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Background: The phenotypic stability of mixed connective tissue disease (MCTD) is not clear, and knowledge about disease activity and remission is scarce. We aimed to establish the occurrence of evolution from MCTD to another defined rheumatic condition, and the prevalence and durability of remission after long-term observation., Methods: In this large population-based prospective observational MCTD cohort study (N = 118), disease conversion was defined by the development of new auto-antibodies and clinical features compliant with another well-defined rheumatic condition. Remission was defined by a combination of systemic lupus erythematosus disease activity index 2000 (SLEDAI-2 K) of 0 and European League Against Rheumatism scleroderma trials and research (EUSTAR) activity index <2.5. Predictors of phenotypic stability and disease remission were assessed by logistic regression., Results: Among 118 patients, 14 (12%) developed another well-defined rheumatic condition other than MCTD after mean disease duration of 17 (SD 9) years. Puffy hands predicted a stable MCTD phenotype in univariable regression analysis (OR 7, CI 2-27, P = .010). Disease activity defined by SLEDAI-2 K, decreased gradually across the observation period and > 90% of patients had EUSTAR activity index <2.5. There were 13% patients in remission throughout the whole mean observation period of 7 (SD 2) years. The strongest predictor of remission was percentage of predicted higher forced vital capacity., Conclusions: Our results strengthen the view of MCTD as a relatively stable disease entity. Long-term remission in MCTD is not frequent; however, the low SLEDAI-2 K and EUSTAR scores during the observation period suggests that the disease runs a milder course than systemic lupus erythematosus and systemic sclerosis.

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.

Yersinia pseudotuberculosis is a Gram-negative intestinal pathogen of humans and has been responsible for several nationwide gastrointestinal outbreaks. Large-scale population genomic studies have been performed on the other human pathogenic species of the genus Yersinia, Yersinia pestis and Yersinia enterocolitica allowing a high-resolution understanding of the ecology, evolution and dissemination of these pathogens. However, to date no purpose-designed large-scale global population genomic analysis of Y. pseudotuberculosis has been performed. Here we present analyses of the genomes of 134 strains of Y. pseudotuberculosis isolated from around the world, from multiple ecosystems since the 1960s. Our data display a phylogeographic split within the population, with an Asian ancestry and subsequent dispersal of successful clonal lineages into Europe and the rest of the world. These lineages can be differentiated by CRISPR cluster arrays, and we show that the lineages are limited with respect to inter-lineage genetic exchange. This restriction of genetic exchange maintains the discrete lineage structure in the population despite co-existence of lineages for thousands of years in multiple countries. Our data highlights how CRISPR can be informative of the evolutionary trajectory of bacterial lineages, and merits further study across bacteria.

The genus Camelus is an interesting model to study adaptive evolution in the mitochondrial genome, as the three extant Old World camel species inhabit hot and low-altitude as well as cold and high-altitude deserts. We sequenced 24 camel mitogenomes and combined them with three previously published sequences to study the role of natural selection under different environmental pressure, and to advance our understanding of the evolutionary history of the genus Camelus. We confirmed the heterogeneity of divergence across different components of the electron transport system. Lineage-specific analysis of mitochondrial protein evolution revealed a significant effect of purifying selection in the concatenated protein-coding genes in domestic Bactrian camels. The estimated dN/dS < 1 in the concatenated protein-coding genes suggested purifying selection as driving force for shaping mitogenome diversity in camels. Additional analyses of the functional divergence in amino acid changes between species-specific lineages indicated fixed substitutions in various genes, with radical effects on the physicochemical properties of the protein products. The evolutionary time estimates revealed a divergence between domestic and wild Bactrian camels around 1.1 [0.58-1.8] million years ago (mya). This has major implications for the conservation and management of the critically endangered wild species, Camelus ferus.

Escherichia coli associated with urinary tract infections and bacteremia has been intensively investigated, including recent work focusing on the virulent, globally disseminated, multidrug-resistant lineage ST131. To contextualize ST131 within the broader E. coli population associated with disease, we used genomics to analyze a systematic 11-yr hospital-based survey of E. coli associated with bacteremia using isolates collected from across England by the British Society for Antimicrobial Chemotherapy and from the Cambridge University Hospitals NHS Foundation Trust. Population dynamics analysis of the most successful lineages identified the emergence of ST131 and ST69 and their establishment as two of the five most common lineages along with ST73, ST95, and ST12. The most frequently identified lineage was ST73. Compared to ST131, ST73 was susceptible to most antibiotics, indicating that multidrug resistance was not the dominant reason for prevalence of E. coli lineages in this population . Temporal phylogenetic analysis of the emergence of ST69 and ST131 identified differences in the dynamics of emergence and showed that expansion of ST131 in this population was not driven by sequential emergence of increasingly resistant subclades. We showed that over time, the E. coli population was only transiently disturbed by the introduction of new lineages before a new equilibrium was rapidly achieved. Together, these findings suggest that the frequency of E. coli lineages in invasive disease is driven by negative frequency-dependent selection occurring outside of the hospital, most probably in the commensal niche, and that drug resistance is not a primary determinant of success in this niche., (© 2017 Kallonen et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: Adaptation to local habitat conditions may lead to the natural divergence of populations in life-history traits such as body size, time of reproduction, mate signaling or dispersal capacity. Given enough time and strong enough selection pressures, populations may experience local genetic differentiation. The genetic basis of many life-history traits, and their evolution according to different environmental conditions remain however poorly understood., Methods: We conducted an association study on the Glanville fritillary butterfly, using material from five populations along a latitudinal gradient within the Baltic Sea region, which show different degrees of habitat fragmentation. We investigated variation in 10 principal components, cofounding in total 21 life-history traits, according to two environmental types, and 33 genetic SNP markers from 15 candidate genes., Results: We found that nine SNPs from five genes showed strong trend for trait associations ( p -values under 0.001 before correction). These associations, yet non-significant after multiple test corrections, with a total number of 1,086 tests, were consistent across the study populations. Additionally, these nine genes also showed an allele frequency difference between the populations from the northern fragmented versus the southern continuous landscape., Discussion: Our study provides further support for previously described trait associations within the Glanville fritillary butterfly species across different spatial scales. Although our results alone are inconclusive, they are concordant with previous studies that identified these associations to be related to climatic changes or habitat fragmentation within the Åland population., Competing Interests: The authors declare that they have no competing interests.

