Bayesian Inference of the Evolution of a Phenotype Distribution on a Phylogenetic Tree

Amylase-associated genetic pattern in Xanthomonas euvesicatoria on pepper

Bacterial leaf spot of pepper (BSP), primarily caused by Xanthomonas euvesicatoria (Xe), poses a significant challenge to pepper production worldwide. Despite its impact, the genetic diversity of this pathogen remains underexplored, which limits our understanding of its population structure. To bridge this knowledge gap, we conducted a comprehensive analysis using 103 Xe strains isolated from pepper in southwest Florida to characterize genomic and type III effector (T3E) variation in this population. Phylogenetic analysis of core genomes revealed a major distinct genetic lineage associated with amylolytic activity. This amylolytic lineage was represented in Xe strains globally. Molecular clock analysis dated the emergence of amylolytic strains in Xe to around 1972. Notably, non-amylolytic strains possessed a single base pair frameshift deletion in the ⍺-amylase gene yet retained a conserved C-terminus. GUS assay revealed the expression of two open reading frames in non-amylolytic strains, one at the N-terminus and another that starts 136 base pairs upstream of the ⍺-amylase gene. Analysis of T3Es in the Florida Xe population identified variation in 12 effectors, including two classes of mutations in avrBs2 that prevent AvrBs2 from triggering a hypersensitive response in Bs2-resistant pepper plants. Knowledge of T3E variation could be used for effector-targeted disease management. This study revealed previously undescribed population structure in this economically important pathogen.IMPORTANCEBacterial leaf spot (BSP), a significant threat to pepper production globally, is primarily caused by Xanthomonas euvesicatoria (Xe). Limited genomic data has hindered detailed studies on its population diversity. This study analyzed the whole-genome sequences of 103 Xe strains from peppers in southwest Florida, along with additional global strains, to explore the pathogen's diversity. The study revealed two major distinct genetic groups based on their amylolytic activity, the ability to break down starch, with non-amylolytic strains having a mutation in the ⍺-amylase gene. Additionally, two classes of mutations in the avrBs2 gene were found, leading to susceptibility in pepper plants with the Bs2 resistance gene, a commercially available resistance gene for BSP. These findings highlight the need to forecast the emergence of such strains, identify genetic factors for innovative disease management, and understand how this pathogen evolves and spreads.

Analysis of Escherichia coli O157 strains in cattle and humans between Scotland and England & Wales: implications for human health

For the last two decades, the human infection frequency of Escherichia coli O157 (O157) in Scotland has been 2.5-fold higher than in England and Wales. Results from national cattle surveys conducted in Scotland and England and Wales in 2014/2015 were combined with data on reported human clinical cases from the same time frame to determine if strain differences in national populations of O157 in cattle could be associated with higher human infection rates in Scotland. Shiga toxin subtype (Stx) and phage type (PT) were examined within and between host (cattle vs human) and nation (Scotland vs England and Wales). For a subset of the strains, whole genome sequencing (WGS) provided further insights into geographical and host association. All three major O157 lineages (I, II, I/II) and most sub-lineages (Ia, Ib, Ic, IIa, IIb, IIc) were represented in cattle and humans in both nations. While the relative contribution of different reservoir hosts to human infection is unknown, WGS analysis indicated that the majority of O157 diversity in human cases was captured by isolates from cattle. Despite comparable cattle O157 prevalence between nations, strain types were localized. PT21/28 (sub-lineage Ic, Stx2a+) was significantly more prevalent in Scottish cattle [odds ratio (OR) 8.7 (2.3-33.7; P<0.001] and humans [OR 2.2 (1.5-3.2); P<0.001]. In England and Wales, cattle had a significantly higher association with sub-lineage IIa strains [PT54, Stx2c; OR 5.6 (1.27-33.3); P=0.011] while humans were significantly more closely associated with sub-lineage IIb [PT8, Stx1 and Stx2c; OR 29 (4.9-1161); P<0.001]. Therefore, cattle farms in Scotland were more likely to harbour Stx2a+O157 strains compared to farms in E and W (P<0.001). There was evidence of limited cattle strain migration between nations and clinical isolates from one nation were more similar to cattle isolates from the same nation, with sub-lineage Ic (mainly PT21/28) exhibiting clear national association and evidence of local transmission in Scotland. While we propose the higher rate of O157 clinical cases in Scotland, compared to England and Wales, is a consequence of the nationally higher level of Stx2a+O157 strains in Scottish cattle, we discuss the multiple additional factors that may also contribute to the different infection rates between these nations.

