Biology subjects

Duchene, S.

Publications and source records attributed to Duchene, S..

33 records · Page 2Linked to original sources

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

Direct RNA sequencing and early evolution of SARS-CoV-2

Fundamental aspects of SARS-CoV-2 biology remain to be described, having the potential to provide insight to the response effort for this high-priority pathogen. Here we describe the first native RNA sequence of SARS-CoV-2, detailing the coronaviral transcriptome and epitranscriptome, and share these data publicly. A data-driven inference of viral genetic features and evolutionary rate is also made. The rapid sharing of sequence information throughout the SARS-CoV-2 pandemic represents an inflection point for public health and genomic epidemiology, providing early insights into the biology and evolution of this emerging pathogen.

microbiology↗

bioRxiv2019

Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behaviour of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

bioinformatics↗

bioRxiv2019

Inferring infectious disease phylodynamics with notification data

Point 1Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially amid the SARS-CoV-2 outbreak, new practical questions about their use are emerging. Point 2One such question focuses on the inclusion of un-sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. Point 3Using simulations, we demonstrate that birth-death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS-CoV-2 epidemic in the northwest USA supports these results. Point 4We conclude that occurrence data are a valuable source of information in combination with birth-death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future.

evolutionary biology↗

bioRxiv2019

The molecular clock of Mycobacterium tuberculosis

The molecular clock and its phylogenetic applications to genomic data have changed how we study and understand one of the major human pathogens, Mycobacterium tuberculosis (MTB), the causal agent of tuberculosis. Genome sequences of MTB strains sampled at different times are increasingly used to infer when a particular outbreak begun, when a drug resistant clone appeared and expanded, or when a strain was introduced into a specific region. Despite the growing importance of the molecular clock in tuberculosis research, there is a lack of consensus as to whether MTB displays a clocklike behavior and about its rate of evolution. Here we performed a systematic study of the MTB molecular clock on a large genomic data set (6,285 strains), covering most of the global MTB diversity and representing different epidemiological settings. We found wide variation in the degree of clocklike structure among data sets, indicating that sampling times are sometimes insufficient to calibrate the clock of MTB. For data sets with temporal structure, we found that MTB genomes accumulate between 1x10-8 and 5x10-7 nucleotide changes per-site-per-year, which corresponds to 0.04 - 2.2 SNPs per-genome-per-year. Contrary to what expected, these estimates were not dependent on the time of the calibration points as they did not change significantly when we used epidemiological isolates (sampled in the last 40 years) or ancient DNA samples (about 1,000 years old) to calibrate the tree. Additionally, the uncertainty and the discrepancies in the results of different methods were often large, highlighting the importance of using different methods, and of considering carefully their assumptions and limitations. Significance StatementOne of the major recent advancement in evolutionary biology is the development of statistical methods to infer the past evolutionary history of species and populations with genomic data. In the last five years, many researchers have used the molecular clock to study the evolution of Mycobacterium tuberculosis, a bacterial pathogen that causes tuberculosis and is responsible for millions of human deaths every year. The application of the molecular clock to tuberculosis is extremely useful to understand the evolution of drug resistance, the spread of different strains and the origin of the disease. Since some of these studies found contrasting results, we performed a systematic analysis of the molecular clock of MTB. This study will provide an important guideline for future analyses of tuberculosis and other organisms.

evolutionary biology↗

bioRxiv2018

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.\n\nDeveloping such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.\n\nAuthor summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

evolutionary biology↗

bioRxiv2018

Linking Branch Lengths Across Loci Provides the Best Fit for Phylogenetic Inference

Evolution leaves heterogeneous patterns of nucleotide variation across the genome, with different loci subject to varying degrees of mutation, selection, and drift. Appropriately modelling this heterogeneity is important for reliable phylogenetic inference. One modelling approach in statistical phylogenetics is to apply independent models of molecular evolution to different groups of sites, where the groups are usually defined by locus, codon position, or combinations of the two. The potential impacts of partitioning data for the assignment of substitution models are well appreciated. Meanwhile, the treatment of branch lengths has received far less attention. In this study, we examined the effects of linking and unlinking branch-length parameters across loci. By analysing a range of empirical data sets, we find that the best-fitting model for phylogenetic inference is consistently one in which branch lengths are proportionally linked: gene trees have the same pattern of branch-length variation, but with varying absolute tree lengths. This model provided a substantially better fit than those that either assumed identical branch lengths across gene trees or that allowed each gene tree to have its own distinct set of branch lengths. Using simulations, we show that the fit of the three different models of branch lengths varies with the length of the sequence alignment and with the number of taxa in the data set. Our findings suggest that a model with proportionally linked branch lengths across loci is likely to provide the best fit under the conditions that are most commonly seen in practice. In future work, improvements in fit might be afforded by models with levels of complexity intermediate to proportional and free branch lengths. The results of our study have implications for model selection, computational efficiency, and experimental design in phylogenomics.