Prokaryotic evolution is affected by horizontal transfer of genetic material through recombination. Inference of an evolutionary tree of bacteria thus relies on accurate identification of the population genetic structure and recombination-derived mosaicism. Rapidly growing databases represent a challenge for computational methods to detect recombinations in bacterial genomes. We introduce a novel algorithm called fastGEAR which identifies lineages in diverse microbial alignments, and recombinations between them and from external origins. The algorithm detects both recent recombinations (affecting a few isolates) and ancestral recombinations between detected lineages (affecting entire lineages), thus providing insight into recombinations affecting deep branches of the phylogenetic tree. In simulations, fastGEAR had comparable power to detect recent recombinations and outstanding power to detect the ancestral ones, compared with state-of-the-art methods, often with a fraction of computational cost. We demonstrate the utility of the method by analyzing a collection of 616 whole-genomes of a recombinogenic pathogen Streptococcus pneumoniae, for which the method provided a high-resolution view of recombination across the genome. We examined in detail the penicillin-binding genes across the Streptococcus genus, demonstrating previously undetected genetic exchanges between different species at these three loci. Hence, fastGEAR can be readily applied to investigate mosaicism in bacterial genes across multiple species. Finally, fastGEAR correctly identified many known recombination hotspots and pointed to potential new ones. Matlab code and Linux/Windows executables are available at https://users.ics.aalto.fi/~pemartti/fastGEAR/ (last accessed February 6, 2017)., (© The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Staphylococcus aureus are globally disseminated among farmed chickens causing skeletal muscle infections, dermatitis, and septicaemia. The emergence of poultry-associated lineages has involved zoonotic transmission from humans to chickens but questions remain about the specific adaptations that promote proliferation of chicken pathogens. We characterized genetic variation in a population of genome-sequenced S. aureus isolates of poultry and human origin. Genealogical analysis identified a dominant poultry-associated sequence cluster within the CC5 clonal complex. Poultry and human CC5 isolates were significantly distinct from each other and more recombination events were detected in the poultry isolates. We identified 44 recombination events in 33 genes along the branch extending to the poultry-specific CC5 cluster, and 47 genes were found more often in CC5 poultry isolates compared with those from humans. Many of these gene sequences were common in chicken isolates from other clonal complexes suggesting horizontal gene transfer among poultry associated lineages. Consistent with functional predictions for putative poultry-associated genes, poultry isolates showed enhanced growth at 42 °C and greater erythrocyte lysis on chicken blood agar in comparison with human isolates. By combining phenotype information with evolutionary analyses of staphylococcal genomes, we provide evidence of adaptation, following a human-to-poultry host transition. This has important implications for the emergence and dissemination of new pathogenic clones associated with modern agriculture., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Conjugate vaccination against seven pneumococcal serotypes (PCV7) reduced disease prevalence due to antibiotic-resistant strains throughout the 2000s. However, diseases caused by resistant nonvaccine type (NVT) strains increased. Some of these emerging strains were derived from vaccine types (VT) that had changed their capsule by recombination. The introduction of a vaccine targeting 13 serotypes (PCV13) in 2010 has led to concern that this scenario will repeat itself. We generated high-quality draft genomes from 265 isolates of NVT pneumococci not susceptible to penicillin (PNSP) in 2009 and compared them with the genomes of 581 isolates from 2012 to 2013 collected by the Active Bacterial Core surveillance (ABCs) of the Centers for Disease Control and Prevention (CDC). Of the seven sequence clusters (SCs) identified, three SCs fell into a single lineage associated with serogroup 23, which had an origin in 1908 as dated by coalescent analysis and included isolates with a divergent 23B capsule locus. Three other SCs represented relatively deep-branching lineages associated with serotypes 35B, 15A, and 15BC. In all cases, the resistant clones originated prior to 2010, indicating that PNSP are at present dominated by descendants of NVT clones present before vaccination. With one exception (15BC/ST3280), these SCs were related to clones identified by the Pneumococcal Molecular Epidemiology Network (PMEN). We conclude that postvaccine diversity in NVT PNSP between 2009 and 2013 was driven mainly by the persistence of preexisting strains rather than through de novo adaptation, with few cases of serotype switching. Future surveillance is essential for documenting the long-term dynamics and resistance of NVT PNSP., (Copyright © 2017 American Society for Microbiology.)

