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Background The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. Results The Bacterial Isolate Genome Sequence Database (BIGS DB ) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGS DB are illustrated with the genera Neisseria and Streptococcus . The BIGS DB source code and documentation are available at http://pubmlst.org/software/database/bigsdb/ . Conclusions Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGS DB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera.  Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes.  Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications.  The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation.  Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes.  There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question.  In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

Background Following the association of Cronobacter spp. to several publicized fatal outbreaks in neonatal intensive care units of meningitis and necrotising enterocolitis, the World Health Organization (WHO) in 2004 requested the establishment of a molecular typing scheme to enable the international control of the organism. This paper presents the application of Next Generation Sequencing (NGS) to Cronobacter which has led to the establishment of the Cronobacter PubMLST genome and sequence definition database ( http://pubmlst.org/cronobacter/ ) containing over 1000 isolates with metadata along with the recognition of specific clonal lineages linked to neonatal meningitis and adult infections Results Whole genome sequencing and multilocus sequence typing (MLST) has supports the formal recognition of the genus Cronobacter composed of seven species to replace the former single species Enterobacter sakazakii . Applying the 7-loci MLST scheme to 1007 strains revealed 298 definable sequence types, yet only C. sakazakii clonal complex 4 (CC4) was principally associated with neonatal meningitis. This clonal lineage has been confirmed using ribosomal-MLST (51-loci) and whole genome-MLST (1865 loci) to analyse 107 whole genomes via the Cronobacter PubMLST database. This database has enabled the retrospective analysis of historic cases and outbreaks following re-identification of those strains. Conclusions The Cronobacter PubMLST database offers a central, open access, reliable sequence-based repository for researchers. It has the capacity to create new analysis schemes ‘on the fly’, and to integrate metadata (source, geographic distribution, clinical presentation). It is also expandable and adaptable to changes in taxonomy, and able to support the development of reliable detection methods of use to industry and regulatory authorities. Therefore it meets the WHO (2004) request for the establishment of a typing scheme for this emergent bacterial pathogen. Whole genome sequencing has additionally shown a range of potential virulence and environmental fitness traits which may account for the association of C. sakazakii CC4 pathogenicity, and propensity for neonatal CNS.

Mycobacterium bovis ( M . bovis) is a causative agent of bovine tuberculosis, a significant source of morbidity and mortality in the global cattle industry. The Randomised Badger Culling Trial was a field experiment carried out between 1998 and 2005 in the South West of England. As part of this trial, M . bovis isolates were collected from contemporaneous and overlapping populations of badgers and cattle within ten defined trial areas. We combined whole genome sequences from 1,442 isolates with location and cattle movement data, identifying transmission clusters and inferred rates and routes of transmission of M . bovis . Most trial areas contained a single transmission cluster that had been established shortly before sampling, often contemporaneous with the expansion of bovine tuberculosis in the 1980s. The estimated rate of transmission from badger to cattle was approximately two times higher than from cattle to badger, and the rate of within-species transmission considerably exceeded these for both species. We identified long distance transmission events linked to cattle movement, recurrence of herd breakdown by infection within the same transmission clusters and superspreader events driven by cattle but not badgers. Overall, our data suggests that the transmission clusters in different parts of South West England that are still evident today were established by long-distance seeding events involving cattle movement, not by recrudescence from a long-established wildlife reservoir. Clusters are maintained primarily by within-species transmission, with less frequent spill-over both from badger to cattle and cattle to badger.

Chickens are the most common birds on Earth and colibacillosis is among the most common diseases affecting them. This major threat to animal welfare and safe sustainable food production is difficult to combat because the etiological agent, avian pathogenic Escherichia coli (APEC), emerges from ubiquitous commensal gut bacteria, with no single virulence gene present in all disease-causing isolates. Here, we address the underlying evolutionary mechanisms of extraintestinal spread and systemic infection in poultry. Combining population scale comparative genomics and pangenome-wide association studies, we compare E. coli from commensal carriage and systemic infections. We identify phylogroup-specific and species-wide genetic elements that are enriched in APEC, including pathogenicity-associated variation in 143 genes that have diverse functions, including genes involved in metabolism, lipopolysaccharide synthesis, heat shock response, antimicrobial resistance and toxicity. We find that horizontal gene transfer spreads pathogenicity elements, allowing divergent clones to cause infection. Finally, a Random Forest model prediction of disease status (carriage vs. disease) identifies pathogenic strains in the emergent ST-117 poultry-associated lineage with 73% accuracy, demonstrating the potential for early identification of emergent APEC in healthy flocks. It is unclear how gut-dwelling E. coli bacteria often emerge to cause systemic infection in chickens. Here, Mageiros et al. use population genomics and pangenome-wide association studies to identify genetic elements associated with pathogenicity in avian E. coli .

