knap: Resources, Writing - Review & Editing. Celine M. Gossner: Resources, Writing - Review & Editing. Jana Kerlik: Resources, Writing - Review & Editing. Henna Makela: Resources, Writing - Review & Editing. Mateusz Markowicz: Resources,Writing - Review & Editing. Roya Olyazadeh: Resources, Writing - Review & Editing. Lukas Richter: Resources, Writing - Review & Editing. William Wint: Resources, Writing - Review & Editing. Maria Grazia Zuccali: Resources, Writing - Review & Editing. Milda [Z]ygutiene: Resources, Writing - Review & Editing. Simon Dellicour: Methodology, Writing - Review & Editing. Annapaola Rizzoli: Conceptualization, Methodology, Writing - Review & Editing. Data availabilityThe data that support the findings of this study were provided by ECDC, Azienda Provinciale per i Servizi Sanitari Provincia Autonoma di Trento (APSS), Unita Locale Socio Sanitaria Dolomiti (ULSS N.1 Dolomiti), Public Health Authority of the Slovak Republic, Austrian Agency for Health and Food Safety (AGES), Finnish Institute for Health and Welfare (THL), National Public Health Center under the Ministry of Health (Lithuania) and University of Ljubljana. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The interactive risk maps can be explored in detail at https://mood-platform.avia-gis.com. DisclaimerThe views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data.

ecology↗

bioRxiv2024

Grains, trade and war in the multimodal transmission of Rice yellow mottle virus: an historical and phylogeographical retrospective

Rice yellow mottle virus (RYMV) is a major pathogen of rice in Africa. RYMV has a narrow host range limited to rice and a few related poaceae species. We explore the links between the spread of RYMV in East Africa and rice history since the second half of the 19th century. The phylogeography of RYMV in East Africa was reconstructed from coat protein gene sequences (ORF4) of 335 isolates sampled over two million square kilometers between 1966 and 2020. Dispersal patterns obtained from ORF2a and ORF2b, and full-length sequences converged to the same scenario. The following imprints of rice cultivation on RYMV epidemiology were unveiled. RYMV emerged in the middle of the 19th century in the Eastern Arc Mountains where slash-and-burn rice cultivation was practiced. Several spillovers from wild hosts to cultivated rice occurred. RYMV was then rapidly introduced into the adjacent large rice growing Kilombero valley. Harvested seeds are contaminated by debris of virus infected plants that subsist after threshing and winnowing. Long-distance dispersal of RYMV is consistent (i) with rice introduction along the caravan routes from the Indian Ocean Coast to Lake Victoria in the second half of the 19th century, (ii) seed movement from East Africa to West Africa at the end of the 19th century, from Lake Victoria to the north of Ethiopia in the second half of the 20th century and to Madagascar at the end of the 20th century, (iii) and, unexpectedly, with rice transport at the end of the First World War as a troop staple food from the Kilombero valley towards the South of Lake Malawi. Overall, RYMV dispersal was associated to a broad range of human activities, some unsuspected. Consequently, RYMV has a wide dispersal capacity, its dispersal metrics estimated from phylogeographic reconstructions are similar to those of highly mobile zoonotic viruses. Author summaryRice yellow mottle virus (RYMV) poses a major threat to rice production in Africa. We explored through a multidisciplinary approach the links between the history of rice in East Africa since the second half of the 19th century and the spread of RYMV. The results illuminate the causes of RYMV diffusion. We show the role of long-distance caravan trade, the impact of the First World War and the consequences of seed exchange in the dispersal of RYMV. The paradoxical role of seeds in the spread of RYMV - which is vector transmitted and not seed transmitted - is explained in the light of rice biology and agronomy. Overall, this study reveals the wide range of transmission ways, some unexpected, in the dispersal of plant viruses. It also highlights the role of human transmission of pathogens, even vector-borne, and sheds light on the risk of transmission of RYMV and of other plant viruses from Africa to other continents.

evolutionary biology↗

bioRxiv2024

Modeling the velocity of evolving lineages and predicting dispersal patterns

Accurate estimation of the dispersal velocity or speed of evolving organisms is no mean feat. In fact, existing probabilistic models in phylogeography or spatial population genetics generally do not provide an adequate framework to define velocity in a relevant manner. For instance, the very concept of instantaneous speed simply does not exist under one of the most popular approaches that models the evolution of spatial coordinates as Brownian trajectories running along a phylogeny (Lemey et al., 2010). Here, we introduce a new family of models - the so-called "Phylogenetic Integrated Velocity" (PIV) models - that use Gaussian processes to explicitly model the velocity of evolving lineages instead of focusing on the fluctuation of spatial coordinates over time. We describe the properties of these models and show an increased accuracy of velocity estimates compared to previous approaches. Analyses of West Nile virus data in the U.S.A. indicate that PIV models provide sensible predictions of the dispersal of evolving pathogens at a one-year time horizon. These results demonstrate the feasibility and relevance of predictive phylogeography in monitoring epidemics in time and space.

evolutionary biology↗

bioRxiv2024

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.

