scholarly journals Plasmid Identification and Plasmid-Mediated Antimicrobial Gene Detection in Norwegian Isolates

2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
Abdolrahman Khezri ◽  
Ekaterina Avershina ◽  
Rafi Ahmad
Keyword(s):  
Antibiotic Resistance ◽  
Horizontal Transmission ◽  
Antibiotic Resistance Genes ◽  
Bioinformatic Analysis ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Hybrid Assembly ◽  
Sequencing Data ◽  
Gene Detection ◽  
E Coli

Norway is known for being one of the countries with the lowest levels of antimicrobial resistance (AMR). AMR, through acquired genes located on transposons or conjugative plasmids, is the horizontal transmission of genes required for a given bacteria to withstand antibiotics. In this work, bioinformatic analysis of whole-genome sequences and hybrid assembled data from Escherichia coli, and Klebsiella pneumoniae isolates from Norwegian patients was performed. For detection of putative plasmids in isolates, the plasmid assembly mode in SPAdes was used, followed by annotation of resulting contigs using PlasmidFinder and two curated plasmid databases (Brooks and PLSDB). Furthermore, ResFinder and Comprehensive Antibiotic Resistance Database (CARD) were used for the identification of antibiotic resistance genes (ARGs). The IncFIB plasmid was detected as the most prevalent plasmid in both E. coli, and K. pneumoniae isolates. Furthermore, ARGs such as aph(3″)-Ib, aph(6)-Id, sul1, sul2, tet(D), and qnrS1 were identified as the most abundant plasmid-mediated ARGs in Norwegian E. coli and K. pneumoniae isolates, respectively. Using hybrid assembly, we were able to locate plasmids and predict ARGs more confidently. In conclusion, plasmid identification and ARG detection using whole-genome sequencing data are heavily dependent on the database of choice; therefore, it is best to use several tools and/or hybrid assembly for obtaining reliable identification results.

scholarly journals MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5895 ◽  
Author(s):  
Thomas Andreas Kohl ◽  
Christian Utpatel ◽  
Viola Schleusener ◽  
Maria Rosaria De Filippo ◽  
Patrick Beckert ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Mycobacterium Tuberculosis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Phylogenomic Analysis ◽  
Whole Genome ◽  
Sequencing Data ◽  
Desktop Computer

Analyzing whole-genome sequencing data of Mycobacterium tuberculosis complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.

scholarly journals Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

2019 ◽  
Vol 17 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Valentina Galata ◽  
Cédric C. Laczny ◽  
Christina Backes ◽  
Georg Hemmrich-Stanisak ◽  
Susanne Schmolke ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Clinical Isolates ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

scholarly journals TETyper: a bioinformatic pipeline for classifying variation and genetic contexts of transposable elements from short-read whole-genome sequencing data

2018 ◽  
Author(s):  
Anna E Sheppard ◽  
Nicole Stoesser ◽  
Ian German-Mesner ◽  
Kasi Vegesana ◽  
A Sarah Walker ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Transposable Elements ◽  
Genome Sequencing ◽  
Resistance Genes ◽  
Whole Genome Sequencing Data ◽  
Sequence Variants ◽  
Whole Genome ◽  
Sequencing Data ◽  
Bioinformatic Pipeline ◽  
Short Read

ABSTRACTMuch of the worldwide dissemination of antibiotic resistance has been driven by resistance gene associations with mobile genetic elements (MGEs), such as plasmids and transposons. Although increasing, our understanding of resistance spread remains relatively limited, as methods for tracking mobile resistance genes through multiple species, strains and plasmids are lacking. We have developed a bioinformatic pipeline for tracking variation within, and mobility of, specific transposable elements (TEs), such as transposons carrying antibiotic resistance genes. TETyper takes short-read whole-genome sequencing data as input and identifies single-nucleotide mutations and deletions within the TE of interest, to enable tracking of specific sequence variants, as well as the surrounding genetic context(s), to enable identification of transposition events. To investigate global dissemination of Klebsiella pneumoniae carbapenemase (KPC) and its associated transposon Tn4401, we applied TETyper to a collection of >3000 publicly available Illumina datasets containing blaKPC. This revealed surprising diversity, with >200 distinct flanking genetic contexts for Tn4401, indicating high levels of transposition. Integration of sample metadata revealed insights into associations between geographic locations, host species, Tn4401 sequence variants and flanking genetic contexts. To demonstrate the ability of TETyper to cope with high copy number TEs and to track specific short-term evolutionary changes, we also applied it to the insertion sequence IS26 within a defined K. pneumoniae outbreak. TETyper is implemented in python and is freely available at https://github.com/aesheppard/TETyper.

scholarly journals Recovering Escherichia coli Plasmids in the Absence of Long-Read Sequencing Data