evolutionary biology↗

bioRxiv2018

Distinct evolutionary dynamics of horizontal gene transfer in drug resistant and virulent clones of Klebsiella pneumoniae

Klebsiella pneumoniae (Kp) has emerged as an important cause of two distinct public health threats: multidrug resistant (MDR) healthcare-associated infections1 and community-acquired invasive infections, particularly pyogenic liver abscess2. The majority of MDR hospital outbreaks are caused by a subset of Kp clones with a high prevalence of acquired antimicrobial resistance (AMR) genes, while the majority of community-acquired invasive infections are caused by hypervirulent clones that rarely harbour acquired AMR genes but have high prevalence of key virulence loci3-5. Worryingly, the last few years have seen increasing reports of convergence of MDR and the key virulence genes within individual Kp strains6, but it is not yet clear whether these represent a transient phenomenon or a significant ongoing threat. Here we perform comparative genomic analyses for 28 distinct Kp clones, including 6 hypervirulent and 8 MDR, to better understand their evolutionary histories and the risks of convergence. We show that MDR clones are highly diverse with frequent chromosomal recombination and gene content variability that far exceeds that of the hypervirulent clones. Consequently, we predict a much greater risk of virulence gene acquisition by MDR Kp clones than of resistance gene acquisition by hypervirulent clones.

microbiology↗

bioRxiv2018

Phylodynamic model adequacy using posterior predictive simulations

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data was best described by the birth-death SIR model.

bioinformatics↗

bioRxiv2018

Laboratory and Molecular Surveillance of Paediatric Typhoidal Salmonella in Nepal: Antimicrobial Resistance and Implications for Vaccine Policy

BackgroundChildren are substantially affected by enteric fever in most settings with a high burden of the disease, which could be due to immune naivety, or enhanced risk of exposure to the pathogen. Although Nepal is a high burden setting for enteric fever, the bacterial population structure and transmission dynamics are poorly delineated in young children, the proposed target group for immunization programs.\n\nMethodsBlood culture surveillance amongst children aged 2 months to 15 years of age was conducted at Patan Hospital between 2008 and 2016. A total of 198 S. Typhi and 66 S. Paratyphi A isolated from children treated in both inpatient and outpatient settings were subjected to whole genome sequencing and antimicrobial susceptibility testing. Demographic and clinical data were also collected from the inpatients. The resulting data were used to place these paediatric Nepali isolates into a worldwide context, based on their phylogeny and carriage of molecular determinants of antimicrobial resistance (AMR).\n\nResultsChildren aged [≤]4 years made up >40% of the inpatient population. The majority of isolates (78 %) were S. Typhi, comprising several distinct genotypes but dominated by 4.3.1 (H58). Several distinct S. Typhi genotypes were identified, but the globally disseminated S. Typhi clade 4.3.1 (H58) dominated. The majority of isolates (86%) were insusceptible to fluoroquinolones. This was mainly associated with S. Typhi H58 Lineage II and S. Paratyphi A; non-susceptible strains from these two genotypes accounted for 50% and 25% of all enteric fever cases. Multi-drug resistance (MDR) was rare (3.5% of S. Typhi, 0 S. Paratyphi A) and restricted to chromosomal insertions of AMR genes in H58 lineage I strains. Comparison to global data sets showed the local S. Typhi and S. Paratyphi A strains had close genetic relatives in other South Asian countries, indicating regional strain circulation.\n\nConclusionsThese data indicate that enteric fever in Nepal continues to be a major public health issue with ongoing inter- and intra-country transmission, and highlights the need for regional coordination of intervention strategies. The absence of a S. Paratyphi A vaccine is cause for concern, given its prevalence as an enteric fever agent in this setting, and the large proportion of isolates displaying fluoroquinolone resistance. This study also highlights an urgent need for routine laboratory and molecular surveillance to monitor the epidemiology of enteric fever and evolution of antimicrobial resistance within the bacterial population as a means to facilitate public health interventions in prevention and control of this febrile illness.

molecular biology↗

bioRxiv2017

Global phylogenomics of multidrug-resistant Staphylococcus aureus sequence type 772: the Bengal Bay clone

