# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 3771 | 0 | 1.0000 | RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning. Antimicrobial-resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know whether the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole-genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like k-mer composition, circularity of the DNA molecule, copy number or sequence identity to plasmid replication genes, all of which have their drawbacks, especially when faced with large single-copy plasmids, which often carry resistance genes. With our newly developed prediction tool RFPlasmid, we use a combination of multiple features, including k-mer composition and databases with plasmid and chromosomal marker proteins, to predict whether the likely source of a contig is plasmid or chromosomal. The tool RFPlasmid supports models for 17 different bacterial taxa, including Campylobacter, Escherichia coli and Salmonella, and has a taxon agnostic model for metagenomic assemblies or unsupported organisms. RFPlasmid is available both as a standalone tool and via a web interface. | 2021 | 34846288 |
| 9880 | 1 | 0.9996 | Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology. Plasmids are extra-chromosomal genetic elements ubiquitous in bacteria, and commonly transmissible between host cells. Their genomes include variable repertoires of 'accessory genes,' such as antibiotic resistance genes, as well as 'backbone' loci which are largely conserved within plasmid families, and often involved in key plasmid-specific functions (e.g., replication, stable inheritance, mobility). Classifying plasmids into different types according to their phylogenetic relatedness provides insight into the epidemiology of plasmid-mediated antibiotic resistance. Current typing schemes exploit backbone loci associated with replication (replicon typing), or plasmid mobility (MOB typing). Conventional PCR-based methods for plasmid typing remain widely used. With the emergence of whole-genome sequencing (WGS), large datasets can be analyzed using in silico plasmid typing methods. However, short reads from popular high-throughput sequencers can be challenging to assemble, so complete plasmid sequences may not be accurately reconstructed. Therefore, localizing resistance genes to specific plasmids may be difficult, limiting epidemiological insight. Long-read sequencing will become increasingly popular as costs decline, especially when resolving accurate plasmid structures is the primary goal. This review discusses the application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology; novel in silico plasmid analysis tools are highlighted. Due to the diverse and plastic nature of plasmid genomes, current typing schemes do not classify all plasmids, and identifying conserved, phylogenetically concordant genes for subtyping and phylogenetics is challenging. Analyzing plasmids as nodes in a network that represents gene-sharing relationships between plasmids provides a complementary way to assess plasmid diversity, and allows inferences about horizontal gene transfer to be made. | 2017 | 28232822 |
| 9879 | 2 | 0.9996 | IntegronFinder 2.0: Identification and Analysis of Integrons across Bacteria, with a Focus on Antibiotic Resistance in Klebsiella. Integrons are flexible gene-exchanging platforms that contain multiple cassettes encoding accessory genes whose order is shuffled by a specific integrase. Integrons embedded within mobile genetic elements often contain multiple antibiotic resistance genes that they spread among nosocomial pathogens and contribute to the current antibiotic resistance crisis. However, most integrons are presumably sedentary and encode a much broader diversity of functions. IntegronFinder is a widely used software to identify novel integrons in bacterial genomes, but has aged and lacks some useful functionalities to handle very large datasets of draft genomes or metagenomes. Here, we present IntegronFinder version 2. We have updated the code, improved its efficiency and usability, adapted the output to incomplete genome data, and added a few novel functions. We describe these changes and illustrate the relevance of the program by analyzing the distribution of integrons across more than 20,000 fully sequenced genomes. We also take full advantage of its novel capabilities to analyze close to 4000 Klebsiella pneumoniae genomes for the presence of integrons and antibiotic resistance genes within them. Our data show that K. pneumoniae has a large diversity of integrons and the largest mobile integron in our database of plasmids. The pangenome of these integrons contains a total of 165 different gene families with most of the largest families being related with resistance to numerous types of antibiotics. IntegronFinder is a free and open-source software available on multiple public platforms. | 2022 | 35456751 |
| 5111 | 3 | 0.9996 | Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp. | 2019 | 31597945 |
