Rapid Nanopore Whole-Genome Sequencing for Anthrax Emergency Preparedness. - Related Documents




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512101.0000Rapid Nanopore Whole-Genome Sequencing for Anthrax Emergency Preparedness. Human anthrax cases necessitate rapid response. We completed Bacillus anthracis nanopore whole-genome sequencing in our high-containment laboratory from a human anthrax isolate hours after receipt. The de novo assembled genome showed no evidence of known antimicrobial resistance genes or introduced plasmid(s). Same-day genomic characterization enhances public health emergency response.202031961318
493510.9994Three Distinct Annotation Platforms Differ in Detection of Antimicrobial Resistance Genes in Long-Read, Short-Read, and Hybrid Sequences Derived from Total Genomic DNA or from Purified Plasmid DNA. Recent advances and lower costs in rapid high-throughput sequencing have engendered hope that whole genome sequencing (WGS) might afford complete resistome characterization in bacterial isolates. WGS is particularly useful for the clinical characterization of fastidious and slow-growing bacteria. Despite its potential, several challenges should be addressed before adopting WGS to detect antimicrobial resistance (AMR) genes in the clinical laboratory. Here, with three distinct ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), different approaches were compared to identify best practices for detecting AMR genes, including: total genomic DNA and plasmid DNA extractions, the solo assembly of Illumina short-reads and of Oxford Nanopore Technologies (ONT) long-reads, two hybrid assembly pipelines, and three in silico AMR databases. We also determined the susceptibility of each strain to 21 antimicrobials. We found that all AMR genes detected in pure plasmid DNA were also detectable in total genomic DNA, indicating that, at least in these three enterobacterial genera, the purification of plasmid DNA was not necessary to detect plasmid-borne AMR genes. Illumina short-reads used with ONT long-reads in either hybrid or polished assemblies of total genomic DNA enhanced the sensitivity and accuracy of AMR gene detection. Phenotypic susceptibility closely corresponded with genotypes identified by sequencing; however, the three AMR databases differed significantly in distinguishing mobile dedicated AMR genes from non-mobile chromosomal housekeeping genes in which rare spontaneous resistance mutations might occur. This study indicates that each method employed in a WGS workflow has an impact on the detection of AMR genes. A combination of short- and long-reads, followed by at least three different AMR databases, should be used for the consistent detection of such genes. Further, an additional step for plasmid DNA purification and sequencing may not be necessary. This study reveals the need for standardized biochemical and informatic procedures and database resources for consistent, reliable AMR genotyping to take full advantage of WGS in order to expedite patient treatment and track AMR genes within the hospital and community.202236290058
462420.9994Deciphering 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.201728205635
511130.9993Antimicrobial 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.201931597945
511440.9993Datasets 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.202235705638
511050.9993Surveillance of carbapenem-resistant organisms using next-generation sequencing. The genomic data generated from next-generation sequencing (NGS) provides nucleotide-level resolution of bacterial genomes which is critical for disease surveillance and the implementation of prevention strategies to interrupt the spread of antimicrobial resistance (AMR) bacteria. Infection with AMR bacteria, including Gram-negative Carbapenem-Resistant Organisms (CRO), may be acute and recurrent-once they have colonized a patient, they are notoriously difficult to eradicate. Through phylogenetic tools that assess the single nucleotide polymorphisms (SNPs) within a pathogen genome dataset, public health scientists can estimate the genetic identity between isolates. This information is used as an epidemiologic proxy of a putative outbreak. Pathogens with minimal to no differences in SNPs are likely to be the same strain attributable to a common source or transmission between cases. These genomic comparisons enhance public health response by prompting targeted intervention and infection control measures. This methodology overview demonstrates the utility of phenotypic and molecular assays, antimicrobial susceptibility testing (AST), NGS, publicly available genomics databases, and open-source bioinformatics pipelines for a tiered workflow to detect resistance genes and potential clusters of illness. These methods, when used in combination, facilitate a genomic surveillance workflow for detecting potential AMR bacterial outbreaks to inform epidemiologic investigations. Use of this workflow helps to target and focus epidemiologic resources to the cases with the highest likelihood of being related.202337255756