Recent advances in the scale and diversity of population genomic datasets for bacteria now provide the potential for genome-wide patterns of co-evolution to be studied at the resolution of individual bases. Here we describe a new statistical method, genomeDCA, which uses recent advances in computational structural biology to identify the polymorphic loci under the strongest co-evolutionary pressures. We apply genomeDCA to two large population data sets representing the major human pathogens Streptococcus pneumoniae (pneumococcus) and Streptococcus pyogenes (group A Streptococcus). For pneumococcus we identified 5,199 putative epistatic interactions between 1,936 sites. Over three-quarters of the links were between sites within the pbp2x, pbp1a and pbp2b genes, the sequences of which are critical in determining non-susceptibility to beta-lactam antibiotics. A network-based analysis found these genes were also coupled to that encoding dihydrofolate reductase, changes to which underlie trimethoprim resistance. Distinct from these antibiotic resistance genes, a large network component of 384 protein coding sequences encompassed many genes critical in basic cellular functions, while another distinct component included genes associated with virulence. The group A Streptococcus (GAS) data set population represents a clonal population with relatively little genetic variation and a high level of linkage disequilibrium across the genome. Despite this, we were able to pinpoint two RNA pseudouridine synthases, which were each strongly linked to a separate set of loci across the chromosome, representing biologically plausible targets of co-selection. The population genomic analysis method applied here identifies statistically significantly co-evolving locus pairs, potentially arising from fitness selection interdependence reflecting underlying protein-protein interactions, or genes whose product activities contribute to the same phenotype. This discovery approach greatly enhances the future potential of epistasis analysis for systems biology, and can complement genome-wide association studies as a means of formulating hypotheses for targeted experimental work.

The environmental bacterium Burkholderia pseudomallei causes an estimated 165,000 cases of human melioidosis per year worldwide and is also classified as a biothreat agent. We used whole genome sequences of 469 B. pseudomallei isolates from 30 countries collected over 79 years to explore its geographic transmission. Our data point to Australia as an early reservoir, with transmission to Southeast Asia followed by onward transmission to South Asia and East Asia. Repeated reintroductions were observed within the Malay Peninsula and between countries bordered by the Mekong River. Our data support an African origin of the Central and South American isolates with introduction of B. pseudomallei into the Americas between 1650 and 1850, providing a temporal link with the slave trade. We also identified geographically distinct genes/variants in Australasian or Southeast Asian isolates alone, with virulence-associated genes being among those over-represented. This provides a potential explanation for clinical manifestations of melioidosis that are geographically restricted.

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.]

Background: Various ℓ 1 -penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation., Results: We explore the consistency of ℓ 1 -based methods for a class of bipartite graphs motivated by the structure of models commonly used for gene regulatory networks. We show that all ℓ 1 -based methods fail dramatically for models with nearly linear dependencies between the variables. We also study the consistency on models derived from real gene expression data and note that the assumptions needed for consistency never hold even for modest sized gene networks and ℓ 1 -based methods also become unreliable in practice for larger networks., Conclusions: Our results demonstrate that ℓ 1 -penalised undirected network structure learning methods are unable to reliably learn many sparse bipartite graph structures, which arise often in gene expression data. Users of such methods should be aware of the consistency criteria of the methods and check if they are likely to be met in their application of interest.

Background: Pneumococcus kills over one million children annually and over 90 % of these deaths occur in low-income countries especially in Sub-Saharan Africa (SSA) where HIV exacerbates the disease burden. In SSA, serotype 1 pneumococci particularly the endemic ST217 clone, causes majority of the pneumococcal disease burden. To understand the evolution of the virulent ST217 clone, we analysed ST217 whole genomes from isolates sampled from African and Asian countries., Methods: We analysed 226 whole genome sequences from the ST217 lineage sampled from 9 African and 4 Asian countries. We constructed a whole genome alignment and used it for phylogenetic and coalescent analyses. We also screened the genomes to determine presence of antibiotic resistance conferring genes., Results: Population structure analysis grouped the ST217 isolates into five sequence clusters (SCs), which were highly associated with different geographical regions and showed limited intracontinental and intercontinental spread. The SCs showed lower than expected genomic sequence, which suggested strong purifying selection and small population sizes caused by bottlenecks. Recombination rates varied between the SCs but were lower than in other successful clones such as PMEN1. African isolates showed higher prevalence of antibiotic resistance genes than Asian isolates. Interestingly, certain West African isolates harbored a defective chloramphenicol and tetracycline resistance-conferring element (Tn5253) with a deletion in the loci encoding the chloramphenicol resistance gene (cat pC194 ), which caused lower chloramphenicol than tetracycline resistance. Furthermore, certain genes that promote colonisation were absent in the isolates, which may contribute to serotype 1's rarity in carriage and consequently its lower recombination rates., Conclusions: The high phylogeographic diversity of the ST217 clone shows that this clone has been in circulation globally for a long time, which allowed its diversification and adaptation in different geographical regions. Such geographic adaptation reflects local variations in selection pressures in different locales. Further studies will be required to fully understand the biological mechanisms which makes the ST217 clone highly invasive but unable to successfully colonise the human nasopharynx for long durations which results in lower recombination rates.

The decreased costs of genome sequencing have increased the capability to apply whole-genome sequencing to epidemiological surveillance of zoonotic Campylobacter jejuni. However, knowledge of the genetic diversity of this bacteria is vital for inferring relatedness between epidemiologically linked isolates and a necessary prerequisite for correct application of this methodology. To address this issue in C. jejuni we investigated the spatial and temporal signals in the genomes of a major clonal complex and generalist lineage, ST-45 CC, by analysing the population structure and genealogy as well as applying genome-wide association analysis of 340 isolates from across Europe collected over a wide time range. The occurrence and strength of the geographical signal varied between sublineages and followed the clonal frame when present, while no evidence of a temporal signal was found. Certain sublineages of ST-45 formed discrete and genetically isolated clades containing isolates with extremely similar genomes regardless of time and location of sampling. Based on a separate data set, these monomorphic genotypes represent successful C. jejuni clones, possibly spread around the globe by rapid animal (migrating birds), food or human movement. In addition, we observed an incongruence between the genealogy of the strains and multilocus sequence typing (MLST), challenging the existing clonal complex definition and the use of whole-genome gene-by-gene hierarchical nomenclature schemes for C. jejuni .

