Biology subjects

Lemey, P.

Publications and source records attributed to Lemey, P..

48 records · Page 3Linked to original sources

bioRxiv2021

Archival influenza virus genomes from Europe reveal genomic and phenotypic variability during the 1918 pandemic

The 1918 influenza pandemic was the deadliest respiratory pandemic of the 20th century and determined the genomic make-up of subsequent human influenza A viruses (IAV). Here, we analyze the first 1918 IAV genomes from Europe and from the first, milder wave of the pandemic. 1918 IAV genomic diversity is consistent with local transmission and frequent long-distance dispersal events and in vitro polymerase characterization suggests potential phenotypic variability. Comparison of first and second wave genomes shows variation at two sites in the nucleoprotein gene associated with resistance to host antiviral response, pointing at a possible adaptation of 1918 IAV to humans. Finally, phylogenetic estimates based on extended molecular clock modelling suggests a pure pandemic descent of seasonal H1N1 IAV as an alternative to the hypothesis of an intrasubtype reassortment origin. One Sentence SummaryMuch can be learned about past pandemics by uncovering their footprints in medical archives, which we here demonstrate for the 1918 flu pandemic.

microbiology↗

bioRxiv2020

Unravelling the evolutionary relationships of hepaciviruses within and across rodent hosts

Hepatitis C virus (HCV; genus Hepacivirus) represents a major public health problem, infecting about 3 % of the human population ({+/-} 185,000,000 people). Because no plausible animal reservoir carrying closely related hepaciviruses has been identified, the zoonotic origins of HCV still remain elusive. Motivated by recent findings of divergent hepaciviruses in rodents and a plausible African origin of HCV genotypes, we have screened a comprehensive collection of small mammals samples from seven sub-Saharan African countries. Out of 4,303 samples screened, 80 were found positive for the presence of hepaciviruses in 29 different host species. We here report 56 novel genomes that considerably increase the diversity of three divergent rodent hepacivirus lineages, which previously were almost exclusively represented by New World and European hepaciviruses. Further-more, we provide undisputable evidence for hepacivirus co-infections in rodents, which remarkably, we exclusively but repeatedly found in four sampled species of brush-furred mice. We also point at hepacivirus co-infections indirectly in different animal hosts by demonstrating evidence for recombination within specific host lineages. Our study considerably expands the available hepacivirus genomic data and elucidates the relatively deep evolutionary history that these pathogens have in rodents compared to other mammalian hosts. Overall, our results emphasize the importance of rodents as a potential hepacivirus reservoir and as models for investigating HCV infection dynamics.

evolutionary biology↗

bioRxiv2020

Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study.

Spatiotemporal bias in genome sequence sampling can severely confound phylogeographic inference based on discrete trait ancestral reconstruction. This has impeded our ability to accurately track the emergence and spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic. Despite the availability of unprecedented numbers of SARS-CoV-2 genomes on a global scale, evolutionary reconstructions are hindered by the slow accumulation of sequence divergence over its relatively short transmission history. When confronted with these issues, incorporating additional contextual data may critically inform phylodynamic reconstructions. Here, we present a new approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2, while also including global air transportation data. We demonstrate that including travel history data for each SARS-CoV-2 genome yields more realistic reconstructions of virus spread, particularly when travelers from undersampled locations are included to mitigate sampling bias. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations in the analyses. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts. Although further research is needed to fully examine the performance of our travel-aware phylogeographic analyses with unsampled diversity and to further improve them, they represent multiple new avenues for directly addressing the colossal issue of sample bias in phylogeographic inference.