511360.9993Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.202134882354
512470.9993Oxford nanopore long-read sequencing enables the generation of complete bacterial and plasmid genomes without short-read sequencing. INTRODUCTION: Genome-based analysis is crucial in monitoring antibiotic-resistant bacteria (ARB)and antibiotic-resistance genes (ARGs). Short-read sequencing is typically used to obtain incomplete draft genomes, while long-read sequencing can obtain genomes of multidrug resistance (MDR) plasmids and track the transmission of plasmid-borne antimicrobial resistance genes in bacteria. However, long-read sequencing suffers from low-accuracy base calling, and short-read sequencing is often required to improve genome accuracy. This increases costs and turnaround time. METHODS: In this study, a novel ONT sequencing method is described, which uses the latest ONT chemistry with improved accuracy to assemble genomes of MDR strains and plasmids from long-read sequencing data only. Three strains of Salmonella carrying MDR plasmids were sequenced using the ONT SQK-LSK114 kit with flow cell R10.4.1, and de novo genome assembly was performed with average read accuracy (Q > 10) of 98.9%. RESULTS AND DISCUSSION: For a 5-Mb-long bacterial genome, finished genome sequences with accuracy of >99.99% could be obtained at 75× sequencing coverage depth using Flye and Medaka software. Thus, this new ONT method greatly improves base-calling accuracy, allowing for the de novo assembly of high-quality finished bacterial or plasmid genomes without the need for short-read sequencing. This saves both money and time and supports the application of ONT data in critical genome-based epidemiological analyses. The novel ONT approach described in this study can take the place of traditional combination genome assembly based on short- and long-read sequencing, enabling pangenomic analyses based on high-quality complete bacterial and plasmid genomes to monitor the spread of antibiotic-resistant bacteria and antibiotic resistance genes.202337256057
462380.9992Capturing 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.201931611361
511590.9992Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates.201526197475
5474100.9992Optimized Plasmid Extraction Uncovers Novel and Mobilizable Plasmids in Staphylococcus nepalensis Sharing Antimicrobial Resistance Across Different Bacterial Genera. Plasmids are key vectors in the dissemination of antimicrobial resistance (AMR), often transcending species and genus boundaries through horizontal gene transfer. Staphylococcus nepalensis, typically regarded as a commensal species, has emerged as a potential reservoir of resistance genes. In this study, we optimized plasmid extraction protocols to enhance the recovery of low-copy plasmids and applied whole-genome sequencing to characterize plasmids from a S. nepalensis strain isolated from the oral microbiota of a healthy cat in Brazil. Plasmid-enriched extraction using the Qiagen miniprep kit, with an additional enzymatic lysis step, significantly improved assembly outcomes, enabling the recovery of four complete plasmids. Three of them carried mobilizable antimicrobial resistance genes (aadK, cat, and tetK), conferring resistance to streptomycin, chloramphenicol, and tetracycline, respectively. Comparative and phylogenetic analyses revealed a high sequence similarity between these plasmids and mobile elements found in diverse pathogenic and environmental bacteria, including Staphylococcus aureus, S. epidermidis, Enterococcus sp., and Pseudomonas aeruginosa, indicating plasmid circulation across bacterial genera. Additionally, one novel plasmid was identified, displaying limited similarity to any known sequence and suggesting the existence of uncharacterized plasmid lineages in commensal staphylococci. These findings highlight the underestimated role of S. nepalensis as a hidden reservoir of mobilizable resistance genes and reinforce the need to surveil non-pathogenic bacteria in AMR monitoring frameworks.202540790092
4936110.9992A New Tool for Analyses of Whole Genome Sequences Reveals Dissemination of Specific Strains of Vancomycin-Resistant Enterococcus faecium in a Hospital. A new easy-to-use online bioinformatic tool analyzing whole genome sequences of healthcare associated bacteria was used by a local infection control unit to retrospectively map genetic relationship of isolates of E. faecium carrying resistance genes to vancomycin in a hospital. Three clusters of isolates were detected over a period of 5 years, suggesting transmission between patients. Individual relatedness between isolates within each cluster was established by SNP analyses provided by the system. Genetic antimicrobial resistance mechanisms to antibiotics other than vancomycin were identified. The results suggest that the system is suited for hospital surveillance of E. faecium carrying resistance genes to vancomycin in settings with access to next Generation Sequencing without bioinformatic expertise for interpretation of the genome sequences.202134778297