2021 ◽  
Vol 9 (8) ◽  
pp. 1613
Author(s):  
Julian A. Paganini ◽  
Nienke L. Plantinga ◽  
Sergio Arredondo-Alonso ◽  
Rob J. L. Willems ◽  
Anita C. Schürch
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Multidrug Resistant ◽  
Special Focus ◽  
Valuable Insight ◽  
Sequencing Data ◽  
Extended Spectrum Beta Lactamase ◽  
E Coli ◽  
Long Read

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.

scholarly journals Recovering Escherichia coli plasmids in the absence of long-read sequencing data

2021 ◽  
Author(s):  
Julian A. Paganini ◽  
Nienke L. Plantinga ◽  
Sergio Arredondo-Alonso ◽  
Rob J.L. Willems ◽  
Anita C. Schurch
Keyword(s):  
Escherichia Coli ◽  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Special Focus ◽  
Valuable Insight ◽  
Sequencing Data ◽  
Data Set ◽  
E Coli ◽  
Long Read

The incidence of infections caused by multidrug-resistant Escherichia 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 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. They use either assembly graph information (plasmidSPAdes, gplas), reference databases (MOB-Suite, FishingForPlasmids) or both (HyAsP and SCAPP) to produce plasmid predictions. The benchmark data set consisted of 240 E. coli strains, harboring 631 plasmids, which were representative for the diversity of E. coli in public databases. Notably, these strains were not used for training any of the tools. 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 recovered with small precision values (median=0.47, IQR=0.61), indicating that plasmidSPAdes frequently merged sequences derived from distinct replicons. Additionally, only 63% of all plasmid-borne ARGs were correctly predicted by plasmidSPAdes. The remaining four tools (FishingForPlasmids, HyAsP, SCAPP and gplas) were able to correctly reconstruct a combined total of 18 plasmids that were missed by MOB-suite and plasmidSPAdes. 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.

scholarly journals Characterization of Tn7-like transposons and its related antibiotic resistance among Enterobacteriaceae from livestock and poultry in China

2020 ◽  
Author(s):  
Juan He ◽  
Cui Li ◽  
Pengfei Cui ◽  
Hongning Wang
Keyword(s):  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Horizontal Transmission ◽  
Antibiotic Resistance Genes ◽  
Variable Region ◽  
Multidrug Resistant ◽  
Antibiotic Resistant ◽  
E Coli ◽  
New Type

Abstract Background: This study was aimed to investigate the prevalence and structure of Tn7-like in Enterobacteriaceae from livestock and poultry as well as their possible role as reservoir of antibiotic resistance genes (ARGs).Methods: Polymerase chain reaction (PCR) and DNA sequencing analyses were used for the characterization of Tn7-like, associated integrons and ARGs. The antimicrobial resistance profile of the isolates was examined by using disc diffusion test.Results: Three hundred and seventy-eight Tn7-like-positive strains of Enterobacteriaceae were isolated, and included E. coli (128), Proteus(150), K. pneumonia(17), Salmonella(13), M. morganii (21) and A. baumannii(1), wherein high resistance was observed for Trimethoprim/Sulfamethoxazole and Streptomycin, and fifty percent of the strains were multidrug-resistant. Integrons class 2 were detected in all of the isolates and there are high frequency mutation sites especially in 535, a stop mutation. Variable region of class 2 integrons carried same gene cassettes, namely aadA1-sat2-dfrA1. From the 378 isolated strains, we found a new type of Tn7-like on a plasmid, named Tn6765.Conclusions: These findings proved that the Tn7-like can contribute to the horizontal transmission of antibiotic resistant genes in livestock and poultry. As potential vessels for antibiotic resistance genes (ARGs), Tn7-like could not be ignored due to their efficient transfer ability in environments.

scholarly journals Whole Genome Sequencing of Extended-Spectrum- and AmpC- β-Lactamase-Positive Enterobacterales Isolated From Spinach Production in Gauteng Province, South Africa

2021 ◽  
Vol 12 ◽  
Author(s):  
Loandi Richter ◽  
Erika M. du Plessis ◽  
Stacey Duvenage ◽  
Mushal Allam ◽  
Arshad Ismail ◽  
...  
Keyword(s):  
South Africa ◽  
Antibiotic Resistance ◽  
Resistance Genes ◽  
Irrigation Water ◽  
Production Systems ◽  
Antibiotic Resistance Genes ◽  
Whole Genome ◽  
Human Pathogens ◽  
E Coli ◽  
Encoding Gene