evolutionary biology↗

bioRxiv2020

The emergence of SARS-CoV-2 in Europe and the US

Accurate understanding of the global spread of emerging viruses is critically important for public health response and for anticipating and preventing future outbreaks. Here, we elucidate when, where and how the earliest sustained SARS-CoV-2 transmission networks became established in Europe and the United States (US). Our results refute prior findings erroneously linking cases in January 2020 with outbreaks that occurred weeks later. Instead, rapid interventions successfully prevented onward transmission of those early cases in Germany and Washington State. Other, later introductions of the virus from China to both Italy and Washington State founded the earliest sustained European and US transmission networks. Our analyses reveal an extended period of missed opportunity when intensive testing and contact tracing could have prevented SARS-CoV-2 from becoming established in the US and Europe.

genetics↗

bioRxiv2020

Temporal signal and the phylodynamic threshold of SARS-CoV-2

The ongoing SARS-CoV-2 outbreak marks the first time that large amounts of genome sequence data have been generated and made publicly available in near real-time. Early analyses of these data revealed low sequence variation, a finding that is consistent with a recently emerging outbreak, but which raises the question of whether such data are sufficiently informative for phylogenetic inferences of evolutionary rates and time scales. The phylodynamic threshold is a key concept that refers to the point in time at which sufficient molecular evolutionary change has accumulated in available genome samples to obtain robust phylodynamic estimates. For example, before the phylodynamic threshold is reached, genomic variation is so low that even large amounts of genome sequences may be insufficient to estimate the viruss evolutionary rate and the time scale of an outbreak. We collected genome sequences of SARS-CoV-2 from public databases at 8 different points in time and conducted a range of tests of temporal signal to determine if and when the phylodynamic threshold was reached, and the range of inferences that could be reliably drawn from these data. Our results indicate that by February 2nd 2020, estimates of evolutionary rates and time scales had become possible. Analyses of subsequent data sets, that included between 47 to 122 genomes, converged at an evolutionary rate of about 1.1x10-3 subs/site/year and a time of origin of around late November 2019. Our study provides guidelines to assess the phylodynamic threshold and demonstrates that establishing this threshold constitutes a fundamental step for understanding the power and limitations of early data in outbreak genome surveillance.

evolutionary biology↗

bioRxiv2020

Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic

There are outstanding evolutionary questions on the recent emergence of coronavirus SARS-CoV-2/hCoV-19 in Hubei province that caused the COVID-19 pandemic, including (1) the relationship of the new virus to the SARS-related coronaviruses, (2) the role of bats as a reservoir species, (3) the potential role of other mammals in the emergence event, and (4) the role of recombination in viral emergence. Here, we address these questions and find that the sarbecoviruses - the viral subgenus responsible for the emergence of SARS-CoV and SARS-CoV-2 - exhibit frequent recombination, but the SARS-CoV-2 lineage itself is not a recombinant of any viruses detected to date. In order to employ phylogenetic methods to date the divergence events between SARS-CoV-2 and the bat sarbecovirus reservoir, recombinant regions of a 68-genome sarbecovirus alignment were removed with three independent methods. Bayesian evolutionary rate and divergence date estimates were consistent for all three recombination-free alignments and robust to two different prior specifications based on HCoV-OC43 and MERS-CoV evolutionary rates. Divergence dates between SARS-CoV-2 and the bat sarbecovirus reservoir were estimated as 1948 (95% HPD: 1879-1999), 1969 (95% HPD: 1930-2000), and 1982 (95% HPD: 1948-2009). Despite intensified characterization of sarbecoviruses since SARS, the lineage giving rise to SARS-CoV-2 has been circulating unnoticed for decades in bats and been transmitted to other hosts such as pangolins. The occurrence of a third significant coronavirus emergence in 17 years together with the high prevalence and virus diversity in bats implies that these viruses are likely to cross species boundaries again. In BriefThe Betacoronavirus SARS-CoV-2 is a member of the sarbecovirus subgenus which shows frequent recombination in its evolutionary history. We characterize the extent of this genetic exchange and identify non-recombining regions of the sarbecovirus genome using three independent methods to remove the effects of recombination. Using these non-recombining genome regions and prior information on coronavirus evolutionary rates, we obtain estimates from three approaches that the most likely divergence date of SARS-CoV-2 from its most closely related available bat sequences ranges from 1948 to 1982. Key PointsO_LIRaTG13 is the closest available bat virus to SARS-CoV-2; a sub-lineage of these bat viruses is able to infect humans. Two sister lineages of the RaTG13/SARS-CoV-2 lineage infect Malayan pangolins. C_LIO_LIThe sarbecoviruses show a pattern of deep recombination events, indicating that there are high levels of co-infection in horseshoe bats and that the viral pool can generate novel allele combinations and substantial genetic diversity; the sarbecoviruses are efficient explorers of phenotype space. C_LIO_LIThe SARS-CoV-2 lineage is not a recent recombinant, at least not involving any of the bat or pangolin viruses sampled to date. C_LIO_LINon-recombinant regions of the sarbecoviruses can be identified, allowing for phylogenetic inference and dating to be performed. We constructed three such regions using different methods. C_LIO_LIWe estimate that RaTG13 and SARS-CoV-2 diverged 40 to 70 years ago. There is a diverse unsampled reservoir of generalist viruses established in horseshoe bats. C_LIO_LIWhile an intermediate host responsible for the zoonotic event cannot be ruled out, the relevant evolution for spillover to humans very likely occurred in horseshoe bats. C_LI