Biolog Phenotype Microarray (PM) is a technology allowing simultaneous screening of the metabolic behaviour of bacteria under a large number of different conditions. Bacteria may often undergo several cycles of metabolic activity during a Biolog experiment. We introduce a novel algorithm to identify these metabolic cycles in PM experimental data, thus increasing the potential of PM technology in microbiology. Our method is based on a statistical decomposition of the time-series measurements into a set of growth models. We show that the method is robust to measurement noise and captures accurately the biologically relevant signals from the data. Our implementation is made freely available as a part of an R package for PM data analysis and can be found at www.helsinki.fi/bsg/software/Biolog&#95;Decomposition., Competing Interests: The authors have declared that no competing interests exist

Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes. We thus demonstrate that our method can answer important biologically and medically relevant questions.

The use of whole-genome phylogenetic analysis has revolutionized our understanding of the evolution and spread of many important bacterial pathogens due to the high resolution view it provides. However, the majority of such analyses do not consider the potential role of accessory genes when inferring evolutionary trajectories. Moreover, the recently discovered importance of the switching of gene regulatory elements suggests that an exhaustive analysis, combining information from core and accessory genes with regulatory elements could provide unparalleled detail of the evolution of a bacterial population. Here we demonstrate this principle by applying it to a worldwide multi-host sample of the important pathogenic E. coli lineage ST131. Our approach reveals the existence of multiple circulating subtypes of the major drug-resistant clade of ST131 and provides the first ever population level evidence of core genome substitutions in gene regulatory regions associated with the acquisition and maintenance of different accessory genome elements., Competing Interests: The authors have declared no competing interests exist.

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA&#95;Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Unlabelled: For 100 years, it has been obvious that Salmonella enterica strains sharing the serotype with the formula 1,4,[5],12:b:1,2-now known as Paratyphi B-can cause diseases ranging from serious systemic infections to self-limiting gastroenteritis. Despite considerable predicted diversity between strains carrying the common Paratyphi B serotype, there remain few methods that subdivide the group into groups that are congruent with their disease phenotypes. Paratyphi B therefore represents one of the canonical examples in Salmonella where serotyping combined with classical microbiological tests fails to provide clinically informative information. Here, we use genomics to provide the first high-resolution view of this serotype, placing it into a wider genomic context of the Salmonella enterica species. These analyses reveal why it has been impossible to subdivide this serotype based upon phenotypic and limited molecular approaches. By examining the genomic data in detail, we are able to identify common features that correlate with strains of clinical importance. The results presented here provide new diagnostic targets, as well as posing important new questions about the basis for the invasive disease phenotype observed in a subset of strains., Importance: Salmonella enterica strains carrying the serotype Paratyphi B have long been known to possess Jekyll and Hyde characteristics; some cause gastroenteritis, while others cause serious invasive disease. Understanding what makes up the population of strains carrying this serotype, as well as the source of their invasive disease, is a 100-year-old puzzle that we address here using genomics. Our analysis provides the first high-resolution view of this serotype, placing strains carrying serotype Paratyphi B into the wider genomic context of the Salmonella enterica species. This work reveals a history of disease dating back to the middle ages, caused by a group of distinct lineages with various abilities to cause invasive disease. By quantifying the key genomic differences between the invasive and noninvasive populations, we are able to identify key virulence-related targets that can form the basis of simple, rapid, point-of-care tests., (Copyright © 2016 Connor et al.)

Unlabelled: For over a century, a fundamental objective in infection biology research has been to understand the molecular processes contributing to the origin and perpetuation of epidemics. Divergent hypotheses have emerged concerning the extent to which environmental events or pathogen evolution dominates in these processes. Remarkably few studies bear on this important issue. Based on population pathogenomic analysis of 1,200 Streptococcus pyogenes type emm89 infection isolates, we report that a series of horizontal gene transfer events produced a new pathogenic genotype with increased ability to cause infection, leading to an epidemic wave of disease on at least two continents. In the aggregate, these and other genetic changes substantially remodeled the transcriptomes of the evolved progeny, causing extensive differential expression of virulence genes and altered pathogen-host interaction, including enhanced immune evasion. Our findings delineate the precise molecular genetic changes that occurred and enhance our understanding of the evolutionary processes that contribute to the emergence and persistence of epidemically successful pathogen clones. The data have significant implications for understanding bacterial epidemics and for translational research efforts to blunt their detrimental effects., Importance: The confluence of studies of molecular events underlying pathogen strain emergence, evolutionary genetic processes mediating altered virulence, and epidemics is in its infancy. Although understanding these events is necessary to develop new or improved strategies to protect health, surprisingly few studies have addressed this issue, in particular, at the comprehensive population genomic level. Herein we establish that substantial remodeling of the transcriptome of the human-specific pathogen Streptococcus pyogenes by horizontal gene flow and other evolutionary genetic changes is a central factor in precipitating and perpetuating epidemic disease. The data unambiguously show that the key outcome of these molecular events is evolution of a new, more virulent pathogenic genotype. Our findings provide new understanding of epidemic disease., (Copyright © 2016 Beres et al.)