Repeated evolution of similar phenotypes: Integrating comparative methods with developmental pathways

Repeated phenotypes, often referred to as 'homoplasies' in cladistic analyses, may evolve through changes in developmental processes. Genetic bases of recurrent evolution gained attention and have been studied in the past years using approaches that combine modern analytical phylogenetic tools with the stunning assemblage of new information on developmental mechanisms. In this review, we evaluated the topic under an integrated perspective, revisiting the classical definitions of convergence and parallelism and detailing comparative methods used to evaluate evolution of repeated phenotypes, which include phylogenetic inference, estimates of evolutionary rates and reconstruction of ancestral states. We provide examples to illustrate how a given methodological approach can be used to identify evolutionary patterns and evaluate developmental mechanisms associated with the intermittent expression of a given trait along the phylogeny. Finally, we address why repeated trait loss challenges strict definitions of convergence and parallelism, discussing how changes in developmental pathways might explain the high frequency of repeated trait loss in specific lineages.

Distribution and origins of Mycobacterium tuberculosis L4 in Southeast Asia

Molecular and genomic studies have revealed that Mycobacterium tuberculosis Lineage 4 (L4, Euro-American lineage) emerged in Europe before becoming distributed around the globe by trade routes, colonial migration and other historical connections. Although L4 accounts for tens or hundreds of thousands of tuberculosis (TB) cases in multiple Southeast Asian countries, phylogeographical studies have either focused on a single country or just included Southeast Asia as part of a global analysis. Therefore, we interrogated public genomic data to investigate the historical patterns underlying the distribution of L4 in Southeast Asia and surrounding countries. We downloaded 6037 genomes associated with 29 published studies, focusing on global analyses of L4 and Asian studies of M. tuberculosis. We identified 2256 L4 genomes including 968 from Asia. We show that 81 % of L4 in Thailand, 51 % of L4 in Vietnam and 9 % of L4 in Indonesia belong to sub-lineages of L4 that are rarely seen outside East and Southeast Asia (L4.2.2, L4.4.2 and L4.5). These sub-lineages have spread between East and Southeast Asian countries, with no recent European ancestor. Although there is considerable uncertainty about the exact direction and order of intra-Asian M. tuberculosis dispersal, due to differing sampling frames between countries, our analysis suggests that China may be the intermediate location between Europe and Southeast Asia for two of the three predominantly East and Southeast Asian L4 sub-lineages (L4.2.2 and L4.5). This new perspective on L4 in Southeast Asia raises the possibility of investigating host population-specific evolution and highlights the need for more structured sampling from Southeast Asian countries to provide more certainty of the historical and current routes of dispersal.

Phylogenetic Analysis of Bacterial Pathogen Genomes

The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes.

A scalable analytical approach from bacterial genomes to epidemiology

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Listeria monocytogenes: Investigation of Fitness in Soil Does Not Support the Relevance of Ecotypes

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes the serious foodborne illness listeriosis. Although soil is a primary reservoir and a central habitat for Lm, little information is available on the genetic features underlying the fitness of Lm strains in this complex habitat. The aim of this study was to identify (i) correlations between the strains fitness in soil, their origin and their phylogenetic position (ii) identify genetic markers allowing Lm to survive in the soil. To this end, we assembled a balanced panel of 216 Lm strains isolated from three major ecological compartments (outdoor environment, animal hosts, and food) and from 33 clonal complexes occurring worldwide. The ability of the 216 strains to survive in soil was tested phenotypically. Hierarchical clustering identified three phenotypic groups according to the survival rate (SR): phenotype 1 “poor survivors” (SR &lt; 2%), phenotype 2 “moderate survivors” (2% &lt; SR &lt; 5%) and phenotype 3 “good survivors” (SR &gt; 5%). Survival in soil depended neither on strains’ origin nor on their phylogenetic position. Genome-wide-association studies demonstrated that a greater number of genes specifically associated with a good survival in soil was found in lineage II strains (57 genes) than in lineage I strains (28 genes). Soil fitness was mainly associated with variations in genes (i) coding membrane proteins, transcription regulators, and stress resistance genes in both lineages (ii) coding proteins related to motility and (iii) of the category “phage-related genes.” The cumulative effect of these small genomic variations resulted in significant increase of soil fitness.