evolutionary biology↗

bioRxiv2020

nosoi: a stochastic agent-based transmission chain simulation framework in R

The transmission process of an infectious agent creates a connected chain of hosts linked by transmission events, known as a transmission chain. Reconstructing transmission chains remains a challenging endeavor, except in rare cases characterized by intense surveillance and epidemiological inquiry. Inference frameworks attempt to estimate or approximate these transmission chains but the accuracy and validity of such methods generally lack formal assessment on datasets for which the actual transmission chain was observed. We here introduce nosoi, an open-source R package that offers a complete, tunable, and expandable agent-based framework to simulate transmission chains under a wide range of epidemiological scenarios for single-host and dual-host epidemics. nosoi is accessible through GitHub and CRAN, and is accompanied by extensive documentation, providing help and practical examples to assist users in setting up their own simulations. Once infected, each host or agent can undergo a series of events during each time step, such as moving (between locations) or transmitting the infection, all of these being driven by user-specified rules or data, such as travel patterns between locations. nosoi is able to generate a multitude of epidemic scenarios, that can - for example - be used to validate a wide range of reconstruction methods, including epidemic modeling and phylodynamic analyses. nosoi also offers a comprehensive framework to leverage empirically acquired data, allowing the user to explore how variations in parameters can affect epidemic potential. Aside from research questions, nosoi can provide lecturers with a complete teaching tool to offer students a handson exploration of the dynamics of epidemiological processes and the factors that impact it. Because the package does not rely on mathematical formalism but uses a more intuitive algorithmic approach, even extensive changes of the entire model can be easily and quickly implemented.

ecology↗

bioRxiv2019

The history of measles: from a 1912 genome to an antique origin

Many infectious diseases are thought to have emerged in humans after the Neolithic revolution. While it is broadly accepted that this also applies to measles, the exact date of emergence for this disease is controversial. Here, we sequenced the genome of a 1912 measles virus and used selection-aware molecular clock modeling to determine the divergence date of measles virus and rinderpest virus. This divergence date represents the earliest possible date for the establishment of measles in human populations. Our analyses show that the measles virus potentially arose as early as the 4th century BCE, rekindling the recently challenged hypothesis of an antique origin of this disease. One Sentence SummaryMeasles virus diverged from rinderpest virus in the 4th century BCE, which is compatible with an emergence of measles during Antiquity.