4975120.9992Comprehensive genomic and plasmid characterization of multidrug-resistant bacterial strains by R10.4.1 nanopore sequencing. The escalating prevalence of multidrug-resistant (MDR) bacteria pose a significant public health threat. Understanding the genomic features and deciphering the antibiotic resistance profiles of these pathogens is crucial for development of effective surveillance and treatment strategies. In this study, we employed the R10.4.1 nanopore sequencing technology, specifically through the use of the MinION platform, to analyze eight MDR bacterial strains originating from clinical, ecological and food sources. A single 72-hour sequencing run could yield approximately 12 million reads which covered a total of 34 gigabases (Gbp). The nanopore R10.4.1 data was processed using the Flye assembler, successfully assembling the genomes of eight bacterial strains and their 18 plasmids. Notably, the assemblies generated solely from R10.4.1 nanopore data closely matched those from next-generation sequencing data. Diverse antibiotic resistance patterns and specific resistance genes in the test strains were identified. Hospital strains that exhibited multidrug resistance were found to harbor various resistance genes that encode efflux pumps and extended-spectrum β-lactamases. Environmental and food sources were found to display resistance profiles in a species-specific manner. The composition of structurally complex plasmids in the test strains could also be revealed by analysis of nanopore long reads, which also suggested evidence of horizontal transfer of plasmids between different bacterial species. These findings provide valuable insights into the genetic characteristics of MDR bacteria and demonstrating the practicality of nanopore sequencing technology for detecting of resistance elements in bacterial pathogens.202438460283
4561130.9992Genomic Epidemiological Analysis of Antimicrobial-Resistant Bacteria with Nanopore Sequencing. Antimicrobial-resistant (AMR) bacterial infections caused by clinically important bacteria, including ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and mycobacteria (Mycobacterium tuberculosis and nontuberculous mycobacteria), have become a global public health threat. Their epidemic and pandemic clones often accumulate useful accessory genes in their genomes, such as AMR genes (ARGs) and virulence factor genes (VFGs). This process is facilitated by horizontal gene transfer among microbial communities via mobile genetic elements (MGEs), such as plasmids and phages. Nanopore long-read sequencing allows easy and inexpensive analysis of complex bacterial genome structures, although some aspects of sequencing data calculation and genome analysis methods are not systematically understood. Here we describe the latest and most recommended experimental and bioinformatics methods available for the construction of complete bacterial genomes from nanopore sequencing data and the detection and classification of genotypes of bacterial chromosomes, ARGs, VFGs, plasmids, and other MGEs based on their genomic sequences for genomic epidemiological analysis of AMR bacteria.202336781732
5125140.9992Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected >96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data.202438354391
9886150.9992Development 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.202338033626
5112160.9992Genome-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.201930381421
4566170.9992Genomic signatures of resistance and adaptability in Aeromonas and Pseudomonas from a Brazilian Hope Spot. The Cagarras Islands Archipelago, a marine protected area in Rio de Janeiro, Brazil, is endowed with a rich and abundant biodiversity. Despite being a "Hope Spot" for marine conservation, it suffers from the impacts of urban pollution, given its proximity to a major metropolis. To date, few studies have delved into the characterization of the bacterial community in the Cagarras Islands, and limited knowledge regarding the pollution effects on a microbiological scale is available. This study presents the first genomic characterization of seven potentially pathogenic strains of Aeromonas and Pseudomonas isolated from water samples collected in four expeditions. Whole genome sequencing was conducted to assess antimicrobial and heavy metal resistance, as well as pathogenicity. Comparative genomic analyses were performed using additional Aeromonas and Pseudomonas genomes from different sources. The identification of multiple genes associated with heavy metal resistance, alongside clinically relevant antimicrobial resistance genes such as mcr-3 (conferring resistance to the last-resort antimicrobial colistin) and bla(OXA) (associated with beta-lactam resistance), showcases the adaptability of these strains and the presence of potentially pathogenic, and antibiotic-resistant bacteria in a protected marine ecosystem. This study highlights the complex genomic landscape of bacteria isolated from a Brazilian Hope Spot, underscoring the importance of integrated microbial monitoring strategies.202540478385