| 4455 | 4 | 0.9995 | A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences. BACKGROUND: Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. RESULTS: In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. CONCLUSIONS: The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/. | 2012 | 23231464 |
| 9849 | 5 | 0.9995 | Analysis of antibiotic resistance regions in Gram-negative bacteria. Antibiotic resistance in Gram-negative bacteria is often due to the acquisition of resistance genes from a shared pool. In multiresistant isolates these genes, together with associated mobile elements, may be found in complex conglomerations on plasmids or on the chromosome. Analysis of available sequences reveals that these multiresistance regions (MRR) are modular, mosaic structures composed of different combinations of components from a limited set arranged in a limited number of ways. Components common to different MRR provide targets for homologous recombination, allowing these regions to evolve by combinatorial evolution, but our understanding of this process is far from complete. Advances in technology are leading to increasing amounts of sequence data, but currently available automated annotation methods usually focus on identifying ORFs and predicting protein function by homology. In MRR, where the genes are often well characterized, the challenge is to identify precisely which genes are present and to define the boundaries of complete and fragmented mobile elements. This review aims to summarize the types of mobile elements involved in multiresistance in Gram-negative bacteria and their associations with particular resistance genes, to describe common components of MRR and to illustrate methods for detailed analysis of these regions. | 2011 | 21564142 |
| 4345 | 6 | 0.9995 | Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. Traditional genetic association studies are very difficult in bacteria, as the generally limited recombination leads to large linked haplotype blocks, confounding the identification of causative variants. Beta-lactam antibiotic resistance in Streptococcus pneumoniae arises readily as the bacteria can quickly incorporate DNA fragments encompassing variants that make the transformed strains resistant. However, the causative mutations themselves are embedded within larger recombined blocks, and previous studies have only analysed a limited number of isolates, leading to the description of "mosaic genes" as being responsible for resistance. By comparing a large number of genomes of beta-lactam susceptible and non-susceptible strains, the high frequency of recombination should break up these haplotype blocks and allow the use of genetic association approaches to identify individual causative variants. Here, we performed a genome-wide association study to identify single nucleotide polymorphisms (SNPs) and indels that could confer beta-lactam non-susceptibility using 3,085 Thai and 616 USA pneumococcal isolates as independent datasets for the variant discovery. The large sample sizes allowed us to narrow the source of beta-lactam non-susceptibility from long recombinant fragments down to much smaller loci comprised of discrete or linked SNPs. While some loci appear to be universal resistance determinants, contributing equally to non-susceptibility for at least two classes of beta-lactam antibiotics, some play a larger role in resistance to particular antibiotics. All of the identified loci have a highly non-uniform distribution in the populations. They are enriched not only in vaccine-targeted, but also non-vaccine-targeted lineages, which may raise clinical concerns. Identification of single nucleotide polymorphisms underlying resistance will be essential for future use of genome sequencing to predict antibiotic sensitivity in clinical microbiology. | 2014 | 25101644 |
| 9070 | 7 | 0.9995 | Automated annotation of mobile antibiotic resistance in Gram-negative bacteria: the Multiple Antibiotic Resistance Annotator (MARA) and database. BACKGROUND: Multiresistance in Gram-negative bacteria is often due to acquisition of several different antibiotic resistance genes, each associated with a different mobile genetic element, that tend to cluster together in complex conglomerations. Accurate, consistent annotation of resistance genes, the boundaries and fragments of mobile elements, and signatures of insertion, such as DR, facilitates comparative analysis of complex multiresistance regions and plasmids to better understand their evolution and how resistance genes spread. OBJECTIVES: To extend the Repository of Antibiotic resistance Cassettes (RAC) web site, which includes a database of 'features', and the Attacca automatic DNA annotation system, to encompass additional resistance genes and all types of associated mobile elements. METHODS: Antibiotic resistance genes and mobile elements were added to RAC, from existing registries where possible. Attacca grammars were extended to accommodate the expanded database, to allow overlapping features to be annotated and to identify and annotate features such as composite transposons and DR. RESULTS: The Multiple Antibiotic Resistance Annotator (MARA) database includes antibiotic resistance genes and selected mobile elements from Gram-negative bacteria, distinguishing important variants. Sequences can be submitted to the MARA web site for annotation. A list of positions and orientations of annotated features, indicating those that are truncated, DR and potential composite transposons is provided for each sequence, as well as a diagram showing annotated features approximately to scale. CONCLUSIONS: The MARA web site (http://mara.spokade.com) provides a comprehensive database for mobile antibiotic resistance in Gram-negative bacteria and accurately annotates resistance genes and associated mobile elements in submitted sequences to facilitate comparative analysis. | 2018 | 29373760 |