Bayesian inference of clonal expansions in a dated phylogeny

Microbial population genetics models often assume that all lineages are constrained by the same population size dynamics over time. However, many neutral and selective events can invalidate this assumption and can contribute to the clonal expansion of a specific lineage relative to the rest of the population. Such differential phylodynamic properties between lineages result in asymmetries and imbalances in phylogenetic trees that are sometimes described informally but which are difficult to analyze formally. To this end, we developed a model of how clonal expansions occur and affect the branching patterns of a phylogeny. We show how the parameters of this model can be inferred from a given dated phylogeny using Bayesian statistics, which allows us to assess the probability that one or more clonal expansion events occurred. For each putative clonal expansion event, we estimate its date of emergence and subsequent phylodynamic trajectory, including its long-term evolutionary potential which is important to determine how much effort should be placed on specific control measures. We demonstrate the applicability of our methodology on simulated and real data sets. Inference under our clonal expansion model can reveal important features in the evolution and epidemiology of infectious disease pathogens. [Clonal expansion; genomic epidemiology; microbial population genomics; phylodynamics.]

Viral genome wide association study identifies novel hepatitis C virus polymorphisms associated with sofosbuvir treatment failure

Persistent hepatitis C virus (HCV) infection is a major cause of chronic liver disease, worldwide. With the development of direct-acting antivirals, treatment of chronically infected patients has become highly effective, although a subset of patients responds less well to therapy. Sofosbuvir is a common component of current de novo or salvage combination therapies, that targets the HCV NS5B polymerase. We use pre-treatment whole-genome sequences of HCV from 507 patients infected with HCV subtype 3a and treated with sofosbuvir containing regimens to detect viral polymorphisms associated with response to treatment. We find three common polymorphisms in non-targeted HCV NS2 and NS3 proteins are associated with reduced treatment response. These polymorphisms are enriched in post-treatment HCV sequences of patients unresponsive to treatment. They are also associated with lower reductions in viral load in the first week of therapy. Using in vitro short-term dose-response assays, these polymorphisms do not cause any reduction in sofosbuvir potency, suggesting an indirect mechanism of action in decreasing sofosbuvir efficacy. The identification of polymorphisms in NS2 and NS3 proteins associated with poor treatment outcomes emphasises the value of systematic genome-wide analyses of viruses in uncovering clinically relevant polymorphisms that impact treatment.

Machine learning to predict the source of campylobacteriosis using whole genome data

Campylobacteriosis is among the world's most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential.

Global prevalence and phylogeny of hepatitis B virus (HBV) drug and vaccine resistance mutations

Vaccination and anti-viral therapy with nucleos(t)ide analogues (NAs) are key approaches to reducing the morbidity, mortality and transmission of hepatitis B virus (HBV) infection. However, the efficacy of these interventions may be reduced by the emergence of drug resistance-associated mutations (RAMs) and/or vaccine escape mutations (VEMs). We have assimilated data on the global prevalence and distribution of HBV RAMs/VEMs from publicly available data and explored the evolution of these mutations. We analysed sequences downloaded from the HBV Database and calculated prevalence of 41 RAMs and 38 VEMs catalogued from published studies. We generated maximum likelihood phylogenetic trees and used treeBreaker to investigate the distribution and estimated the age of selected mutations across tree branches. RAM M204I/V had the highest prevalence, occurring in 3.8% (109/2838) of all HBV sequences in our data set, and a significantly higher rate in genotype C at 5.4% (60/1102, p = 0.0007). VEMs had an overall prevalence of 1.3% (37/2837) and had the highest prevalence in genotype C and in Asia at 2.2% (24/1102; p = 0.002) and 1.6% (34/2109; p = 0.009), respectively. Phylogenetic analysis suggested that RAM/VEMs can arise independently of treatment/vaccine exposure. In conclusion, HBV RAMs/VEMs have been found globally and across genotypes, with the highest prevalence observed in genotype C. Screening for genotype and for resistance-associated mutations may help to improve stratified patient treatment. As NAs and HBV vaccines are increasingly being deployed for HBV prevention and treatment, monitoring for resistance and advocating for better treatment regimens for HBV remains essential.