The global spread of antimicrobial resistance has been well documented in Gram-negative bacteria and healthcare-associated epidemic pathogens, often emerging from regions with heavy antimicrobial use. However, the degree to which similar processes occur with Gram-positive bacteria in the community setting is less well understood. Here we demonstrate the recent origin and global spread from the Indian subcontinent of a multidrug resistant Staphylococcus aureus lineage, sequence type 772 (Bengal Bay clone). Short-term outbreaks occurred following intercontinental transmission, typically associated with travel and family contacts, but ongoing endemic transmission was uncommon. Instrumental in the emergence of a single dominant clade in the early 1990s was the acquisition of a multidrug resistance integrated plasmid that did not appear to incur a significant fitness cost. The Bengal Bay clone therefore combines the multidrug resistance of traditional healthcare-associated clones with the epidemiological and virulence potential of community-associated clones.

genomics↗

bioRxiv2017

Population genomics of hypervirulent Klebsiella pneumoniae clonal group 23 reveals early emergence and rapid global dissemination

Since the mid-1980s there have been increasing reports of severe community-acquired pyogenic liver abscess, meningitis and bloodstream infections caused by hypervirulent Klebsiella pneumoniae, predominantly encompassing clonal group (CG) 23 serotype K1 strains. Common features of CG23 include a virulence plasmid associated with iron scavenging and hypermucoidy, and a chromosomal integrative and conjugative element (ICE) encoding the siderophore yersiniabactin and the genotoxin colibactin. Here we investigate the evolutionary history and genomic diversity of CG23 based on comparative analysis of 98 genomes. Contrary to previous reports with more limited samples, we show that CG23 comprises several deep branching sublineages dating back to the 1870s, many of which are associated with distinct chromosomal insertions of ICEs encoding yersiniabactin. We find that most liver abscess isolates (>80%) belong to a dominant sublineage, CG23-I, which emerged in the 1920s following acquisition of ICEKp10 (encoding colibactin in addition to yersiniabactin) and has undergone clonal expansion and global dissemination within the human population. The unique genomic feature of CG23-I is the production of colibactin, which has been reported previously as a promoter of gut colonisation and dissemination to the liver and brain in a mouse model of CG23 K. pneumoniae infection, and has been linked to colorectal cancer. We also identify an antibiotic-resistant subclade of CG23-I associated with sexually-transmitted infections in horses dating back to the 1980s. These data show that hypervirulent CG23 K. pneumoniae was circulating in humans for decades before the liver abscess epidemic was first recognised, and has the capacity to acquire and maintain AMR plasmids. These data provide a framework for future epidemiological and experimental studies of hypervirulent K. pneumoniae. To further support such studies we present an open access and completely sequenced human liver abscess isolate, SGH10, which is typical of the globally disseminated CG23-I sublineage.

genomics↗

bioRxiv2017

Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods

BackgroundRecent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale.\n\nResultsWe compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart.\n\nConclusionsHybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

bioinformatics↗

bioRxiv2017

A Comparison of Methods for Estimating Substitution Rates from Ancient DNA Sequence Data

The estimation of evolutionary rates from ancient DNA sequences can be negatively affected by among-lineage rate variation and non-random sampling. Using a simulation study, we compared the performance of three phylogenetic methods for inferring evolutionary rates from time-structured data sets: root-to-tip regression, least-squares dating, and Bayesian inference. Our results show that these methods produce reliable estimates when the substitution rate is high, rate variation is low, and samples of similar ages are not phylogenetically clustered. The interaction of these factors is particularly important for Bayesian estimation of evolutionary rates. We also inferred rates for time-structured mitogenomic data sets from six vertebrate species. Root-to-tip regression estimated a different rate from least-squares dating and Bayesian inference for mitogenomes from the horse, which has high levels of among-lineage rate variation. We recommend using multiple methods of inference and testing data for temporal signal, among-lineage rate variation, and phylo-temporal clustering.

evolutionary biology↗

bioRxiv2017

Millennia of genomic stability within the invasive Para C Lineage of Salmonella enterica

Salmonella enterica serovar Paratyphi C is the causative agent of enteric (paratyphoid) fever. While today a potentially lethal infection of humans that occurs in Africa and Asia, early 20th century observations in Eastern Europe suggest it may once have had a wider-ranging impact on human societies. We recovered a draft Paratyphi C genome from the 800-year-old skeleton of a young woman in Trondheim, Norway, who likely died of enteric fever. Analysis of this genome against a new, significantly expanded database of related modern genomes demonstrated that Paratyphi C is descended from the ancestors of swine pathogens, serovars Choleraesuis and Typhisuis, together forming the Para C Lineage. Our results indicate that Paratyphi C has been a pathogen of humans for at least 1,000 years, and may have evolved after zoonotic transfer from swine during the Neolithic period.\n\nOne Sentence SummaryThe combination of an 800-year-old Salmonella enterica Paratyphi C genome with genomes from extant bacteria reshapes our understanding of this pathogens origins and evolution.

microbiology↗