The origins of haplotype 58 (H58) Salmonella enterica serovar Typhi.

Antimicrobial resistance (AMR) poses a serious threat to the clinical management of typhoid fever. AMR in Salmonella Typhi (S. Typhi) is commonly associated with the H58 lineage, a lineage that arose comparatively recently before becoming globally disseminated. To better understand when and how H58 emerged and became dominant, we performed detailed phylogenetic analyses on contemporary genome sequences from S. Typhi isolated in the period spanning the emergence. Our dataset, which contains the earliest described H58 S. Typhi organism, indicates that ancestral H58 organisms were already multi-drug resistant (MDR). These organisms emerged spontaneously in India in 1987 and became radially distributed throughout South Asia and then globally in the ensuing years. These early organisms were associated with a single long branch, possessing mutations associated with increased bile tolerance, suggesting that the first H58 organism was generated during chronic carriage. The subsequent use of fluoroquinolones led to several independent mutations in gyrA. The ability of H58 to acquire and maintain AMR genes continues to pose a threat, as extensively drug-resistant (XDR; MDR plus resistance to ciprofloxacin and third generation cephalosporins) variants, have emerged recently in this lineage. Understanding where and how H58 S. Typhi originated and became successful is key to understand how AMR drives successful lineages of bacterial pathogens. Additionally, these data can inform optimal targeting of typhoid conjugate vaccines (TCVs) for reducing the potential for emergence and the impact of new drug-resistant variants. Emphasis should also be placed upon the prospective identification and treatment of chronic carriers to prevent the emergence of new drug resistant variants with the ability to spread efficiently.

A retrospective investigation of the population structure and geospatial distribution of Salmonella Paratyphi A in Kathmandu, Nepal.

Salmonella Paratyphi A, one of the major etiologic agents of enteric fever, has increased in prevalence in recent decades in certain endemic regions in comparison to S. Typhi, the most prevalent cause of enteric fever. Despite this increase, data on the prevalence and molecular epidemiology of S. Paratyphi A remain generally scarce. Here, we analysed the whole genome sequences of 216 S. Paratyphi A isolates originating from Kathmandu, Nepal between 2005 and 2014, of which 200 were from patients with acute enteric fever and 16 from the gallbladder of people with suspected chronic carriage. By exploiting the recently developed genotyping framework for S. Paratyphi A (Paratype), we identified several genotypes circulating in Kathmandu. Notably, we observed an unusual clonal expansion of genotype 2.4.3 over a four-year period that spread geographically and systematically replaced other genotypes. This rapid genotype replacement is hypothesised to have been driven by both reduced susceptibility to fluoroquinolones and genetic changes to virulence factors, such as functional and structural genes encoding the type 3 secretion systems. Finally, we show that person-to-person is likely the most common mode of transmission and chronic carriers seem to play a limited role in maintaining disease circulation.

A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch.

As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones.

Rapid multi-locus sequence typing direct from uncorrected long reads using Krocus.

Genome sequencing is rapidly being adopted in reference labs and hospitals for bacterial outbreak investigation and diagnostics where time is critical. Seven gene multi-locus sequence typing is a standard tool for broadly classifying samples into sequence types (STs), allowing, in many cases, to rule a sample out of an outbreak, or allowing for general characteristics about a bacterial strain to be inferred. Long-read sequencing technologies, such as from Oxford Nanopore, can produce read data within minutes of an experiment starting, unlike short-read sequencing technologies which require many hours/days. However, the error rates of raw uncorrected long read data are very high. We present Krocus which can predict a ST directly from uncorrected long reads, and which was designed to consume read data as it is produced, providing results in minutes. It is the only tool which can do this from uncorrected long reads. We tested Krocus on over 700 isolates sequenced using long-read sequencing technologies from Pacific Biosciences and Oxford Nanopore. It provides STs for isolates on average within 90 s, with a sensitivity of 94% and specificity of 97% on real sample data, directly from uncorrected raw sequence reads. The software is written in Python and is available under the open source license GNU GPL version 3.

SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence data.

Streptococcus pneumoniae is responsible for 240 000-460 000 deaths in children under 5 years of age each year. Accurate identification of pneumococcal serotypes is important for tracking the distribution and evolution of serotypes following the introduction of effective vaccines. Recent efforts have been made to infer serotypes directly from genomic data but current software approaches are limited and do not scale well. Here, we introduce a novel method, SeroBA, which uses a k-mer approach. We compare SeroBA against real and simulated data and present results on the concordance and computational performance against a validation dataset, the robustness and scalability when analysing a large dataset, and the impact of varying the depth of coverage on sequence-based serotyping. SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98 % concordance using a k-mer-based method, can process 10 000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 15-21×. SeroBA is implemented in Python3 and is freely available under an open source GPLv3 licence from: https://github.com/sanger-pathogens/seroba.

PlasmidTron: assembling the cause of phenotypes and genotypes from NGS data.

Increasingly rich metadata are now being linked to samples that have been whole-genome sequenced. However, much of this information is ignored. This is because linking this metadata to genes, or regions of the genome, usually relies on knowing the gene sequence(s) responsible for the particular trait being measured and looking for its presence or absence in that genome. Examples of this would be the spread of antimicrobial resistance genes carried on mobile genetic elements (MGEs). However, although it is possible to routinely identify the resistance gene, identifying the unknown MGE upon which it is carried can be much more difficult if the starting point is short-read whole-genome sequence data. The reason for this is that MGEs are often full of repeats and so assemble poorly, leading to fragmented consensus sequences. Since mobile DNA, which can carry many clinically and ecologically important genes, has a different evolutionary history from the host, its distribution across the host population will, by definition, be independent of the host phylogeny. It is possible to use this phenomenon in a genome-wide association study to identify both the genes associated with the specific trait and also the DNA linked to that gene, for example the flanking sequence of the plasmid vector on which it is encoded, which follows the same patterns of distribution as the marker gene/sequence itself. We present PlasmidTron, which utilizes the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographical information, to identify traits that are likely to be present on DNA that can randomly reassort across defined bacterial populations. It is also possible to use this methodology to associate unknown genes/sequences (e.g. plasmid backbones) with a specific molecular signature or marker (e.g. resistance gene presence or absence) using PlasmidTron. PlasmidTron uses a k-mer-based approach to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs in a fast and scalable-to-large manner. PlasmidTron is written in Python 3 and is available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads.

Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods.

Comparison of classical multi-locus sequence typing software for next-generation sequencing data.

Multi-locus sequence typing (MLST) is a widely used method for categorizing bacteria. Increasingly, MLST is being performed using next-generation sequencing (NGS) data by reference laboratories and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared eight of these applications against real and simulated data, and present results on: (1) the accuracy of each method against traditional typing methods, (2) the performance on real outbreak datasets, (3) the impact of contamination and varying depth of coverage, and (4) the computational resource requirements.

Phylogenetic Analysis of Klebsiella pneumoniae from Hospitalized Children, Pakistan.

Klebsiella pneumoniae shows increasing emergence of multidrug-resistant lineages, including strains resistant to all available antimicrobial drugs. We conducted whole-genome sequencing of 178 highly drug-resistant isolates from a tertiary hospital in Lahore, Pakistan. Phylogenetic analyses to place these isolates into global context demonstrate the expansion of multiple independent lineages, including K. quasipneumoniae.

The TraDIS toolkit: sequencing and analysis for dense transposon mutant libraries.

UNLABELLED: Transposon insertion sequencing is a high-throughput technique for assaying large libraries of otherwise isogenic transposon mutants providing insight into gene essentiality, gene function and genetic interactions. We previously developed the Transposon Directed Insertion Sequencing (TraDIS) protocol for this purpose, which utilizes shearing of genomic DNA followed by specific PCR amplification of transposon-containing fragments and Illumina sequencing. Here we describe an optimized high-yield library preparation and sequencing protocol for TraDIS experiments and a novel software pipeline for analysis of the resulting data. The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools. This article can serve as a general reference for the application of the TraDIS methodology. AVAILABILITY AND IMPLEMENTATION: The optimized sequencing protocol is included as supplementary information. The Bio-Tradis analysis pipeline is available under a GPL license at https://github.com/sanger-pathogens/Bio-Tradis CONTACT: parkhill@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments.