An Unbiased Molecular Approach Using 3'-UTRs Resolves the Avian Family-Level Tree of Life

Presumably, due to a rapid early diversification, major parts of the higher-level phylogeny of birds are still resolved controversially in different analyses or are considered unresolvable. To address this problem, we produced an avian tree of life, which includes molecular sequences of one or several species of ∼90% of the currently recognized family-level taxa (429 species, 379 genera) including all 106 family-level taxa of the nonpasserines and 115 of the passerines (Passeriformes). The unconstrained analyses of noncoding 3-prime untranslated region (3′-UTR) sequences and those of coding sequences yielded different trees. In contrast to the coding sequences, the 3′-UTR sequences resulted in a well-resolved and stable tree topology. The 3′-UTR contained, unexpectedly, transcription factor binding motifs that were specific for different higher-level taxa. In this tree, grebes and flamingos are the sister clade of all other Neoaves, which are subdivided into five major clades. All nonpasserine taxa were placed with robust statistical support including the long-time enigmatic hoatzin (Opisthocomiformes), which was found being the sister taxon of the Caprimulgiformes. The comparatively late radiation of family-level clades of the songbirds (oscine Passeriformes) contrasts with the attenuated diversification of nonpasseriform taxa since the early Miocene. This correlates with the evolution of vocal production learning, an important speciation factor, which is ancestral for songbirds and evolved convergent only in hummingbirds and parrots. As 3′-UTR-based phylotranscriptomics resolved the avian family-level tree of life, we suggest that this procedure will also resolve the all-species avian tree of life

Phylogenetic Methods for Genome-Wide Association Studies in Bacteria

Genome-wide association studies in bacteria have great potential to deliver a better understanding of the genetic basis of many biologically important phenotypes, including antibiotic resistance, pathogenicity, and host adaptation. Such studies need however to account for the specificities of bacterial genomics, especially in terms of population structure, homologous recombination, and genomic plasticity. A powerful way to tackle this challenge is to use a phylogenetic approach, which is based on long-standing methodology for the evolutionary analysis of bacterial genomic data. Here we present both the theoretical and practical aspects involved in the use of phylogenetic methods for bacterial genome-wide association studies.

Inferring the Allelic Series at QTL in Multiparental Populations

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

Testing for dependence on tree structures

Tree-like structures are abundant in the empirical sciences as they can summarize high-dimensional data and show latent structure among many samples in a single framework. Prominent examples include phylogenetic trees or hierarchical clustering derived from genetic data. Currently, users employ ad hoc methods to test for association between a given tree and a response variable, which reduces reproducibility and robustness. In this paper, we introduce treeSeg, a simple to use and widely applicable methodology with high power for testing between all levels of hierarchy for a given tree and the response while accounting for the overall false positive rate. Our method allows for precise uncertainty quantification and therefore, increases interpretability and reproducibility of such studies across many fields of science.

Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.