Enterococci are Gram-positive bacteria that are found in plants, soil and as commensals of the gastrointestinal tract of humans, mammals, and insects. Despite their commensal nature, they have also become globally important nosocomial pathogens. Within the genus Enterococcus, Enterococcus faecium, and Enterococcus faecalis are clinically most relevant. In this review, we will discuss how E. faecium and E. faecalis have evolved to become a globally disseminated nosocomial pathogen. E. faecium has a defined sub-population that is associated with hospitalized patients and is rarely encountered in community settings. These hospital-associated clones are characterized by the acquisition of adaptive genetic elements, including genes involved in metabolism, biofilm formation, and antibiotic resistance. In contrast to E. faecium, clones of E. faecalis isolated from hospitalized patients, including strains causing clinical infections, are not exclusively found in hospitals but are also present in healthy individuals and animals. This observation suggests that the division between commensals and hospital-adapted lineages is less clear for E. faecalis than for E. faecium. In addition, genes that are reported to be associated with virulence of E. faecalis are often not unique to clinical isolates, but are also found in strains that originate from commensal niches. As a reflection of more ancient association of E. faecalis with different hosts, these determinants Thus, they may not represent genuine virulence genes but may act as host-adaptive functions that are useful in a variety of intestinal environments. The scope of the review is to summarize recent trends in the emergence of antibiotic resistance and explore recent developments in the molecular epidemiology, population structure and mechanisms of adaptation of E. faecium and E. faecalis.

Many key bacterial pathogens are frequently carried asymptomatically, and the emergence and spread of these opportunistic pathogens can be driven, or mitigated, via demographic changes within the host population. These inter-host transmission dynamics combine with basic evolutionary parameters such as rates of mutation and recombination, population size and selection, to shape the genetic diversity within bacterial populations. Whilst many studies have focused on how molecular processes underpin bacterial population structure, the impact of host migration and the connectivity of the local populations has received far less attention. A stochastic neutral model incorporating heightened local transmission has been previously shown to fit closely with genetic data for several bacterial species. However, this model did not incorporate transmission limiting population stratification, nor the possibility of migration of strains between subpopulations, which we address here by presenting an extended model. We study the consequences of migration in terms of shared genetic variation and show by simulation that the previously used summary statistic, the allelic mismatch distribution, can be insensitive to even large changes in microepidemic and migration rates. Using likelihood-free inference with genotype network topological summaries we fit a simpler model to commensal and hospital samples from the common nosocomial pathogens Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis and Enterococcus faecium. Only the hospital data for E. faecium display clearly marked deviations from the model predictions which may be attributable to its adaptation to the hospital environment., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Unlabelled: The implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasive Staphylococcus aureus isolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL (http://www.microreact.org/project/EkUvg9uY?tt=rc). Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show that in silico predictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) community-oriented database infrastructure and analysis tools., Importance: The spread of antibiotic-resistant bacteria is a public health emergency of global concern, threatening medical intervention at every level of health care delivery. Several recent studies have demonstrated the promise of routine whole-genome sequencing (WGS) of bacterial pathogens for epidemiological surveillance, outbreak detection, and infection control. However, as this technology becomes more widely adopted, the key challenges of generating representative national and international data sets and the development of bioinformatic tools to manage and interpret the data become increasingly pertinent. This study provides a road map for the integration of WGS data into routine pathogen surveillance. We emphasize the importance of large-scale routine surveys to provide the population context for more targeted or localized investigation and the development of open-access bioinformatic tools to provide the means to combine and compare independently generated data with publicly available data sets., (Copyright © 2016 Aanensen et al.)

Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models presents two challenges: (1) the likelihood function of the models is generally not computable, and computationally intensive simulation-based inference methods need to be employed, and (2) the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences that are driven more by prior assumptions than by the data themselves. We consider a popular and relatively simple yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, also presenting some methodological advances regarding the inference. Using likelihood approximations, we show that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size, which generally has been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision., (Copyright © 2016 by the Genetics Society of America.)

The single-humped dromedary (Camelus dromedarius) is the most numerous and widespread of domestic camel species and is a significant source of meat, milk, wool, transportation and sport for millions of people. Dromedaries are particularly well adapted to hot, desert conditions and harbour a variety of biological and physiological characteristics with evolutionary, economic and medical importance. To understand the genetic basis of these traits, an extensive resource of genomic variation is required. In this study, we assembled at 65× coverage, a 2.06 Gb draft genome of a female dromedary whose ancestry can be traced to an isolated population from the Canary Islands. We annotated 21,167 protein-coding genes and estimated ~33.7% of the genome to be repetitive. A comparison with the recently published draft genome of an Arabian dromedary resulted in 1.91 Gb of aligned sequence with a divergence of 0.095%. An evaluation of our genome with the reference revealed that our assembly contains more error-free bases (91.2%) and fewer scaffolding errors. We identified ~1.4 million single-nucleotide polymorphisms with a mean density of 0.71 × 10(-3) per base. An analysis of demographic history indicated that changes in effective population size corresponded with recent glacial epochs. Our de novo assembly provides a useful resource of genomic variation for future studies of the camel's adaptations to arid environments and economically important traits. Furthermore, these results suggest that draft genome assemblies constructed with only two differently sized sequencing libraries can be comparable to those sequenced using additional library sizes, highlighting that additional resources might be better placed in technologies alternative to short-read sequencing to physically anchor scaffolds to genome maps., (© 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.)

