Verschuuren, T. D. | bioRxiv Science

bioRxiv2019

Extended-spectrum beta-lactamase (ESBL)-producing and non-ESBL-producing Escherichia coli isolates causing bacteremia in the Netherlands (2014 - 2016) differ in ST distribution, antimicrobial resistance gene and virulence gene content

BackgroundKnowledge on the molecular epidemiology of Escherichia coli causing E. coli bacteremia (ECB) in the Netherlands is mostly based on extended-spectrum beta-lactamase-producing E. coli (ESBL-Ec). We determined differences in clonality and resistance and virulence gene (VG) content between non-ESBL-producing E. coli (non-ESBL-Ec) and ESBL-Ec blood isolates with different epidemiological characteristics.\n\nMaterials/methodsA random selection of non-ESBL-Ec isolates as well as all available ESBL-Ec blood isolates was obtained from two Dutch hospitals between 2014 and 2016. Whole genome sequencing was performed to infer sequence types (STs), serotypes, acquired antibiotic resistance genes and VG scores, based on presence of 49 predefined putative pathogenic VG.\n\nResultsST73 was most prevalent among the 212 non-ESBL-Ec (N=26, 12.3%) and ST131 among the 69 ESBL-Ec (N=30, 43.5%). Prevalence of ST131 among non-ESBL-Ec was 10.4% (N=22, P value < 0.001 compared to ESBL-Ec). O25:H4 was the most common serotype in both non-ESBL-Ec and ESBL-Ec. Median acquired resistance gene counts were 1 (IQR 1 - 6) and 7 (IQR 4 - 9) for non-ESBL-Ec and ESBL-Ec, respectively (P value < 0.001). Among non-ESBL-Ec, acquired resistance gene count was highest among blood isolates from a primary gastro-intestinal focus (median 4, IQR 1 - 8). Median VG scores were 13 (IQR 9 - 20) and 12 (IQR 8 - 14) for non-ESBL-Ec and ESBL-Ec, respectively (P value = 0.002). VG scores among non-ESBL-Ec from a primary urinary focus (median 15, IQR 11 - 21) were higher compared to non-ESBL-Ec from a primary gastro-intestinal (median 10, IQR 6 - 13) or hepatic-biliary focus (median 11, IQR 5 - 18) (P values = 0.007 and 0.036, respectively). VG content varied between different E. coli STs.\n\nConclusionsNon-ESBL-Ec and ESBL-Ec blood isolates from two Dutch hospitals differed in clonal distribution, resistance gene and VG content. Also, resistance gene and VG content differed between non-ESBL-Ec from different primary foci of ECB.

microbiology↗

bioRxiv2018

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

Assembly of bacterial short-read whole genome sequencing (WGS) data frequently results in hundreds of contigs for which the origin, plasmid or chromosome, is unclear. Long-read sequencing has emerged as a solution to resolve plasmid structures and to obtain complete genomes for most bacterial species. This information can be used to generate and label datasets from short-read based contigs as plasmid- or chromosome-derived. We investigated the use of several popular machine learning methods to classify short-read contigs with known plasmid- or chromosome-origin from Enterococcus faecium, Klebsiella pneumoniae and Escherichia coli using pentamer frequencies. Based on resulting F1-scores we selected support-vector machine (SVM) models as best classifier for all three bacterial species (F1-score E. faecium = 0.94, F1-score K. pneumoniae = 0.90, F1-score E. coli = 0.76), which outperformed other existing plasmid tools using an independent set of isolates (precision E. faecium = 0.92, precision K. pneumoniae = 0.86, precision E. coli = 0.82). We demonstrated the scalability of our model by accurately predicting the plasmidome of a large collection of 1,644 E. faecium isolates with only short-read WGS available using a standard laptop with a single core. A low number of false positive predicted sequences suggests that the assignment of a particular gene of interest as plasmid- or chromosome-encoded by the models is plausible. The SVM classifiers are publicly available as a new R package called mlplasmids at https://gitlab.com/sirarredondo/mlplasmids under the GNU General Public License v3.0. We additionally developed a graphical-user interface using the Shiny package which can be accessed at https://sarredondo.shinyapps.io/mlplasmids/. Single genomes can easily be predicted by uploading genome assemblies. We anticipate that this tool may significantly facilitate research on the dissemination of plasmids encoding antibiotic resistance and/or contributing to host adaptation.

microbiology↗