Interferon lambda 4 impacts the genetic diversity of hepatitis C virus

Hepatitis C virus (HCV) is a highly variable pathogen that frequently establishes chronic infection. This genetic variability is affected by the adaptive immune response but the contribution of other host factors is unclear. Here, we examined the role played by interferon lambda-4 (IFN-λ4) on HCV diversity; IFN-λ4 plays a crucial role in spontaneous clearance or establishment of chronicity following acute infection. We performed viral genome-wide association studies using human and viral data from 485 patients of white ancestry infected with HCV genotype 3a. We demonstrate that combinations of host genetic variants, which determine IFN-λ4 protein production and activity, influence amino acid variation across the viral polyprotein - not restricted to specific viral proteins or HLA restricted epitopes - and modulate viral load. We also observed an association with viral di-nucleotide proportions. These results support a direct role for IFN-λ4 in exerting selective pressure across the viral genome, possibly by a novel mechanism.

The impact of antimicrobials on gonococcal evolution

The sexually transmitted pathogen Neisseria gonorrhoeae is regarded as being on the way to becoming an untreatable superbug. Despite its clinical importance, little is known about its emergence and evolution, and how this corresponds with the introduction of antimicrobials. We present a genome-based phylogeographic analysis of 419 gonococcal isolates from across the globe. Results indicate that modern gonococci originated in Europe or Africa, possibly as late as the 16^th century and subsequently disseminated globally. We provide evidence that the modern gonococcal population has been shaped by antimicrobial treatment of sexually transmitted and other infections, leading to the emergence of two major lineages with different evolutionary strategies. The well-described multi-resistant lineage is associated with high rates of homologous recombination and infection in high-risk sexual networks. A second, multi-susceptible lineage is more associated with heterosexual networks, with potential implications for infection control.

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions

Intraspecific variability of the behavior of most foodborne pathogens is well described and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors (strain origin, serotype, …) explaining these differences are scarce or contradictory between studies. Nowadays, Whole Genome Sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens, based on various recently published bioinformatics tools. The objective of this study is to get a better insight into different existing bioinformatics approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food matrices and environments) and belonging to 17 clonal complexes (CC), were selected to represent large population diversity. Furthermore, the phenotypic variability of growth at low temperature was determined (i.e. qualitative phenotype), and the whole genomes of selected strains were sequenced. The almost exhaustive gene content, as well as the core genome SNPs based phylogenetic reconstruction, were derived from the whole sequenced genomes. A Bayesian inference method was applied to identify the branches on which the phenotype distribution evolves within sub-lineages. Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS) were independently performed in order to link genetic mutations to the phenotype of interest. The genomic analyses presented in this study were successfully applied on the selected dataset. The Bayesian phylogenetic approach emphasized an association with "slow" growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II. Moreover, both gene- and SNP-GWAS approaches displayed significant statistical associations with the tested phenotype. A list of 114 significantly associated genes, including genes already known to be involved in the cold adaption mechanism of L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were highlighted by SNP-GWAS, including SNPs detected in genes which were already likely involved in cold adaption; hypothetical proteins; and intergenic regions where for example promotors and regulators can be located. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes, could contribute to improve prediction of microbial behaviors in food. The implementation of this information in hazard identification and exposure assessment processes will open new possibilities to feed QMRA-models.

Diagnosing Emerging Fungal Threats: A One Health Perspective

Emerging fungal pathogens are a growing threat to global health, ecosystems, food security, and the world economy. Over the last century, environmental change and globalized transport, twinned with the increasing application of antifungal chemical drugs have led to increases in outbreaks of fungal diseases with sometimes catastrophic effects. In order to tackle contemporary epidemics and predemic threats, there is a pressing need for a unified approach in identification and monitoring of fungal pathogens. In this paper, we discuss current high throughput technologies, as well as new platforms capable of combining diverse data types to inform practical epidemiological strategies with a focus on emerging fungal pathogens of wildlife.

Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey

BACKGROUND

Traditional methods for molecular epidemiology of Neisseria gonorrhoeae are suboptimal. Whole-genome sequencing (WGS) offers ideal resolution to describe population dynamics and to predict and infer transmission of antimicrobial resistance, and can enhance infection control through linkage with epidemiological data. We used WGS, in conjunction with linked epidemiological and phenotypic data, to describe the gonococcal population in 20 European countries. We aimed to detail changes in phenotypic antimicrobial resistance levels (and the reasons for these changes) and strain distribution (with a focus on antimicrobial resistance strains in risk groups), and to predict antimicrobial resistance from WGS data.