The increasing occurrence of multidrug-resistant (MDR) extended-spectrum β-lactamase- (ESBL) and/or AmpC β-lactamase- (AmpC) producing Enterobacterales in irrigation water and associated irrigated fresh produce represents risks related to the environment, food safety, and public health. In South Africa, information about the presence of ESBL/AmpC-producing Enterobacterales from non-clinical sources is limited, particularly in the water–plant-food interface. This study aimed to characterize 19 selected MDR ESBL/AmpC-producing Escherichia coli (n=3), Klebsiella pneumoniae (n=5), Serratia fonticola (n=10), and Salmonella enterica (n=1) isolates from spinach and associated irrigation water samples from two commercial spinach production systems within South Africa, using whole genome sequencing (WGS). Antibiotic resistance genes potentially encoding resistance to eight different classes were present, with blaCTX-M-15 being the dominant ESBL encoding gene and blaACT-types being the dominant AmpC encoding gene detected. A greater number of resistance genes across more antibiotic classes were seen in all the K. pneumoniae strains, compared to the other genera tested. From one farm, blaCTX-M-15-positive K. pneumoniae strains of the same sequence type 985 (ST 985) were present in spinach at harvest and retail samples after processing, suggesting successful persistence of these MDR strains. In addition, ESBL-producing K. pneumoniae ST15, an emerging high-risk clone causing nosocomical outbreaks worldwide, was isolated from irrigation water. Known resistance plasmid replicon types of Enterobacterales including IncFIB, IncFIA, IncFII, IncB/O, and IncHI1B were observed in all strains following analysis with PlasmidFinder. However, blaCTX-M-15 was the only β-lactamase resistance gene associated with plasmids (IncFII and IncFIB) in K. pneumoniae (n=4) strains. In one E. coli and five K. pneumoniae strains, integron In191 was observed. Relevant similarities to human pathogens were predicted with PathogenFinder for all 19 strains, with a confidence of 0.635–0.721 in S. fonticola, 0.852–0.931 in E. coli, 0.796–0.899 in K. pneumoniae, and 0.939 in the S. enterica strain. The presence of MDR ESBL/AmpC-producing E. coli, K. pneumoniae, S. fonticola, and S. enterica with similarities to human pathogens in the agricultural production systems reflects environmental and food contamination mediated by anthropogenic activities, contributing to the spread of antibiotic resistance genes.

scholarly journals Conjugation factors controlling F-plasmid antibiotic resistance transmission

2018 ◽  
Author(s):  
Hanna Alalam ◽  
Fabrice E. Graf ◽  
Martin Palm ◽  
Marie Abadikhah ◽  
Martin Zackrisson ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Horizontal Transmission ◽  
Antibiotic Resistance Genes ◽  
Host Cells ◽  
The Novel ◽  
F Plasmid ◽  
Resistance Development ◽  
E Coli ◽  
Cost Efficient ◽  
Efficient Screening

ABSTRACTThe rapid horizontal transmission of many antibiotic resistance genes between bacterial host cells on conjugative plasmids is a major cause of the accelerating antibiotic resistance crisis. Preventing understanding and targeting conjugation, there currently are no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation. We introduce a novel experimental framework to screen for conjugation based horizontal transmission of antibiotic resistance between >60.000 pairs of cell populations in parallel. Plasmid-carrying donor strains are constructed in high throughput. We then mix the resistance plasmid carrying donors with recipients in a design where only transconjugants can reproduce, measure growth in dense intervals and extract transmission times as the growth lag. As proof-of-principle, we exhaustively explored chromosomal genes controlling F plasmid donation withinE. colipopulations, by screening the Keio deletion collection at high replication. We recover all six known chromosomal gene mutants affecting conjugation and identify >50 novel factors, all of which diminish antibiotic resistance transmission. We verify 10 of the novel genes’ effects in a liquid mating assay. The new framework holds great potential for exhaustive disclosing of candidate targets for helper drugs that delay resistance development in patients and societies and improves the longevity of current and future antibiotics.

scholarly journals Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

2019 ◽  
Author(s):  
Allison L. Hicks ◽  
Nicole Wheeler ◽  
Leonor Sánchez-Busó ◽  
Jennifer L. Rakeman ◽  
Simon R. Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Antibiotic Susceptibility ◽  
Sequence Data ◽  
Model Performance ◽  
Outcome Data ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

AbstractPrediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.Author SummaryMachine learning-based prediction of antibiotic resistance from bacterial genome sequences represents a promising tool to rapidly determine the antibiotic susceptibility profile of clinical isolates and reduce the morbidity and mortality resulting from inappropriate and ineffective treatment. However, while there has been much focus on demonstrating the diagnostic potential of these modeling approaches, there has been little assessment of potential caveats and prerequisites associated with implementing predictive models of drug resistance in the clinical setting. Our results highlight significant biological and technical challenges facing the application of machine learning-based prediction of antibiotic resistance as a diagnostic tool. By outlining specific factors affecting model performance, our findings provide a framework for future work on modeling drug resistance and underscore the necessity of continued comprehensive sampling and reporting of treatment outcome data for building reliable and sustainable diagnostics.

Sign in / Sign up

Export Citation Format

Share Document