| 9886 | 8 | 0.9995 | Development of an antimicrobial resistance plasmid transfer gene database for enteric bacteria. Introduction: Type IV secretion systems (T4SSs) are integral parts of the conjugation process in enteric bacteria. These secretion systems are encoded within the transfer (tra) regions of plasmids, including those that harbor antimicrobial resistance (AMR) genes. The conjugal transfer of resistance plasmids can lead to the dissemination of AMR among bacterial populations. Methods: To facilitate the analyses of the conjugation-associated genes, transfer related genes associated with key groups of AMR plasmids were identified, extracted from GenBank and used to generate a plasmid transfer gene dataset that is part of the Virulence and Plasmid Transfer Factor Database at FDA, serving as the foundation for computational tools for the comparison of the conjugal transfer genes. To assess the genetic feature of the transfer gene database, genes/proteins of the same name (e.g., traI/TraI) or predicted function (VirD4 ATPase homologs) were compared across the different plasmid types to assess sequence diversity. Two analyses tools, the Plasmid Transfer Factor Profile Assessment and Plasmid Transfer Factor Comparison tools, were developed to evaluate the transfer genes located on plasmids and to facilitate the comparison of plasmids from multiple sequence files. To assess the database and associated tools, plasmid, and whole genome sequencing (WGS) data were extracted from GenBank and previous WGS experiments in our lab and assessed using the analysis tools. Results: Overall, the plasmid transfer database and associated tools proved to be very useful for evaluating the different plasmid types, their association with T4SSs, and increased our understanding how conjugative plasmids contribute to the dissemination of AMR genes. | 2023 | 38033626 |
| 4661 | 9 | 0.9995 | Methods for the targeted sequencing and analysis of integrons and their gene cassettes from complex microbial communities. Integrons are microbial genetic elements that can integrate mobile gene cassettes. They are mostly known for spreading antibiotic resistance cassettes among human pathogens. However, beyond clinical settings, gene cassettes encode an extraordinarily diverse range of functions important for bacterial adaptation. The recovery and sequencing of cassettes has promising applications, including: surveillance of clinically important genes, particularly antibiotic resistance determinants; investigating the functional diversity of integron-carrying bacteria; and novel enzyme discovery. Although gene cassettes can be directly recovered using PCR, there are no standardised methods for their amplification and, importantly, for validating sequences as genuine integron gene cassettes. Here, we present reproducible methods for the amplification, sequence processing, and validation of gene cassette amplicons from complex communities. We describe two different PCR assays that either amplify cassettes together with integron integrases, or gene cassettes together within cassette arrays. We compare the performance of Nanopore and Illumina sequencing, and present bioinformatic pipelines that filter sequences to ensure that they represent amplicons from genuine integrons. Using a diverse set of environmental DNAs, we show that our approach can consistently recover thousands of unique cassettes per sample and up to hundreds of different integron integrases. Recovered cassettes confer a wide range of functions, including antibiotic resistance, with as many as 300 resistance cassettes found in a single sample. In particular, we show that class one integrons are collecting and concentrating resistance genes out of the broader diversity of cassette functions. The methods described here can be applied to any environmental or clinical microbiome sample. | 2022 | 35298369 |
| 5109 | 10 | 0.9995 | PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest. Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance. | 2022 | 35382558 |
| 4623 | 11 | 0.9995 | Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes. Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome. | 2019 | 31611361 |