Enterococcus faecium, a common inhabitant of the human gut, has emerged in the last 2 decades as an important multidrug-resistant nosocomial pathogen. Since the start of the 21st century, multilocus sequence typing (MLST) has been used to study the molecular epidemiology of E. faecium. However, due to the use of a small number of genes, the resolution of MLST is limited. Whole-genome sequencing (WGS) now allows for high-resolution tracing of outbreaks, but current WGS-based approaches lack standardization, rendering them less suitable for interlaboratory prospective surveillance. To overcome this limitation, we developed a core genome MLST (cgMLST) scheme for E. faecium. cgMLST transfers genome-wide single nucleotide polymorphism(SNP) diversity into a standardized and portable allele numbering system that is far less computationally intensive than SNP-based analysis of WGS data. The E. faecium cgMLST scheme was built using 40 genome sequences that represented the diversity of the species. The scheme consists of 1,423 cgMLST target genes. To test the performance of the scheme, we performed WGS analysis of 103 outbreak isolates from five different hospitals in the Netherlands, Denmark, and Germany. The cgMLST scheme performed well in distinguishing between epidemiologically related and unrelated isolates, even between those that had the same sequence type (ST), which denotes the higher discriminatory power of this cgMLST scheme over that of conventional MLST. We also show that in terms of resolution, the performance of the E. faecium cgMLST scheme is equivalent to that of an SNP-based approach. In conclusion, the cgMLST scheme developed in this study facilitates rapid, standardized, and high-resolution tracing of E. faecium outbreaks.

Background: Population samples show bacterial genomes can be divided into a core of ubiquitous genes and accessory genes that are present in a fraction of isolates. The ecological significance of this variation in gene content remains unclear. However, microbiologists agree that a bacterial species should be 'genomically coherent', even though there is no consensus on how this should be determined., Results: We use a parsimonious model combining diversification in both the core and accessory genome, including mutation, homologous recombination (HR) and horizontal gene transfer (HGT) introducing new loci, to produce a population of interacting clusters of strains with varying genome content. New loci introduced by HGT may then be transferred on by HR. The model fits well to a systematic population sample of 616 pneumococcal genomes, capturing the major features of the population structure with parameter values that agree well with empirical estimates., Conclusions: The model does not include explicit selection on individual genes, suggesting that crude comparisons of gene content may be a poor predictor of ecological function. We identify a clearly divergent subpopulation of pneumococci that are inconsistent with the model and may be considered genomically incoherent with the rest of the population. These strains have a distinct disease tropism and may be rationally defined as a separate species. We also find deviations from the model that may be explained by recent population bottlenecks or spatial structure.

Large-scale recombination events have led to the emergence of epidemic clones of several major bacterial pathogens. However, the functional impact of the recombination on clonal success is not understood. Here, we identified a novel widespread hybrid clone (ST71) of livestock-associated Staphylococcus aureus that evolved from an ancestor belonging to the major bovine lineage CC97, through multiple large-scale recombination events with other S. aureus lineages occupying the same ruminant niche. The recombination events, affecting a 329 kb region of the chromosome spanning the origin of replication, resulted in allele replacement and loss or gain of an array of genes influencing host-pathogen interactions. Of note, molecular functional analyses revealed that the ST71 hybrid clone has acquired multiple novel pathogenic traits associated with acquired and innate immune evasion and bovine extracellular matrix adherence. These findings provide a paradigm for the impact of large-scale recombination events on the rapid evolution of bacterial pathogens within defined ecological niches.

Background: Campylobacter jejuni is a major zoonotic pathogen, causing gastroenteritis in humans. Invasion is an important pathogenesis trait by which C. jejuni causes disease. Here we report the genomic analysis of 134 strains to identify traits unique to hyperinvasive isolates., Methods: A total of 134 C. jejuni genomes were used to create a phylogenetic tree to position the hyperinvasive strains. Comparative genomics lead to the identification of mosaic capsule regions. A pan genome approach led to the discovery of unique loci, or loci with unique alleles, to the hyperinvasive strains., Results: Phylogenetic analysis showed that the hyper-invasive phenotype is a generalist trait. Despite the fact that hyperinvasive strains are only distantly related based on the whole genome phylogeny, they all possess genes within the capsule region with high identity to capsule genes from C. jejuni subsp. doylei and C. lari. In addition there were genes unique to the hyper-invasive strains with identity to non-C. jejuni genes, as well as allelic variants of mainly pathogenesis related genes already known in the other C. jejuni. In particular, the sequence of flagella genes, flgD-E and flgL were highly conserved amongst the hyper-invasive strains and divergent from sequences in other C. jejuni. A novel cytolethal distending toxin (cdt) operon was also identified as present in all hyper-invasive strains in addition to the classic cdt operon present in other C. jejuni., Conclusions: Overall, the hyper-invasive phenotype is strongly linked to the presence of orthologous genes from other Campylobacter species in their genomes, notably within the capsule region, in addition to the observed association with unique allelic variants in flagellar genes and the secondary cdt operon which is unlikely under random sharing of accessory alleles in separate lineages.

Motivation: Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging., Results: There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity., Availability: An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.

Yersinia enterocolitica is a common cause of food-borne gastroenteritis worldwide. Recent work defining the phylogeny of the genus Yersinia subdivided Y. enterocolitica into six distinct phylogroups. Here, we provide detailed analyses of the evolutionary processes leading to the emergence of these phylogroups. The dominant phylogroups isolated from human infections, PG3-5, show very little diversity at the sequence level, but do present marked patterns of gain and loss of functions, including those involved in pathogenicity and metabolism, including the acquisition of phylogroup-specific O-antigen loci. We tracked gene flow across the species in the core and accessory genome, and show that the non-pathogenic PG1 strains act as a reservoir for diversity, frequently acting as donors in recombination events. Analysis of the core and accessory genome also suggested that the different Y. enterocolitica phylogroups may be ecologically separated, in contrast to the long-held belief of common shared ecological niches across the Y. enterocolitica species.

