Biology subjects

Rossen, J. W. A.

Publications and source records attributed to Rossen, J. W. A..

5 recordsLinked to original sources

bioRxiv2024

Enhancing antimicrobial resistance monitoring: Core Plasmid Multi-Locus sequence typing (cpMLST) with Oxford Nanopore sequencing technology (ONT)

BackgroundThe spread of antimicrobial resistance (AMR) poses a significant threat to global public health, primarily driven by the horizontal gene transfer of resistance genes via plasmids. Understanding the dynamics of plasmid transmission within (intra) and between (inter) bacterial species is crucial for developing effective countermeasures. In this study, we utilize long-read sequencing technology to develop an innovative approach called core plasmid Multi-Locus Sequence Typing (cpMLST) and track the global plasmid transmission pathways. MethodsWe collected wastewater samples from six different hospitals in Germany. Within 24 hours of collection, we isolated extensively drug-resistant (XDR) bacteria using CHROMagar plates (CHROMagar, Paris, France). Species identification was done using MALDI-TOF MS (Bruker Daltonics, Bremen, Germany). Genomic DNA from the isolates was extracted using the PureLink Genomic DNA Mini Kit (Invitrogen, Carlsbad, USA) and subsequently sequenced using Oxford Nanopore Technologies (ONT) sequencing (Oxford Nanopore Technologies). We employed the SQK-RBK114.24 kit on an R10.4.1 flow cell, achieving a minimum of 250 Mb of raw data for each isolate with an accuracy of 98.9%. The reads were assembled using Unicycler, and the quality of the assemblies was assessed using QUAST and Bandage. The assembled genomes were then uploaded to the Center for Genomic Epidemiology (CGE) https://www.genomicepidemiology.org/ platform to determine incompatibility (Inc) types and antimicrobial resistance (AMR) genes. Additionally, we performed core plasmid analysis using chewBBACA v.3.3.10 to assess plasmid relatedness. ResultsThe analysis confirmed the identification of various bacterial isolates. Among the most prevalent XDR isolate were Enterobacter roggenkampii (n=38), Serratia marcescens (n=12), Klebseilla oxytoca (n=11), Klebseilla michiganensis (n=8), and Citrobacter freundii (n=7). We conducted a cpMLST analysis to examine the highly prevalent plasmids and reveal evolutionary relationships and transmission patterns. Each isolate harbored different types of complete plasmids. Among those were the IncFII (n=45), IncHI (n=26), IncX3 (n=21), and other incompatibility groups. By focusing on shared Inc types among the bacterial species, their transfer within a single bacterial species (intra-species transfer) and between different species (inter-species transfer) were identified. The allelic differences ranged from 3-55 (IncHI), 2-32 (IncFII) and 2-24 (IncX3). Moreover, these plasmids, typically identified as smaller plasmids (IncX3), were larger than their commonly reported sizes. The analysis of plasmid dynamics across diverse bacterial hosts provides valuable insight into the mechanisms driving the spread of antimicrobial resistance, offering potential strategies for better controlling the dissemination of antimicrobial resistance and its transmission in clinical environments. ConclusionIn conclusion, long-read sequencing technologies emerge as an essential tool for investigating the transmission dynamics of antimicrobial resistance genes among plasmids. By implementing cp-MLST, we anticipate achieving an unparalleled understanding of the global dissemination and evolutionary patterns of resistance-carrying plasmids. This approach offers superior resolution to conventional plasmid typing methods, potentially opening new avenues for innovative strategies to combat the escalating AMR crisis.

microbiology↗

bioRxiv2020

Genetic diversity of blaKPC-gene-containing IncF plasmids from epidemiologically related and unrelated Enterobacteriaceae

BackgroundLimited information is available on whether blaKPC-containing plasmids from isolates in a hospital outbreak can be differentiated from epidemiologically unrelated blaKPC-containing plasmids based on sequence data. ObjectiveThis study aimed to evaluate the performance of three approaches to distinguish epidemiologically related from unrelated blaKPC-containing IncF plasmids. MethodEpidemiologically related isolates, were short- and long-read whole genome sequenced on an Illumina MiSeq and MinION sequencer. A hybrid assembly was performed and plasmid sequences were extracted from the assembly graph. Epidemiologically unrelated plasmid sequences were extracted from the GenBank. Pairwise comparisons were performed of epidemiologically related and unrelated plasmids based on SNP differences using snippy, phylogenetic distance using Roary and using a similarity index that penalizes size differences between plasmids (Stoesser-index). The percentage of pairwise comparisons misclassified as genetically related or as clonally unrelated was determined using different genetic thresholds for genetic relatedness for all three comparison methods. ResultsDespite the median number of SNP differences, Roary phylogenetic distance, and Stoesser-index differed between the epidemiologically related and unrelated plasmids, the range of differences overlapped between the two comparison groups for all three comparison methods. When using a genetic similarity threshold that classified 100% of epidemiologically related plasmid pairs as genetically related, the percentages of plasmids misclassified as epidemiologically related ranged from 6.7% (Roary) to 20.8% (Stoesser-index). DiscussionAlthough epidemiologically related plasmids can be distinguished from unrelated plasmids based on genetic similarity, epidemiologically related and unrelated blaKPC-containing IncF plasmids show a high degree of sequence similarity. The phylogenetic distance as determined using Roary showed the highest degree of discriminatory power between the epidemiologically related and unrelated plasmids. Impact statementAccurately distinguishing epidemiologically related from unrelated plasmids is essential to detect nosocomial plasmid transmission in outbreaks. However, limited information is available on whether blaKPC-containing plasmids from isolates in a hospital outbreak can be differentiated from epidemiologically unrelated blaKPC-containing plasmids based on sequence data. This study aimed to evaluate the performance of three approaches to distinguish epidemiologically related from unrelated blaKPC-containing IncF plasmids. Pairwise comparisons were performed of epidemiologically related and unrelated plasmids based on SNP differences using snippy, phylogenetic distance using Roary and using a similarity index that penalizes size differences between plasmids (Stoesser-index). Based on our results, epidemiologically related plasmids can be distinguished from unrelated plasmids based on genetic similarity. Despite this, epidemiologically related and unrelated blaKPC-containing IncF plasmids show a high degree of sequence similarity and judgements on the horizontal transfer of these plasmids during hospital outbreaks based on genetic identity should be made with caution. The phylogenetic distance determined using Roary showed the highest discriminatory power between the epidemiologically related and unrelated plasmids. Data summaryShort-and long-read sequence data of the epidemiologically related Enterobacteriaceae isolates included in this study are available from the publicly available European Nucleotide Archive of the European Bioinformatics Institute under study accession number: PRJEB41009. The authors confirm that all supporting data have been provided within the article and through the supplementary data files.

