In Silico Detection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence Typing
01 Jul 2014-Antimicrobial Agents and Chemotherapy (American Society for Microbiology)-Vol. 58, Iss: 7, pp 3895-3903
TL;DR: Two easy-to-use Web tools for in silico detection and characterization of whole-genome sequence (WGS) and whole-plasmid sequence data from members of the family Enterobacteriaceae are designed and developed.
Abstract: In the work presented here, we designed and developed two easy-to-use Web tools for in silico detection and characterization of whole-genome sequence (WGS) and whole-plasmid sequence data from members of the family Enterobacteriaceae. These tools will facilitate bacterial typing based on draft genomes of multidrug-resistant Enterobacteriaceae species by the rapid detection of known plasmid types. Replicon sequences from 559 fully sequenced plasmids associated with the family Enterobacteriaceae in the NCBI nucleotide database were collected to build a consensus database for integration into a Web tool called PlasmidFinder that can be used for replicon sequence analysis of raw, contig group, or completely assembled and closed plasmid sequencing data. The PlasmidFinder database currently consists of 116 replicon sequences that match with at least at 80% nucleotide identity all replicon sequences identified in the 559 fully sequenced plasmids. For plasmid multilocus sequence typing (pMLST) analysis, a database that is updated weekly was generated from www.pubmlst.org and integrated into a Web tool called pMLST. Both databases were evaluated using draft genomes from a collection of Salmonella enterica serovar Typhimurium isolates. PlasmidFinder identified a total of 103 replicons and between zero and five different plasmid replicons within each of 49 S . Typhimurium draft genomes tested. The pMLST Web tool was able to subtype genomic sequencing data of plasmids, revealing both known plasmid sequence types (STs) and new alleles and ST variants. In conclusion, testing of the two Web tools using both fully assembled plasmid sequences and WGS-generated draft genomes showed them to be able to detect a broad variety of plasmids that are often associated with antimicrobial resistance in clinically relevant bacterial pathogens.
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TL;DR: In this paper, phenotypic and genotypic patterns of antimicrobial resistance (AMR) in Klebsiella pneumoniae isolated from intramammary infections, clinical mastitis, fresh feces, rectal swabs, animal hindlimbs, and bulk tank milk samples from Brazilian dairy herds were investigated.
7 citations
TL;DR: This study established the largest chicken gut resistance gene catalogue to date through metagenomic analysis of 629 chicken gut samples and established a reference genome of gut antibiotic resistance genes in chickens, which will help to rationalize the use of drugs in poultry farming.
Abstract: The prevalence of antibiotic resistance genes in the chicken gut environment poses a serious threat to human health; however, we lack a comprehensive exploration of antibiotic resistomes and microbiomes in the chicken gut environment. The results of this study demonstrate the diversity and abundance of antibiotic resistance genes and flora in the chicken gut environment and identify a variety of potential hosts carrying antibiotic resistance genes. ABSTRACT The chicken gut microbiota, as a reservoir of antibiotic resistance genes (ARGs), poses a high risk to humans and animals worldwide. Yet a comprehensive exploration of the chicken gut antibiotic resistomes remains incomplete. In this study, we established the largest chicken gut resistance gene catalogue to date through metagenomic analysis of 629 chicken gut samples. We found significantly higher abundance of ARGs in the Chinese chicken gut than that in the Europe. tetX, mcr, and blaNDM, the genes resistant to antibiotics of last resort for human and animal health, were detected in the Chinese chicken gut. The abundance of ARGs was linearly correlated with that of mobile genetic elements (MGEs). The host-tracking analysis identified Escherichia, Enterococcus, Staphylococcus, Klebsiella, and Lactobacillus as the major ARG hosts. Especially, Lactobacillus, an intestinal probiotic, carried multiple drug resistance genes, and was proportional to ISLhe63, highlighting its potential risk in agricultural production processes. We first established a reference gene catalogue of chicken gut antibiotic resistomes. Our study helps to improve the knowledge and understanding of chicken antibiotic resistomes for knowledge-based sustainable chicken meat production. IMPORTANCE The prevalence of antibiotic resistance genes in the chicken gut environment poses a serious threat to human health; however, we lack a comprehensive exploration of antibiotic resistomes and microbiomes in the chicken gut environment. The results of this study demonstrate the diversity and abundance of antibiotic resistance genes and flora in the chicken gut environment and identify a variety of potential hosts carrying antibiotic resistance genes. Further analysis showed that mobile genetic elements were linearly correlated with antibiotic resistance genes abundance, implying that we should pay attention to the role played by mobile genetic elements in antibiotic resistance genes transmission. We established a reference genome of gut antibiotic resistance genes in chickens, which will help to rationalize the use of drugs in poultry farming.