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

Streptococcus pneumoniae is a significant human pathogen and a leading cause of infant mortality in developing countries. Considerable global variation in the pneumococcal carriage prevalence has been observed and the ecological factors contributing to it are not yet fully understood. We use data from a cohort of infants in Asia to study the effects of climatic conditions on both acquisition and clearance rates of the bacterium, finding significantly higher transmissibility during the cooler and drier months. Conversely, the length of a colonization period is unaffected by the season. Independent carriage data from studies conducted on the African and North American continents suggest similar effects of the climate on the prevalence of this bacterium, which further validates the obtained results. Further studies could be important to replicate the findings and explain the mechanistic role of cooler and dry air in the physiological response to nasopharyngeal acquisition of the pneumococcus.

Campylobacter jejuni is a highly diverse species of bacteria commonly associated with infectious intestinal disease of humans and zoonotic carriage in poultry, cattle, pigs, and other animals. The species contains a large number of distinct clonal complexes that vary from host generalist lineages commonly found in poultry, livestock, and human disease cases to host-adapted specialized lineages primarily associated with livestock or poultry. Here, we present novel data on the ST403 clonal complex of C. jejuni, a lineage that has not been reported in avian hosts. Our data show that the lineage exhibits a distinctive pattern of intralineage recombination that is accompanied by the presence of lineage-specific restriction-modification systems. Furthermore, we show that the ST403 complex has undergone gene decay at a number of loci. Our data provide a putative link between the lack of association with avian hosts of C. jejuni ST403 and both gene gain and gene loss through nonsense mutations in coding sequences of genes, resulting in pseudogene formation., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

The opportunistic pathogens Staphylococcus aureus and Staphylococcus epidermidis represent major causes of severe nosocomial infection, and are associated with high levels of mortality and morbidity worldwide. These species are both common commensals on the human skin and in the nasal pharynx, but are genetically distinct, differing at 24% average nucleotide divergence in 1,478 core genes. To better understand the genome dynamics of these ecologically similar staphylococcal species, we carried out a comparative analysis of 324 S. aureus and S. epidermidis genomes, including 83 novel S. epidermidis sequences. A reference pan-genome approach and whole genome multilocus-sequence typing revealed that around half of the genome was shared between the species. Based on a BratNextGen analysis, homologous recombination was found to have impacted on 40% of the core genes in S. epidermidis, but on only 24% of the core genes in S. aureus. Homologous recombination between the species is rare, with a maximum of nine gene alleles shared between any two S. epidermidis and S. aureus isolates. In contrast, there was considerable interspecies admixture of mobile elements, in particular genes associated with the SaPIn1 pathogenicity island, metal detoxification, and the methicillin-resistance island SCCmec. Our data and analysis provide a context for considering the nature of recombinational boundaries between S. aureus and S. epidermidis and, the selective forces that influence realized recombination between these species., (© The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Streptococcus suis causes disease in pigs worldwide and is increasingly implicated in zoonotic disease in East and South-East Asia. To understand the genetic basis of disease in S. suis, we study the genomes of 375 isolates with detailed clinical phenotypes from pigs and humans from the United Kingdom and Vietnam. Here, we show that isolates associated with disease contain substantially fewer genes than non-clinical isolates, but are more likely to encode virulence factors. Human disease isolates are limited to a single-virulent population, originating in the 1920, s when pig production was intensified, but no consistent genomic differences between pig and human isolates are observed. There is little geographical clustering of different S. suis subpopulations, and the bacterium undergoes high rates of recombination, implying that an increase in virulence anywhere in the world could have a global impact over a short timescale.

Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells' respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

Advantages of model-based clustering methods over heuristic alternatives have been widely demonstrated in the literature. Most model-based clustering algorithms assume that the data are either discrete or continuous, possibly allowing both types to be present in separate features. In this paper, we introduce a model-based approach for clustering feature vectors of mixed type, allowing each feature to simultaneously take on both categorical and real values. Such data may be encountered, for instance, in chemical and biological analyses, in the analysis of survey data, as well as in image analysis. Our model is formulated within a Bayesian predictive framework, where clustering solutions correspond to random partitions of the data. Using conjugate analysis, the posterior probability for each possible partition can be determined analytically, enabling the utilization of efficient computational search strategies for finding the posterior optimal partition. The derived model is illustrated using several synthetic and real datasets.

The diversity of enterococcal populations from fecal samples from hospitalized (n = 133) and nonhospitalized individuals (n = 173) of different age groups (group I, ages 0 to 19 years; group II, ages 20 to 59 years; group III, ages ≥60 years) was analyzed. Enterococci were recovered at similar rates from hospitalized and nonhospitalized persons (77.44% to 79.77%) of all age groups (75.0% to 82.61%). Enterococcus faecalis and Enterococcus faecium were predominant, although seven other Enterococcus species were identified. E. faecalis and E. faecium (including ampicillin-resistant E. faecium) colonization rates in nonhospitalized persons were age independent. For inpatients, E. faecalis colonization rates were age independent, but E. faecium colonization rates (particularly the rates of ampicillin-resistant E. faecium colonization) significantly increased with age. The population structure of E. faecium and E. faecalis was determined by superimposing goeBURST and Bayesian analysis of the population structure (BAPS). Most E. faecium sequence types (STs; 150 isolates belonging to 75 STs) were linked to BAPS groups 1 (22.0%), 2 (31.3%), and 3 (36.7%). A positive association between hospital isolates and BAPS subgroups 2.1a and 3.3a (which included major ampicillin-resistant E. faecium human lineages) and between community-based ampicillin-resistant E. faecium isolates and BAPS subgroups 1.2 and 3.3b was found. Most E. faecalis isolates (130 isolates belonging to 58 STs) were grouped into 3 BAPS groups, BAPS groups 1 (36.9%), 2 (40.0%), and 3 (23.1%), with each one comprising widespread lineages. No positive associations with age or hospitalization were established. The diversity and dynamics of enterococcal populations in the fecal microbiota of healthy humans are largely unexplored, with the available knowledge being fragmented and contradictory. The study offers a novel and comprehensive analysis of enterococcal population landscapes and suggests that E. faecium populations from hospitalized patients and from community-based individuals differ, with a predominance of certain clonal lineages, often in association with elderly individuals, occurring in the hospital setting., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