evolutionary biology↗

bioRxiv2019

Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing

Computational analyses of pathogen genomes are increasingly used to unravel the dispersal history and transmission dynamics of epidemics. Here, we show how to go beyond historical reconstructions and use spatially-explicit phylogeographic and phylodynamic approaches to formally test epidemiological hypotheses. We illustrate our approach by focusing on the West Nile virus (WNV) spread in North America that has been responsible for substantial impacts on public, veterinary, and wildlife health. WNV isolates have been sampled at various times and locations across North America since its introduction to New York twenty years ago. We exploit this genetic data repository to demonstrate that factors hypothesised to affect viral dispersal and demography can be formally tested. Specifically, we detail and apply an analytical workflow consisting of state-of-the art methods that we further improve to test the impact of environmental factors on the dispersal locations, velocity, and frequency of viral lineages, as well as on the genetic diversity of the viral population through time. We find that WNV lineages tend to disperse faster in areas with higher temperatures and we identify temporal variation in temperature as a main predictor of viral genetic diversity through time. Using a simulation procedure, we find no evidence that viral lineages preferentially circulate within the same migratory bird flyway, suggesting a substantial role for non-migratory birds or mosquito dispersal along the longitudinal gradient. Finally, we also separately apply our testing approaches on the three WNV genotypes that circulated in North America in order to understand and compare their dispersal ability. Our study demonstrates that the development and application of statistical approaches, coupled with comprehensive pathogen genomic data, can address epidemiological questions that might otherwise be difficult or impractically expensive to answer.

evolutionary biology↗

bioRxiv2019

A near-full-length HIV-1 genome from 1966 recovered from formalin-fixed paraffin-embedded tissue

Although estimated to have emerged in humans in Central Africa in the early 1900s, HIV-1, the main causative agent of AIDS, was only discovered in 1983. With very little direct biological data of HIV-1 from before the 1980s, far-reaching evolutionary and epidemiological inferences regarding the long pre-discovery phase of this pandemic are based on extrapolations by phylodynamic models of HIV-1 genomic sequences gathered mostly over recent decades. Here, using a very sensitive multiplex RT-PCR assay, we screened 1,652 formalin-fixed paraffin-embedded tissue specimens collected for pathology diagnostics in Kinshasa, Democratic Republic of Congo (DRC), between 1959 and 1967. We report the near-complete genome of one positive from 1966 (\"DRC66\")--a non-recombinant sister lineage to subtype C that constitutes the oldest HIV-1 near-full-length genome recovered to date. Root-to-tip plots showed the DRC66 sequence is not an outlier as would be expected if dating estimates from more recent genomes were systematically biased; and inclusion of DRC66 sequence in tip-dated BEAST analyses did not significantly alter root and internal node age estimates based on post-1978 HIV-1 sequences. There was larger variation in divergence time estimates among datasets that were subsamples of the available HIV-1 genomes from 1978-2015, showing the inherent phylogenetic stochasticity across subsets of the real HIV-1 diversity. In conclusion, this unique archival HIV-1 sequence provides direct genomic insight into HIV-1 in 1960s DRC, and, as an ancient-DNA calibrator, it validates our understanding of HIV-1 evolutionary history.\n\nSignificanceInferring the precise timing of the origin of the HIV/AIDS pandemic is of great importance because it offers insights into which factors did--or did not--facilitate the emergence of the causal virus. Previous estimates have implicated rapid development during the early 20th century in Central Africa, which wove once-isolated populations into a more continuous fabric. We recovered the first HIV-1 genome from the 1960s, and it provides direct evidence that HIV-1 molecular clock estimates spanning the last half-century are remarkably reliable. And, because this genome itself was sampled only about a half-century after the estimated origin of the pandemic, it empirically anchors this crucial inference with high confidence.

evolutionary biology↗

bioRxiv2018

Whole genome analysis of local Kenyan and global sequences unravels the epidemiological and molecular evolutionary dynamics of RSV genotype ON1 strains