7 citations
12 Mar 2018
TL;DR: PlasmidTron is presented, 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.
Abstract: 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.
7 citations
Cites methods from "In Silico Detection and Typing of P..."
...2017) with the PlasmidFinder database (Carattoli et al. 2014) (accessed 05-12-2017) to identify all of the Incompatibility (Inc) groups in each sample....
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...PlasmidFinder successfully identified one plasmid Inc group, IncFIIS, as present in 89.5% of all samples tested....
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...To provide a point of comparison, and determine what plasmids were present in the input dataset, all of the samples were compared to the PlasmidFinder (Carattoli et al. 2014) database (retrieved 2017-07-25) using Ariba (v2....
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...“In Silico Detection and Typing of Plasmids Using PlasmidFinder and Plasmid Multilocus Sequence Typing.”...
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...The K. pneumoniae dataset from Holt et al. (Holt et al. 2015) was analysed using ARIBA (version 2.10.3) (Hunt et al. 2017) with the PlasmidFinder database (Carattoli et al. 2014) (accessed 05-12-2017) to identify all of the Incompatibility (Inc) groups in each sample....
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TL;DR: The first draft genome sequence of an Enterococcus faecium sequence type 18 (ST18) strain isolated from a tuberculosis patient in Africa is reported.
Abstract: We report the first draft genome sequence of an Enterococcus faecium sequence type 18 (ST18) strain isolated from a tuberculosis patient in Africa. The genome is comprised of 3,202,539 bp, 501 contigs, 37.70% GC content, 3,202 protein-encoding genes, and 61 RNA genes. The resistome and virulome of this important pathogen are presented herein.
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TL;DR: In this article, a longitudinal study was performed by sampling individual pigs, pig farmers and the environment, and the ESC-R-Ec prevalence significantly decreased from 6.2% to 3.9% and 1.8% for the suckling, weaned and fattening pigs, respectively (P < 0.001).
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References
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TL;DR: A web server providing a convenient way of identifying acquired antimicrobial resistance genes in completely sequenced isolates was created, and the method was evaluated on WGS chromosomes and plasmids of 30 isolates.
Abstract: Objectives
Identification of antimicrobial resistance genes is important for understanding the underlying mechanisms and the epidemiology of antimicrobial resistance. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available in routine diagnostic laboratories and is anticipated to substitute traditional methods for resistance gene identification. Thus, the current challenge is to extract the relevant information from the large amount of generated data.
3,956 citations
"In Silico Detection and Typing of P..." refers methods in this paper
...To extract the relevant information from the large amount of data generated, a Web-based tool, ResFinder, for the identification of acquired or intrinsically present antimicrobial resistance genes in whole-genome data was recently developed (15)....
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TL;DR: NCBI’s Conserved Domain Database (CDD) is a resource for the annotation of protein sequences with the location of conserved domain footprints, and functional sites inferred from these footprints.