Many bacterial pathogens are specialized, infecting one or few hosts, and this is often associated with more acute disease presentation. Specific genomes show markers of this specialization, which often reflect a balance between gene acquisition and functional gene loss. Within Salmonella enterica subspecies enterica, a single lineage exists that includes human and animal pathogens adapted to cause infection in different hosts, including S. enterica serovar Enteritidis (multiple hosts), S. Gallinarum (birds), and S. Dublin (cattle). This provides an excellent evolutionary context in which differences between these pathogen genomes can be related to host range. Genome sequences were obtained from ∼ 60 isolates selected to represent the known diversity of this lineage. Examination and comparison of the clades within the phylogeny of this lineage revealed signs of host restriction as well as evolutionary events that mark a path to host generalism. We have identified the nature and order of events for both evolutionary trajectories. The impact of functional gene loss was predicted based upon position within metabolic pathways and confirmed with phenotyping assays. The structure of S. Enteritidis is more complex than previously known, as a second clade of S. Enteritidis was revealed that is distinct from those commonly seen to cause disease in humans or animals, and that is more closely related to S. Gallinarum. Isolates from this second clade were tested in a chick model of infection and exhibited a reduced colonization phenotype, which we postulate represents an intermediate stage in pathogen-host adaptation.

The Cystic Fibrosis (CF) lung harbors a complex, polymicrobial ecosystem, in which Pseudomonas aeruginosa is capable of sustaining chronic infections, which are highly resistant to multiple antibiotics. Here, we investigate the phenotypic and genotypic diversity of 44 morphologically identical P. aeruginosa isolates taken from a single CF patient sputum sample. Comprehensive phenotypic analysis of isolates revealed large variances and trade-offs in growth, virulence factors and quorum sensing (QS) signals. Whole genome analysis of 22 isolates revealed high levels of intra-isolate diversity ranging from 5 to 64 SNPs and that recombination and not spontaneous mutation was the dominant driver of diversity in this population. Furthermore, phenotypic differences between isolates were not linked to mutations in known genes but were statistically associated with distinct recombination events. We also assessed antibiotic susceptibility of all isolates. Resistance to antibiotics significantly increased when multiple isolates were mixed together. Our results highlight the significant role of recombination in generating phenotypic and genetic diversification during in vivo chronic CF infection. We also discuss (i) how these findings could influence how patient-to-patient transmission studies are performed using whole genome sequencing, and (ii) the need to refine antibiotic susceptibility testing in sputum samples taken from patients with CF.

We define two novel species of the genus Staphylococcus that are phenotypically similar to and have near identical 16S rRNA gene sequences to Staphylococcus aureus. However, compared to S. aureus and each other, the two species, Staphylococcus argenteus sp. nov. (type strain MSHR1132(T) = DSM 28299(T) = SSI 89.005(T)) and Staphylococcus schweitzeri sp. nov. (type strain FSA084(T) = DSM 28300(T) = SSI 89.004(T)), demonstrate: 1) at a whole-genome level considerable phylogenetic distance, lack of admixture, average nucleotide identity <95 %, and inferred DNA-DNA hybridization <70 %; 2) different profiles as determined by MALDI-TOF MS; 3) a non-pigmented phenotype for S. argenteus sp. nov.; 4) S. schweitzeri sp. nov. is not detected by standard nucA PCR; 5) distinct peptidoglycan types compared to S. aureus; 6) a separate ecological niche for S. schweitzeri sp. nov.; and 7) a distinct clinical disease profile for S. argenteus sp. nov. compared to S. aureus., (© 2015 IUMS.)

Background: Haemophilus parasuis is the etiologic agent of Glässer's disease in pigs and causes devastating losses to the farming industry. Whilst some hyper-virulent isolates have been described, the relationship between genetics and disease outcome has been only partially established. In particular, there is weak correlation between serovar and disease phenotype. We sequenced the genomes of 212 isolates of H. parasuis and have used this to describe the pan-genome and to correlate this with clinical and carrier status, as well as with serotype., Results: Recombination and population structure analyses identified five groups with very high rates of recombination, separated into two clades of H. parasuis with no signs of recombination between them. We used genome-wide association methods including discriminant analysis of principal components (DAPC) and generalised linear modelling (glm) to look for genetic determinants of this population partition, serovar and pathogenicity. We were able to identify genes from the accessory genome that were significantly associated with phenotypes such as potential serovar specific genes including capsule genes, and 48 putative virulence factors that were significantly different between the clinical and non-clinical isolates. We also show that the presence of many previously suggested virulence factors is not an appropriate marker of virulence., Conclusions: These genes will inform the generation of new molecular diagnostics and vaccines, and refinement of existing typing schemes and show the importance of the accessory genome of a diverse species when investigating the relationship between genotypes and phenotypes.