Rapidly decreasing genome sequencing costs have led to a proportionate increase in the number of samples used in prokaryotic population studies. Extracting single nucleotide polymorphisms (SNPs) from a large whole genome alignment is now a routine task, but existing tools have failed to scale efficiently with the increased size of studies. These tools are slow, memory inefficient and are installed through non-standard procedures. We present SNP-sites which can rapidly extract SNPs from a multi-FASTA alignment using modest resources and can output results in multiple formats for downstream analysis. SNPs can be extracted from a 8.3 GB alignment file (1842 taxa, 22 618 sites) in 267 seconds using 59 MB of RAM and 1 CPU core, making it feasible to run on modest computers. It is easy to install through the Debian and Homebrew package managers, and has been successfully tested on more than 20 operating systems. SNP-sites is implemented in C and is available under the open source license GNU GPL version 3.

Robust high-throughput prokaryote de novo assembly and improvement pipeline for Illumina data.

The rapidly reducing cost of bacterial genome sequencing has lead to its routine use in large-scale microbial analysis. Though mapping approaches can be used to find differences relative to the reference, many bacteria are subject to constant evolutionary pressures resulting in events such as the loss and gain of mobile genetic elements, horizontal gene transfer through recombination and genomic rearrangements. De novo assembly is the reconstruction of the underlying genome sequence, an essential step to understanding bacterial genome diversity. Here we present a high-throughput bacterial assembly and improvement pipeline that has been used to generate nearly 20 000 annotated draft genome assemblies in public databases. We demonstrate its performance on a public data set of 9404 genomes. We find all the genes used in multi-locus sequence typing schema present in 99.6 % of assembled genomes. When tested on low-, neutral- and high-GC organisms, more than 94 % of genes were present and completely intact. The pipeline has been proven to be scalable and robust with a wide variety of datasets without requiring human intervention. All of the software is available on GitHub under the GNU GPL open source license.

Circlator: automated circularization of genome assemblies using long sequencing reads.

The assembly of DNA sequence data is undergoing a renaissance thanks to emerging technologies capable of producing reads tens of kilobases long. Assembling complete bacterial and small eukaryotic genomes is now possible, but the final step of circularizing sequences remains unsolved. Here we present Circlator, the first tool to automate assembly circularization and produce accurate linear representations of circular sequences. Using Pacific Biosciences and Oxford Nanopore data, Circlator correctly circularized 26 of 27 circularizable sequences, comprising 11 chromosomes and 12 plasmids from bacteria, the apicoplast and mitochondrion of Plasmodium falciparum and a human mitochondrion. Circlator is available at http://sanger-pathogens.github.io/circlator/ .

Roary: rapid large-scale prokaryote pan genome analysis.

UNLABELLED: A typical prokaryote population sequencing study can now consist of hundreds or thousands of isolates. Interrogating these datasets can provide detailed insights into the genetic structure of prokaryotic genomes. We introduce Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and accessory genes. Roary makes construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results. Using a single CPU Roary can produce a pan genome consisting of 1000 isolates in 4.5 hours using 13 GB of RAM, with further speedups possible using multiple processors. AVAILABILITY AND IMPLEMENTATION: Roary is implemented in Perl and is freely available under an open source GPLv3 license from http://sanger-pathogens.github.io/Roary CONTACT: roary@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella Typhi identifies inter- and intracontinental transmission events.