4977180.9992In silico analyses of diversity and dissemination of antimicrobial resistance genes and mobile genetics elements, for plasmids of enteric pathogens. INTRODUCTION: The antimicrobial resistance (AMR) mobilome plays a key role in the dissemination of resistance genes encoded by mobile genetics elements (MGEs) including plasmids, transposons (Tns), and insertion sequences (ISs). These MGEs contribute to the dissemination of multidrug resistance (MDR) in enteric bacterial pathogens which have been considered as a global public health risk. METHODS: To further understand the diversity and distribution of AMR genes and MGEs across different plasmid types, we utilized multiple sequence-based computational approaches to evaluate AMR-associated plasmid genetics. A collection of 1,309 complete plasmid sequences from Gammaproteobacterial species, including 100 plasmids from each of the following 14 incompatibility (Inc) types: A/C, BO, FIA, FIB, FIC, FIIA, HI1, HI2, I1, K, M, N, P except W, where only 9 sequences were available, was extracted from the National Center for Biotechnology Information (NCBI) GenBank database using BLAST tools. The extracted FASTA files were analyzed using the AMRFinderPlus web-based tools to detect antimicrobial, disinfectant, biocide, and heavy metal resistance genes and ISFinder to identify IS/Tn MGEs within the plasmid sequences. RESULTS AND DISCUSSION: In silico prediction based on plasmid replicon types showed that the resistance genes were diverse among plasmids, yet multiple genes were widely distributed across the plasmids from enteric bacterial species. These findings provide insights into the diversity of resistance genes and that MGEs mediate potential transmission of these genes across multiple plasmid replicon types. This notion was supported by the observation that many IS/Tn MGEs and resistance genes known to be associated with them were common across multiple different plasmid types. Our results provide critical insights about how the diverse population of resistance genes that are carried by the different plasmid types can allow for the dissemination of AMR across enteric bacteria. The results also highlight the value of computational-based approaches and in silico analyses for the assessment of AMR and MGEs, which are important elements of molecular epidemiology and public health outcomes.202236777021
5745190.9992F Plasmids Are the Major Carriers of Antibiotic Resistance Genes in Human-Associated Commensal Escherichia coli. The evolution and propagation of antibiotic resistance by bacterial pathogens are significant threats to global public health. Contemporary DNA sequencing tools were applied here to gain insight into carriage of antibiotic resistance genes in Escherichia coli, a ubiquitous commensal bacterium in the gut microbiome in humans and many animals, and a common pathogen. Draft genome sequences generated for a collection of 101 E. coli strains isolated from healthy undergraduate students showed that horizontally acquired antibiotic resistance genes accounted for most resistance phenotypes, the primary exception being resistance to quinolones due to chromosomal mutations. A subset of 29 diverse isolates carrying acquired resistance genes and 21 control isolates lacking such genes were further subjected to long-read DNA sequencing to enable complete or nearly complete genome assembly. Acquired resistance genes primarily resided on F plasmids (101/153 [67%]), with smaller numbers on chromosomes (30/153 [20%]), IncI complex plasmids (15/153 [10%]), and small mobilizable plasmids (5/153 [3%]). Nearly all resistance genes were found in the context of known transposable elements. Very few structurally conserved plasmids with antibiotic resistance genes were identified, with the exception of an ∼90-kb F plasmid in sequence type 1193 (ST1193) isolates that appears to serve as a platform for resistance genes and may have virulence-related functions as well. Carriage of antibiotic resistance genes on transposable elements and mobile plasmids in commensal E. coli renders the resistome highly dynamic.IMPORTANCE Rising antibiotic resistance in human-associated bacterial pathogens is a serious threat to our ability to treat many infectious diseases. It is critical to understand how acquired resistance genes move in and through bacteria associated with humans, particularly for species such as Escherichia coli that are very common in the human gut but can also be dangerous pathogens. This work combined two distinct DNA sequencing approaches to allow us to explore the genomes of E. coli from college students to show that the antibiotic resistance genes these bacteria have acquired are usually carried on a specific type of plasmid that is naturally transferrable to other E. coli, and likely to other related bacteria.202032759337