evolutionary biology↗

bioRxiv2023

The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 has led to increased sampling of related sarbecoviruses circulating primarily in horseshoe bats. These viruses undergo frequent recombination and exhibit spatial structuring across Asia. Employing recombination-aware phylogenetic inference on bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed just [~]1-3 years prior to their emergence in humans. Phylogeographic analyses examining the movement of related sarbecoviruses demonstrate that they traveled at similar rates to their horseshoe bat hosts and have been circulating for thousands of years in Asia. The closest-inferred bat virus ancestor of SARS-CoV likely circulated in western China, and that of SARS-CoV-2 likely circulated in a region comprising southwest China and northern Laos, both a substantial distance from where they emerged. This distance and recency indicate that the direct ancestors of SARS-CoV and SARS-CoV-2 could not have reached their respective sites of emergence via the bat reservoir alone. Our recombination-aware dating and phylogeographic analyses reveal a more accurate inference of evolutionary history than performing only whole-genome or single gene analyses. These results can guide future sampling efforts and demonstrate that viral genomic fragments extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.

evolutionary biology↗

bioRxiv2023

The impact of Quaternary Amazonian river dynamics on patterns and process of diversification in uakari monkeys (genus Cacajao)

AimWestern Amazonia is a region that underwent several landscape changes during the Quaternary. While Riverine Barrier Hypothesis is traditionally used to explain the influence of rivers on speciation, processes such as river rearrangements have been overlooked to explain the geographic distribution and evolutionary history of the Amazonia biota. Here we test how river rearrangements in western Amazonia influenced the evolutionary history of uakari monkeys, a primate group most associated with seasonally flooded forests in western Amazonia. LocationWestern Amazonia TaxonThe uakari monkey (genus Cacajao) MethodsWe performed a continuous phylogeographic analysis using 77 cytochrome b sequences and used digital elevation models to identify the role of landscape and riverscape characteristics in the geographic distribution of Cacajao. Finally, we used genome-wide SNPs variation (ddRADseq) to investigate population structure, gene flow and demographic history in three Cacajao species that were impacted by river rearrangements. ResultsOur continuous phylogeographical reconstruction points that the ancestral Cacajao lineage occupied the flooded forests of the Solimoes River at [~]1.7 Mya, and descendant lineages dispersed throughout western Amazonia more recently. We identified gene flow among both black and bald-headed uakari populations, even across rivers considered barriers (e.g., the Negro River). Landscape analysis showed that river rearrangements influenced the geographic distribution and population structure in Cacajao. The demographic analysis indicates that C. calvus, C. amuna, and C. rubicundus went through a population decline in the last 70 Kya and have a low effective population size. Main conclusionOur results support that the river rearrangements have shaped the geographic distribution and divergence of recently diverged Cacajao lineages. Landscape and riverscape changes, along with retractions of the flooded forests, isolated some Cacajao populations in floodplain areas. Our study also suggests that these events led to the recent population decline in species with a restricted geographic distribution.

evolutionary biology↗

bioRxiv2022

Phylogeographic reconstruction of the emergence and spread of Powassan virus in the northeastern United States

Powassan virus is an emerging tick-borne virus of concern for public health, but very little is known about its transmission patterns and ecology. Here, we expanded the genomic dataset by sequencing 279 Powassan viruses isolated from Ixodes scapularis ticks from the northeastern United States. Our phylogeographic reconstructions revealed that Powassan virus lineage II was likely introduced or emerged from a relict population in the Northeast between 1940-1975. Sequences strongly clustered by sampling location, suggesting a highly focal geographical distribution. Our analyses further indicated that Powassan virus lineage II emerged in the northeastern U.S. mostly following a south to north pattern, with a weighted lineage dispersal velocity of ~3 km/year. Since the emergence in the Northeast, we found an overall increase in the effective population size of Powassan virus lineage II, but with growth stagnating during recent years. The cascading effect of population expansion of white-tailed deer and I. scapularis populations likely facilitated the emergence of Powassan virus in the northeastern U.S. Significance statementOur work provides important fundamental insights in the local transmission dynamics of an emerging tick-borne pathogen of public health concern. Without the availability of vaccines or specific treatments, prevention of Powassan virus infection is dependent on education and control. We identified that Powassan virus is maintained in highly localized transmission foci that have been maintained for several years, without introductions of new virus clades. This provides both opportunities for better education about high risk areas and effective targeted control in Powassan virus foci with a long lasting impact.

genomics↗

bioRxiv2022

Impact and mitigation of sampling bias to determine viral spread: evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations

Bayesian phylogeographic inference is a powerful tool in molecular epidemiological studies that enables reconstructing the origin and subsequent geographic spread of pathogens. Such inference is, however, potentially affected by geographic sampling bias. Here, we investigated the impact of sampling bias on the spatiotemporal reconstruction of viral epidemics using Bayesian discrete phylogeographic models and explored different operational strategies to mitigate this impact. We considered the continuous-time Markov chain (CTMC) model and two structured coalescent approximations (BASTA and MASCOT). For each approach, we compared the estimated and simulated spatiotemporal histories in biased and unbiased conditions based on simulated epidemics of rabies virus (RABV) in dogs in Morocco. While the reconstructed spatiotemporal histories were impacted by sampling bias for the three approaches, BASTA and MASCOT reconstructions were also biased when employing unbiased samples. Increasing the number of analyzed genomes led to more robust estimates at low sampling bias for CTMC. Alternative sampling strategies that maximize the spatiotemporal coverage greatly improved the inference at intermediate sampling bias for CTMC, and to a lesser extent, for BASTA and MASCOT. In contrast, allowing for time-varying population sizes in MASCOT resulted in robust inference. We further applied these approaches to two empirical datasets: a RABV dataset from the Philippines and a SARS-CoV-2 dataset describing its early spread across the world. In conclusion, sampling biases are ubiquitous in phylogeographic analyses but may be accommodated by increasing sample size, balancing spatial and temporal composition in the samples, and informing structured coalescent models with reliable case count data.

evolutionary biology↗