| 9894 | 12 | 0.9995 | Mechanisms of Evolution in High-Consequence Drug Resistance Plasmids. The dissemination of resistance among bacteria has been facilitated by the fact that resistance genes are usually located on a diverse and evolving set of transmissible plasmids. However, the mechanisms generating diversity and enabling adaptation within highly successful resistance plasmids have remained obscure, despite their profound clinical significance. To understand these mechanisms, we have performed a detailed analysis of the mobilome (the entire mobile genetic element content) of a set of previously sequenced carbapenemase-producing Enterobacteriaceae (CPE) from the National Institutes of Health Clinical Center. This analysis revealed that plasmid reorganizations occurring in the natural context of colonization of human hosts were overwhelmingly driven by genetic rearrangements carried out by replicative transposons working in concert with the process of homologous recombination. A more complete understanding of the molecular mechanisms and evolutionary forces driving rearrangements in resistance plasmids may lead to fundamentally new strategies to address the problem of antibiotic resistance. IMPORTANCE: The spread of antibiotic resistance among Gram-negative bacteria is a serious public health threat, as it can critically limit the types of drugs that can be used to treat infected patients. In particular, carbapenem-resistant members of the Enterobacteriaceae family are responsible for a significant and growing burden of morbidity and mortality. Here, we report on the mechanisms underlying the evolution of several plasmids carried by previously sequenced clinical Enterobacteriaceae isolates from the National Institutes of Health Clinical Center (NIH CC). Our ability to track genetic rearrangements that occurred within resistance plasmids was dependent on accurate annotation of the mobile genetic elements within the plasmids, which was greatly aided by access to long-read DNA sequencing data and knowledge of their mechanisms. Mobile genetic elements such as transposons and integrons have been strongly associated with the rapid spread of genes responsible for antibiotic resistance. Understanding the consequences of their actions allowed us to establish unambiguous evolutionary relationships between plasmids in the analysis set. | 2016 | 27923922 |
| 5112 | 13 | 0.9995 | Genome-Based Prediction of Bacterial Antibiotic Resistance. Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences. | 2019 | 30381421 |
| 5114 | 14 | 0.9995 | Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing. Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples. | 2022 | 35705638 |
| 4456 | 15 | 0.9995 | Predictive analysis of transmissible quinolone resistance indicates Stenotrophomonas maltophilia as a potential source of a novel family of Qnr determinants. BACKGROUND: Predicting antibiotic resistance before it emerges at clinical settings constitutes a novel approach for preventing and fighting resistance of bacterial pathogens. To analyse the possibility that novel plasmid-encoded quinolone resistance determinants (Qnr) can emerge and disseminate among bacterial pathogens, we searched the presence of those elements in nearly 1000 bacterial genomes and metagenomes. RESULTS: We have found a number of novel potential qnr genes in the chromosomes of aquatic bacteria and in metagenomes from marine organisms. Functional studies of the Stenotrophomonas maltophilia Smqnr gene show that plasmid-encoded SmQnr confers quinolone resistance upon its expression in a heterologous host. CONCLUSION: Altogether, the data presented in our work support the notion that predictive studies on antibiotic resistance are feasible, using currently available information on bacterial genomes and with the aid of bioinformatic and functional tools. Our results confirm that aquatic bacteria can be the origin of plasmid-encoded Qnr, and highlight the potential role of S. maltophilia as a source of novel Qnr determinants. | 2008 | 18793450 |
| 4624 | 16 | 0.9995 | Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the 'distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings. | 2017 | 28205635 |
| 9887 | 17 | 0.9995 | PCR-Based Analysis of ColE1 Plasmids in Clinical Isolates and Metagenomic Samples Reveals Their Importance as Gene Capture Platforms. ColE1 plasmids are important vehicles for the spread of antibiotic resistance in the Enterobacteriaceae and Pasteurellaceae families of bacteria. Their monitoring is essential, as they harbor important resistant determinants in humans, animals and the environment. In this work, we have analyzed ColE1 replicons using bioinformatic and experimental approaches. First, we carried out a computational study examining the structure of different ColE1 plasmids deposited in databases. Bioinformatic analysis of these ColE1 replicons revealed a mosaic genetic structure consisting of a host-adapted conserved region responsible for the housekeeping functions of the plasmid, and a variable region encoding a wide variety of genes, including multiple antibiotic resistance determinants. From this exhaustive computational analysis we developed a new PCR-based technique, targeting a specific sequence in the