METHODS

We carried out an observational study, in which we sequenced isolates taken from patients with gonorrhoea from the European Gonococcal Antimicrobial Surveillance Programme in 20 countries from September to November, 2013. We also developed a web platform that we used for automated antimicrobial resistance prediction, molecular typing (N gonorrhoeae multi-antigen sequence typing [NG-MAST] and multilocus sequence typing), and phylogenetic clustering in conjunction with epidemiological and phenotypic data.

FINDINGS

The multidrug-resistant NG-MAST genogroup G1407 was predominant and accounted for the most cephalosporin resistance, but the prevalence of this genogroup decreased from 248 (23%) of 1066 isolates in a previous study from 2009-10 to 174 (17%) of 1054 isolates in this survey in 2013. This genogroup previously showed an association with men who have sex with men, but changed to an association with heterosexual people (odds ratio=4·29). WGS provided substantially improved resolution and accuracy over NG-MAST and multilocus sequence typing, predicted antimicrobial resistance relatively well, and identified discrepant isolates, mixed infections or contaminants, and multidrug-resistant clades linked to risk groups.

INTERPRETATION

To our knowledge, we provide the first use of joint analysis of WGS and epidemiological data in an international programme for regional surveillance of sexually transmitted infections. WGS provided enhanced understanding of the distribution of antimicrobial resistance clones, including replacement with clones that were more susceptible to antimicrobials, in several risk groups nationally and regionally. We provide a framework for genomic surveillance of gonococci through standardised sampling, use of WGS, and a shared information architecture for interpretation and dissemination by use of open access software.

FUNDING

The European Centre for Disease Prevention and Control, The Centre for Genomic Pathogen Surveillance, Örebro University Hospital, and Wellcome.

A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.

Distinct Campylobacter fetus lineages adapted as livestock pathogens and human pathobionts in the intestinal microbiota

Campylobacter fetus is a venereal pathogen of cattle and sheep, and an opportunistic human pathogen. It is often assumed that C. fetus infection occurs in humans as a zoonosis through food chain transmission. Here we show that mammalian C. fetus consists of distinct evolutionary lineages, primarily associated with either human or bovine hosts. We use whole-genome phylogenetics on 182 strains from 17 countries to provide evidence that C. fetus may have originated in humans around 10,500 years ago and may have "jumped" into cattle during the livestock domestication period. We detect C. fetus genomes in 8% of healthy human fecal metagenomes, where the human-associated lineages are the dominant type (78%). Thus, our work suggests that C. fetus is an unappreciated human intestinal pathobiont likely spread by human to human transmission. This genome-based evolutionary framework will facilitate C. fetus epidemiology research and the development of improved molecular diagnostics and prevention schemes for this neglected pathogen.

A genome-wide association study identifies a horizontally transferred bacterial surface adhesin gene associated with antimicrobial resistant strains

Carbapenems are a class of last-resort antibiotics; thus, the increase in bacterial carbapenem-resistance is a serious public health threat. Acinetobacter baumannii is one of the microorganisms that can acquire carbapenem-resistance; it causes severe nosocomial infection, and is notoriously difficult to control in hospitals. Recently, a machine-learning approach was first used to analyze the genome sequences of hundreds of susceptible and resistant A. baumannii strains, including those carrying commonly acquired resistant mechanisms, to build a classifier that can predict strain resistance. A complementary approach is to explore novel genetic elements that could be associated with the antimicrobial resistance of strains, independent of known mechanisms. Therefore, we carefully selected A. baumannii strains, spanning various genotypes, from public genome databases, and conducted the first genome-wide association study (GWAS) of carbapenem resistance. We employed a recently developed method, capable of identifying any kind of genetic variation and accounting for bacterial population structure, and evaluated its effectiveness. Our study identified a surface adhesin gene that had been horizontally transferred to an ancestral branch of A. baumannii, as well as a specific region of that gene that appeared to accumulate multiple individual variations across the different branches of carbapenem-resistant A. baumannii strains.