microbiology↗

bioRxiv2019

Detection of extended-spectrum beta-lactamase (ESBL) genes and plasmid replicons in Enterobacteriaceae using PlasmidSPAdes assembly of short-read sequence data

INTRODUCTIONKnowledge of the epidemiology of plasmids is essential for understanding the evolution and spread of antimicrobial resistance. PlasmidSPAdes attempts to reconstruct plasmids using short-read sequence data. Accurate detection of extended-spectrum beta-lactamase (ESBL) genes and plasmid replicon genes is a prerequisite for the use of plasmid assembly tools to investigate the role of plasmids in the spread and evolution of ESBL production in Enterobacteriaceae. This study evaluated the performance of PlasmidSPAdes plasmid assembly for Enterobacteriaceae in terms of detection of ESBL-encoding genes, plasmid replicons and chromosomal genes, and assessed the effect of k-mer size. METHODSShort-read sequence data of 59 ESBL-producing Enterobacteriaceae were assembled with PlasmidSPAdes using different k-mer sizes (21, 33, 55, 77, 99 and 127). For every k-mer size, the presence of ESBL genes, plasmid replicons and a selection of chromosomal genes in the plasmid assembly was determined. RESULTSOut of 241 plasmid replicons and 66 ESBL genes detected by whole-genome assemblies, 213 plasmid replicons (88%; 95% Confidence Interval (CI): 83.9-91.9) and 43 ESBL genes (65%; 95%CI: 53.1-75.6) were detected in the plasmid assemblies obtained by PlasmidSPAdes. For most ESBL genes (83.3%) and plasmid replicons (72.0%), detection results did not differ between the k-mer sizes used in the plasmid assembly. No optimal k-mer size could be defined for the number of ESBL genes and plasmid replicons detected. For most isolates, the number of chromosomal genes detected in the plasmid assemblies decreased with increasing k-mer size. CONCLUSIONBased on our findings, PlasmidSPAdes is not a suitable plasmid assembly tool for short-read sequence data of ESBL-encoding plasmids of Enterobacteriaceae.

microbiology↗

bioRxiv2019

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

PurposeShigella spp. and E. coli are closely related and cannot be distinguished using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) with commercially available databases. Here, three alternative approaches using MALDI-TOF MS to identify and distinguish Shigella spp., E. coli, and its pathotype EIEC were explored. MethodsA custom-made database was developed, biomarkers were assigned, and classification models using machine learning were designed and evaluated using spectra of 456 Shigella spp., 42 E. coli, and 61 EIEC isolates, obtained by the direct smear method and the ethanol-formic acid extraction method. The isolates were identified using ipaH PCR and phenotypic and serological typing. ResultsIdentification with a custom-made database resulted in >94% Shigella identified at the genus level and >91% S. sonnei and S. flexneri at the species level, but distinction of S. dysenteriae, S. boydii, and E. coli was poor. Moreover, 10-15% of duplicates rendered discrepant results. With biomarker assignment, 98% S. sonnei isolates were correctly identified, although the S. sonnei biomarkers were not specific as other species, for instance S. boydii and E. coli were also identified as S. sonnei. Discriminating markers for S. dysenteriae, S. boydii, and E. coli were not assigned at all. Classifiers identified Shigella in 96% of isolates correctly, but most E. coli isolates were also assigned to Shigella. ConclusionNone of the proposed alternative approaches is suitable for use in clinical diagnostics for the identification of Shigella spp., E. coli, and EIEC, reflecting their relatedness and problematic taxonomical classification. We suggest the use of MALDI-TOF MS for the identification of the Shigella spp./E. coli complex, but other tests should be used for distinction.

microbiology↗

bioRxiv2019

DEN-IM: Dengue Virus identification from shotgun and targeted metagenomics

Dengue virus (DENV) represents a public health and economic burden in affected countries. The availability of genomic data is key to understand viral evolution and dynamics, supporting improved control strategies. Currently, the use of High Throughput Sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR amplified viral sequences (targeted metagenomics), is the most informative approach to monitor the viral dissemination and genetic diversity.\n\nDespite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper reproducibility and comparison of results.\n\nHere we present DEN-IM, a one-stop, user-friendly, containerised and reproducible workflow for the analysis of DENV sequencing data, both from shotgun and targeted metagenomics approaches. It is able to infer DENV coding sequence (CDS), identify serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement of extensive bioinformatics expertise.\n\nUsing DEN-IM, we successfully analysed two DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples of varying serotype and genotype, including a spiked sample containing the existing four serotypes. The second dataset consisted of 106 targeted metagenomics samples of DENV 3 genotype III where DEN-IM allowed detection of the intra-genotype diversity.\n\nThe DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM.

bioinformatics↗