The increasing availability of hundreds of whole bacterial genomes provides opportunities for enhanced understanding of the genes and alleles responsible for clinically important phenotypes and how they evolved. However, it is a significant challenge to develop easy-to-use and scalable methods for characterizing these large and complex data and relating it to disease epidemiology. Existing approaches typically focus on either homologous sequence variation in genes that are shared by all isolates, or non-homologous sequence variation--focusing on genes that are differentially present in the population. Here we present a comparative genomics approach that simultaneously approximates core and accessory genome variation in pathogen populations and apply it to pathogenic species in the genus Campylobacter. A total of 7 published Campylobacter jejuni and Campylobacter coli genomes were selected to represent diversity across these species, and a list of all loci that were present at least once was compiled. After filtering duplicates a 7-isolate reference pan-genome, of 3,933 loci, was defined. A core genome of 1,035 genes was ubiquitous in the sample accounting for 59% of the genes in each isolate (average genome size of 1.68 Mb). The accessory genome contained 2,792 genes. A Campylobacter population sample of 192 genomes was screened for the presence of reference pan-genome loci with gene presence defined as a BLAST match of ≥ 70% identity over ≥ 50% of the locus length--aligned using MUSCLE on a gene-by-gene basis. A total of 21 genes were present only in C. coli and 27 only in C. jejuni, providing information about functional differences associated with species and novel epidemiological markers for population genomic analyses. Homologs of these genes were found in several of the genomes used to define the pan-genome and, therefore, would not have been identified using a single reference strain approach.

The genus Bordetella includes bacteria that are found in the environment and/or associated with humans and other animals. A few closely related species, including Bordetella pertussis , are human pathogens that cause diseases such as whooping cough. Here, we present a large database of Bordetella isolates and genomes and develop genotyping systems for the genus and for the B. pertussis clade. To generate the database, we merge previously existing databases from Oxford University and Institut Pasteur, import genomes from public repositories, and add 83 newly sequenced B. bronchiseptica genomes. The public database currently includes 2582 Bordetella isolates and their provenance data, and 2085 genomes ( https://bigsdb.pasteur.fr/bordetella/ ). We use core-genome multilocus sequence typing (cgMLST) to develop genotyping systems for the whole genus and for B. pertussis , as well as specific schemes to define antigenic, virulence and macrolide resistance profiles. Phylogenetic analyses allow us to redefine evolutionary relationships among known Bordetella species, and to propose potential new species. Our database provides an expandable resource for genotyping of environmental and clinical Bordetella isolates, thus facilitating evolutionary and epidemiological research on whooping cough and other Bordetella infections. The genus Bordetella includes environmental bacteria as well as human pathogens. Here, the authors present a large database of environmental and clinical Bordetella isolates and genome sequences, and develop genotyping systems to facilitate evolutionary and epidemiological studies.

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415  S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively. Staphylococcus epidermidis is carried asymptomatically by virtually all humans but is also a major cause of nosocomial infection. Here, the authors study 141 isolates from healthy carriage and 274 isolates from clinical infections, and identify genes and genetic elements associated with pathogenicity.

Respiratory syncytial virus (RSV) is the leading cause of hospitalisation for respiratory infection in young children. RSV disease severity is known to be age-dependent and highest in young infants, but other correlates of severity, particularly the presence of additional respiratory pathogens, are less well understood. In this study, nasopharyngeal swabs were collected from two cohorts of RSV-positive infants <12 months in Spain, the UK, and the Netherlands during 2017–20. We show, using targeted metagenomic sequencing of >100 pathogens, including all common respiratory viruses and bacteria, from samples collected from 433 infants, that burden of additional viruses is common (111/433, 26%) but only modestly correlates with RSV disease severity. In contrast, there is strong evidence in both cohorts and across age groups that presence of Haemophilus bacteria (194/433, 45%) is associated with higher severity, including much higher rates of hospitalisation (odds ratio 4.25, 95% CI 2.03–9.31). There is no evidence for association between higher severity and other detected bacteria, and no difference in severity between RSV genotypes. Our findings reveal the genomic diversity of additional pathogens during RSV infection in infants, and provide an evidence base for future causal investigations of the impact of co-infection on RSV disease severity. The impact of other pathogens on disease outcome was studied in European infants with RSV infection. Additional viruses were commonly co-detected during infection but were weakly linked to severity. However, presence of Haemophilus bacteria strongly associated with severe cases.

Acinetobacter baumannii is a troublesome opportunistic pathogen with a high capacity for clonal dissemination. We announce the establishment of a database for the ampC locus in A. baumannii, in which novel ampC alleles are differentiated based on the occurrence of ≥ 1 nucleotide change, regardless of whether it is silent or missense. The database is openly accessible at the pubmlst platform for A. baumannii (http://pubmlst.org/abaumannii/). Forty-eight distinctive alleles of the ampC locus have so far been identified and deposited in the database. Isolates from clonal complex 1 (CC1), according to the Pasteur multilocus sequence typing scheme, had a variety of the ampC locus alleles, including alleles 1, 3, 4, 5, 6, 7, 8, 13, 14, 17, and 18. On the other hand, isolates from CC2 had the ampC alleles 2, 3, 19, 20, 21, 22, 23, 24, 26, 27, 28, and 46. Allele 3 was characteristic for sequence types ST3 or ST32. The ampC alleles 10, 16, and 25 were characteristic for CC10, ST16, and CC25, respectively. Our study points out that novel gene databases, in which alleles are numbered based on differences in their nucleotide identities, should replace traditional records that use amino acid substitutions to define new alleles.