The emergence of multidrug-resistant (MDR) typhoid is a major global health threat affecting many countries where the disease is endemic. Here whole-genome sequence analysis of 1,832 Salmonella enterica serovar Typhi (S. Typhi) identifies a single dominant MDR lineage, H58, that has emerged and spread throughout Asia and Africa over the last 30 years. Our analysis identifies numerous transmissions of H58, including multiple transfers from Asia to Africa and an ongoing, unrecognized MDR epidemic within Africa itself. Notably, our analysis indicates that H58 lineages are displacing antibiotic-sensitive isolates, transforming the global population structure of this pathogen. H58 isolates can harbor a complex MDR element residing either on transmissible IncHI1 plasmids or within multiple chromosomal integration sites. We also identify new mutations that define the H58 lineage. This phylogeographical analysis provides a framework to facilitate global management of MDR typhoid and is applicable to similar MDR lineages emerging in other bacterial species.

IVA: accurate de novo assembly of RNA virus genomes.

MOTIVATION: An accurate genome assembly from short read sequencing data is critical for downstream analysis, for example allowing investigation of variants within a sequenced population. However, assembling sequencing data from virus samples, especially RNA viruses, into a genome sequence is challenging due to the combination of viral population diversity and extremely uneven read depth caused by amplification bias in the inevitable reverse transcription and polymerase chain reaction amplification process of current methods. RESULTS: We developed a new de novo assembler called IVA (Iterative Virus Assembler) designed specifically for read pairs sequenced at highly variable depth from RNA virus samples. We tested IVA on datasets from 140 sequenced samples from human immunodeficiency virus-1 or influenza-virus-infected people and demonstrated that IVA outperforms all other virus de novo assemblers. AVAILABILITY AND IMPLEMENTATION: The software runs under Linux, has the GPLv3 licence and is freely available from http://sanger-pathogens.github.io/iva

Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins.

The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates' recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins, implemented in Python and C and supported on Linux and Mac OS X.

Epithelial IL-22RA1-mediated fucosylation promotes intestinal colonization resistance to an opportunistic pathogen.

Our intestinal microbiota harbors a diverse microbial community, often containing opportunistic bacteria with virulence potential. However, mutualistic host-microbial interactions prevent disease by opportunistic pathogens through poorly understood mechanisms. We show that the epithelial interleukin-22 receptor IL-22RA1 protects against lethal Citrobacter rodentium infection and chemical-induced colitis by promoting colonization resistance against an intestinal opportunistic bacterium, Enterococcus faecalis. Susceptibility of Il22ra1(-/-) mice to C. rodentium was associated with preferential expansion and epithelial translocation of pathogenic E. faecalis during severe microbial dysbiosis and was ameloriated with antibiotics active against E. faecalis. RNA sequencing analyses of primary colonic organoids showed that IL-22RA1 signaling promotes intestinal fucosylation via induction of the fucosyltransferase Fut2. Additionally, administration of fucosylated oligosaccharides to C. rodentium-challenged Il22ra1(-/-) mice attenuated infection and promoted E. faecalis colonization resistance by restoring the diversity of anaerobic commensal symbionts. These results support a model whereby IL-22RA1 enhances host-microbiota mutualism to limit detrimental overcolonization by opportunistic pathogens.

The genomes of four tapeworm species reveal adaptations to parasitism.

Tapeworms (Cestoda) cause neglected diseases that can be fatal and are difficult to treat, owing to inefficient drugs. Here we present an analysis of tapeworm genome sequences using the human-infective species Echinococcus multilocularis, E. granulosus, Taenia solium and the laboratory model Hymenolepis microstoma as examples. The 115- to 141-megabase genomes offer insights into the evolution of parasitism. Synteny is maintained with distantly related blood flukes but we find extreme losses of genes and pathways that are ubiquitous in other animals, including 34 homeobox families and several determinants of stem cell fate. Tapeworms have specialized detoxification pathways, metabolism that is finely tuned to rely on nutrients scavenged from their hosts, and species-specific expansions of non-canonical heat shock proteins and families of known antigens. We identify new potential drug targets, including some on which existing pharmaceuticals may act. The genomes provide a rich resource to underpin the development of urgently needed treatments and control.