conserved region, for the screening, capture and sequencing of these small plasmids, either specific for Enterobacteriaceae or specific for Pasteurellaceae. To validate this PCR-based system, we tested various collections of isolates from both bacterial families, finding that ColE1 replicons were not only highly prevalent in antibiotic-resistant isolates, but also present in susceptible bacteria. In Pasteurellaceae, ColE1 plasmids carried almost exclusively antibiotic resistance genes. In Enterobacteriaceae, these plasmids encoded a large range of traits, including not only antibiotic resistance determinants, but also a wide variety of genes, showing the huge genetic plasticity of these small replicons. Finally, we also used a metagenomic approach in order to validate this technique, performing this PCR system using total DNA extractions from fecal samples from poultry, turkeys, pigs and humans. Using Illumina sequencing of the PCR products we identified a great diversity of genes encoded by ColE1 replicons, including different antibiotic resistance determinants, supporting the previous results achieved with the collections of bacterial isolates. In addition, we detected cryptic ColE1 plasmids in both families with no known genes in their variable region, which we have named sentinel plasmids. In conclusion, in this work we present a useful genetic tool for the detection and analysis of ColE1 plasmids, and confirm their important role in the dissemination of antibiotic resistance, especially in the Pasteurellaceae family of bacteria. | 2018 | 29615998 |
| 5078 | 18 | 0.9995 | A simple cut and stretch assay to detect antimicrobial resistance genes on bacterial plasmids by single-molecule fluorescence microscopy. Antimicrobial resistance (AMR) is a fast-growing threat to global health. The genes conferring AMR to bacteria are often located on plasmids, circular extrachromosomal DNA molecules that can be transferred between bacterial strains and species. Therefore, effective methods to characterize bacterial plasmids and detect the presence of resistance genes can assist in managing AMR, for example, during outbreaks in hospitals. However, existing methods for plasmid analysis either provide limited information or are expensive and challenging to implement in low-resource settings. Herein, we present a simple assay based on CRISPR/Cas9 excision and DNA combing to detect antimicrobial resistance genes on bacterial plasmids. Cas9 recognizes the gene of interest and makes a double-stranded DNA cut, causing the circular plasmid to linearize. The change in plasmid configuration from circular to linear, and hence the presence of the AMR gene, is detected by stretching the plasmids on a glass surface and visualizing by fluorescence microscopy. This single-molecule imaging based assay is inexpensive, fast, and in addition to detecting the presence of AMR genes, it provides detailed information on the number and size of plasmids in the sample. We demonstrate the detection of several β-lactamase-encoding genes on plasmids isolated from clinical samples. Furthermore, we demonstrate that the assay can be performed using standard microbiology and clinical laboratory equipment, making it suitable for low-resource settings. | 2022 | 35660772 |
| 4453 | 19 | 0.9995 | dfrA trimethoprim resistance genes found in Gram-negative bacteria: compilation and unambiguous numbering. To track the spread of antibiotic resistance genes, accurate identification of individual genes is essential. Acquired trimethoprim resistance genes encoding trimethoprim-insensitive homologues of the sensitive dihydrofolate reductases encoded by the folA genes of bacteria are increasingly found in genome sequences. However, naming and numbering in publicly available records (journal publications or entries in the GenBank non-redundant DNA database) has not always been unambiguous. In addition, the nomenclature has evolved over time. Here, the changes in nomenclature and the most commonly encountered problems and pitfalls affecting dfrA gene identification arising from historically incorrect or inaccurate numbering are explained. The complete set of dfrA genes/DfrA proteins found in Gram-negative bacteria for which readily searchable sequence information is currently available has been compiled using less than 98% identity for both the gene and the derived protein sequence as the criteria for assignment of a new number. In most cases, trimethoprim resistance has been demonstrated. The gene context, predominantly in a gene cassette or near the ori end of CR1 or CR2, is also covered. The RefSeq database that underpins the programs used to automatically identify resistance genes in genome data sets has been curated to assign all sequences listed to the correct number. This led to the assignment of corrected or new gene numbers to several mis-assigned sequences. The unique numbers assigned for the dfrA/DfrA set are now listed in the RefSeq database, which we propose provides a way forward that should end future duplication of numbers and the confusion that causes. | 2021 | 34180526 |