The respiratory syncytial virus (RSV) group A variant with the 72-nucleotide duplication in the G gene, genotype ON1, was first detected in Kilifi in 2012 and has almost completely replaced previously circulating genotype GA2 strains. This replacement suggests some fitness advantage of ON1 over the GA2 viruses, and might be accompanied by important genomic substitutions in ON1 viruses. Close observation of such a new virus introduction over time provides an opportunity to better understand the transmission and evolutionary dynamics of the pathogen. We have generated and analyzed 184 RSV-A whole genome sequences (WGS) from Kilifi (Kenya) collected between 2011 and 2016, the first ON1 genomes from Africa and the largest collection globally from a single location. Phylogenetic analysis indicates that RSV-A transmission into this coastal Kenya location is characterized by multiple introductions of viral lineages from diverse origins but with varied success in local transmission. We identify signature amino acid substitutions between ON1 and GA2 viruses within genes encoding the surface proteins (G, F), polymerase (L) and matrix M2-1 proteins, some of which were identified as positively selected, and thereby provide an enhanced picture of RSV-A diversity. Furthermore, five of the eleven RSV open reading frames (ORF) (i.e. G, F, L, N and P), analyzed separately, formed distinct phylogenetic clusters for the two genotypes. This might suggest that coding regions outside of the most frequently studied G ORF play a role in the adaptation of RSV to host populations with the alternative possibility that some of the substitutions are nothing more than genetic hitchhikers. Our analysis provides insight into the epidemiological processes that define RSV spread, highlights the genetic substitutions that characterize emerging strains, and demonstrates the utility of large-scale WGS in molecular epidemiological studies.\n\nAuthor summaryRespiratory syncytial virus (RSV) is the leading viral cause of severe pneumonia and bronchiolitis among infants and children globally. No vaccine exists to date. The high genetic variability of this RNA virus, characterized by group (A or B), genotype (within group) and variant (within genotype) replacement in populations, may pose a challenge to effective vaccine design by enabling immune response escape. To date most sequence data exists for the highly variable G gene encoding the RSV attachment protein, and there is little globally-sampled RSV genomic data to provide a fine resolution of the epidemiology and evolutionary dynamics of the pathogen. Here we use long-term RSV surveillance in coastal Kenya to track the introduction, spread and evolution of a new RSV genotype known as ON1 (having a 72-nucleotide duplication in the G gene). We present a set of 184 RSV-A whole genomes, including 176 of RSV ON1 (the first from Africa), describe patterns of local ON1 spread and show genome-wide changes between the two major RSV-A genotypes that may define the pathogens adaptation to the host. These findings have implications for vaccine design and improved understanding of RSV epidemiology and evolution.

epidemiology↗

bioRxiv2017

Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100046). The recent Ebola virus (EBOV) outbreak in West Africa witnessed considerable efforts to obtain viral genomic data as the epidemic was unfolding. If such data can be deployed in real-time, molecular epidemiological investigations could play a role in complementing contact tracing undertaken by public health agencies. Analysing the EBOV genomes accumulated to date can also deliver insights into epidemic dynamics. Such analyses have been shown that metapopulation dynamics were critical for EBOV dispersal between rural and urban areas during the epidemic, but the implications for specific intervention scenarios remain unclear. Here, we address this issue using a collection of phylodynamic approaches. We show that long-distance dispersal events (between administrative areas >250 km apart) were not crucial for epidemic expansion and that preventing viral lineage movement to any given administrative area would, in most cases, have had little impact. However, urban areas - specifically those encompassing the three capital cities and their suburbs - were critical in attracting and further disseminating the virus: preventing viral lineage movement to all three simultaneously would have contained epidemic size by two-thirds. Using continuous phylogeographic reconstructions we estimate a distance kernel for EBOV spread and reveal considerable heterogeneity in dispersal velocity through time. We also show that announcements of border closures were followed by a significant but transient effect on international virus dispersal. By quantifying the hypothetical impact of different intervention strategies as well as the impact of barriers on dispersal frequency, our study illustrates how phylodynamic analyses can help to address specific epidemiological and outbreak control questions.

epidemiology↗