mlplasmids : a user-friendly tool to predict plasmid- and chromosome-derived sequences for single species

Assembly of bacterial short-read whole-genome sequencing data frequently results in hundreds of contigs for which the origin, plasmid or chromosome, is unclear. Complete genomes resolved by long-read sequencing can be used to generate and label short-read contigs. These were used to train several popular machine learning methods to classify the origin of contigs from Enterococcus faecium, Klebsiella pneumoniae and Escherichia colt using pentamer frequencies. We selected support-vector machine (SVM) models as the best classifier for all three bacterial species (Fl-score E. faecium=0.92, F1-score K. pneumoniae=0.90, F1-score E. coli=0.76), which outperformed other existing plasmid prediction tools using a benchmarking set of isolates. We demonstrated the scalability of our models by accurately predicting the plasmidome of a large collection of 1644 E. faecium isolates and illustrate its applicability by predicting the location of antibiotic-resistance genes in all three species. The SVM classifiers are publicly available as an R package and graphical-user interface called 'mlplasmids'. We anticipate that this tool may significantly facilitate research on the dissemination of plasmids encoding antibiotic resistance and/or contributing to host adaptation.Peer reviewe

gplas : a comprehensive tool for plasmid analysis using short-read graphs

aSummary: Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data.Peer reviewe

Mode and dynamics of vanA-type vancomycin resistance dissemination in Dutch hospitals

Abstract Background Enterococcus faecium is a commensal of the gastrointestinal tract of animals and humans but also a causative agent of hospital-acquired infections. Resistance against glycopeptides and to vancomycin has motivated the inclusion of E. faecium in the WHO global priority list. Vancomycin resistance can be conferred by the vanA gene cluster on the transposon Tn1546, which is frequently present in plasmids. The vanA gene cluster can be disseminated clonally but also horizontally either by plasmid dissemination or by Tn1546 transposition between different genomic locations. Methods We performed a retrospective study of the genomic epidemiology of 309 vancomycin-resistant E. faecium (VRE) isolates across 32 Dutch hospitals (2012–2015). Genomic information regarding clonality and Tn1546 characterization was extracted using hierBAPS sequence clusters (SC) and TETyper, respectively. Plasmids were predicted using gplas in combination with a network approach based on shared k-mer content. Next, we conducted a pairwise comparison between isolates sharing a potential epidemiological link to elucidate whether clonal, plasmid, or Tn1546 spread accounted for vanA-type resistance dissemination. Results On average, we estimated that 59% of VRE cases with a potential epidemiological link were unrelated which was defined as VRE pairs with a distinct Tn1546 variant. Clonal dissemination accounted for 32% cases in which the same SC and Tn1546 variants were identified. Horizontal plasmid dissemination accounted for 7% of VRE cases, in which we observed VRE pairs belonging to a distinct SC but carrying an identical plasmid and Tn1546 variant. In 2% of cases, we observed the same Tn1546 variant in distinct SC and plasmid types which could be explained by mixed and consecutive events of clonal and plasmid dissemination. Conclusions In related VRE cases, the dissemination of the vanA gene cluster in Dutch hospitals between 2012 and 2015 was dominated by clonal spread. However, we also identified outbreak settings with high frequencies of plasmid dissemination in which the spread of resistance was mainly driven by horizontal gene transfer (HGT). This study demonstrates the feasibility of distinguishing between modes of dissemination with short-read data and provides a novel assessment to estimate the relative contribution of nested genomic elements in the dissemination of vanA-type resistance

Challenges and opportunities for whole-genome sequencing–based surveillance of antibiotic resistance

Infections caused by drug-resistant bacteria are increasingly reported across the planet, and drug-resistant bacteria are recognized to be a major threat to public health and modern medicine. In this review, we discuss how whole-genome sequencing (WGS)–based approaches can contribute to the surveillance of the emergence and spread of antibiotic resistance. We outline the characteristics of sequencing technologies that are currently most used for WGS (Illumina short-read technologies and the long-read sequencing platforms developed by Pacific Biosciences and Oxford Nanopore). The challenges posed by the analysis of sequencing data sets for antimicrobial-resistance determinants and the solutions offered by modern bioinformatics tools are discussed. Finally, we illustrate the power of WGS-based surveillance of antimicrobial resistance by summarizing recent studies on the spread of the multidrug-resistant opportunistic pathogen Klebsiella pneumoniae and the transferable colistin-resistance gene mcr-1, in which high-throughput WGS analyses played essential roles. The implementation of WGS for surveillance of antibiotic-resistant bacteria is technically feasible and cost effective and provides actionable results with reference to infection control. Consequently, the time has come for laboratories to implement routine genome sequencing as part of their surveillance programs for antibiotic-resistant bacteria

Recovering escherichia coli plasmids in the absence of long-read sequencing data

The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools (MOB-suite, plasmidSPAdes, gplas, FishingForPlasmids, HyAsP and SCAPP) that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. We found that two thirds (n = 425, 66.3%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n = 85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n = 317, 50.23%), and performed best at reconstructing large plasmids (n = 166, 46.37%) and ARG-plasmids (n = 41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n = 168, 61.54%). Large ARG-plasmids, however, were frequently merged with sequences derived from distinct replicons. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data

Recovering full-length viral genomes from metagenomes

Infectious disease metagenomics is driven by the question: what is causing the disease? in contrast to classical metagenome studies which are guided by what is out there?. In case of a novel virus, a first step to eventually establishing etiology can be to recover a full-length viral genome from a metagenomic sample. However retrieval of a full-length genome of a divergent virus is technically challenging and can be time-consuming and costly. Here we discuss different assembly and fragment linkage strategies such as iterative assembly, motif searches, k-mer frequency profiling, coverage profile binning and other strategies used to recover genomes of potential viral pathogens in a timely and cost-effective manner

Draft genome sequence of haemophilus haemolyticus strain 16/010 o, isolated from a sputum sample from a cystic fibrosis patient

Haemophilus haemolyticus is considered a commensal of the respiratory tract that can cause opportunistic infections. It is closely related to Haemophilus influenzae Here, we report the genome sequence of H. haemolyticus 16/010 O, which was isolated from sputum from a cystic fibrosis patient