Abstract: NCBI's Conserved Domain Database (CDD) is a resource for the annotation of protein sequences with the location of conserved domain footprints, and functional sites inferred from these footprints. CDD includes manually curated domain models that make use of protein 3D structure to refine domain models and provide insights into sequence/structure/function relationships. Manually curated models are organized hierarchically if they describe domain families that are clearly related by common descent. As CDD also imports domain family models from a variety of external sources, it is a partially redundant collection. To simplify protein annotation, redundant models and models describing homologous families are clustered into superfamilies. By default, domain footprints are annotated with the corresponding superfamily designation, on top of which specific annotation may indicate high-confidence assignment of family membership. Pre-computed domain annotation is available for proteins in the Entrez/Protein dataset, and a novel interface, Batch CD-Search, allows the computation and download of annotation for large sets of protein queries. CDD can be accessed via http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml.
2,934 citations
"In Silico Detection and Typing of P..." refers background in this paper
...In particular, the replicase proteins showing the pfam02387 or pfam01051 conserved domains were assigned to the FII and FIB groups, respectively (31)....
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TL;DR: Results indicated that the inc/rep PCR method demonstrates high specificity and sensitivity in detecting replicons on reference plasmids and also revealed the presence of recurrent and common plasmid in epidemiologically unrelated Salmonella isolates of different serotypes.
2,163 citations
"In Silico Detection and Typing of P..." refers methods in this paper
...A collection of 24 previously characterized and fully FIG 1 Numbers of fully sequenced plasmids (y axis) classified into incompatibility groups occurring in the different bacterial species of the Enterobacteriaceae family....
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...Since 2005, a PCR-based replicon typing (PBRT) scheme has been available that targets in multiplex PCRs the replicons of the major plasmid families occurring in members of the family Enterobacteriaceae (2)....
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...Here, we present two free, easy-to-use Web tools, PlasmidFinder and pMLST, to analyze and classify plasmids from bacterial species of the family Enterobacteriaceae....
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...Here, we describe the design of two new easy-to-use Web tools useful for the rapid identification of plasmids in Enterobacteriaceae species that are of interest for epidemiological and clinical microbiology investigations of the plasmid-associated spread of antimicrobial resistance....
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...This method was initially developed to detect the replicons of plasmids belonging to the 18 major incompatibility (Inc) groups of Enterobacteriaceae species (3)....
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TL;DR: The Bacterial Isolate Genome Sequence Database (BIGSDB) 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.
Abstract: 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 The Bacterial Isolate Genome Sequence Database (BIGSDB) 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 BIGSDB are illustrated with the genera Neisseria and Streptococcus The BIGSDB source code and documentation are available at http://pubmlstorg/software/database/bigsdb/
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 BIGSDB 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
1,943 citations
TL;DR: A Web-based method for MLST of 66 bacterial species based on whole-genome sequencing data that enables investigators to determine the sequence types of their isolates on the basis of WGS data.
Abstract: Accurate strain identification is essential for anyone working with bacteria. For many species, multilocus sequence typing (MLST) is considered the “gold standard” of typing, but it is traditionally performed in an expensive and time-consuming manner. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available to scientists and routine diagnostic laboratories. Currently, the cost is below that of traditional MLST. The new challenges will be how to extract the relevant information from the large amount of data so as to allow for comparison over time and between laboratories. Ideally, this information should also allow for comparison to historical data. We developed a Web-based method for MLST of 66 bacterial species based on WGS data. As input, the method uses short sequence reads from four sequencing platforms or preassembled genomes. Updates from the MLST databases are downloaded monthly, and the best-matching MLST alleles of the specified MLST scheme are found using a BLAST-based ranking method. The sequence type is then determined by the combination of alleles identified. The method was tested on preassembled genomes from 336 isolates covering 56 MLST schemes, on short sequence reads from 387 isolates covering 10 schemes, and on a small test set of short sequence reads from 29 isolates for which the sequence type had been determined by traditional methods. The method presented here enables investigators to determine the sequence types of their isolates on the basis of WGS data. This method is publicly available at www.cbs.dtu.dk/services/MLST.
1,620 citations
"In Silico Detection and Typing of P..." refers methods in this paper
...If raw sequence reads are uploaded, they are first assembled (after the sequencing platform is given by the user) as described previously (16)....
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