Search bioRxiv⌕ Search

Biology subjects

Schürch, A. C.

Publications and source records attributed to Schürch, A. C..

5 recordsLinked to original sources

Limited value of Nanopore adaptive sampling in a long-read metagenomic profiling workflow of clinical sputum samples

Oxford Nanopore adaptive sampling (NAS) is a method by which the long-read sequencing flowcell accepts or rejects DNA molecules that are actively being sequenced based on their initial [~]500bp sequences, selectively increasing target data output. NAS promises up to 5-10x enrichment of target sequencing yield without additional sample preparation, but this optimal performance is dependent on ideal sample parameters which may be difficult to achieve under many real-world use-cases. We evaluated the use of NAS with the current R10.4.1 flowcell chemistry for profiling clinical sputum metagenomes, achieving at best 3.1x enrichment of bacterial sequence output due to the shorter read lengths ([~]2.5kb) from the PCR amplification necessary to compensate for low DNA extraction yields. More critically, we encountered rapid pore loss during our runs that reduced total sequencing yield by an estimated 80%. We were unable to mitigate the pore loss despite extensive attempts to reduce contaminant carry-over, and we could not determine its cause but ruled out NAS and pore underloading as contributing factors. We conclude that the utility of NAS is often limited by the characteristics of the metagenomic sample studied, and that the factors contributing to pore loss need to be resolved before ONT sequencing can be reliably applied to long-read metagenomics.

microbiology↗

A high-throughput multiplexing and selection strategy to complete bacterial genomes

BackgroundBacterial whole-genome sequencing based on short-read sequencing data often results in a draft assembly formed by contiguous sequences. The introduction of long-read sequencing technologies permits to unambiguously bridge those contiguous sequences into complete genomes. However, the elevated costs associated with long-read sequencing frequently limit the number of bacterial isolates that can be long-read sequenced. Here we evaluated the recently released 96 barcoding kit from Oxford Nanopore Technologies (ONT) to generate complete genomes on a high-throughput basis. In addition, we propose a long-read isolate selection strategy that optimizes a representative selection of isolates from large-scale bacterial collections. ResultsDespite an uneven distribution of long-reads per barcode, near-complete chromosomal sequences (assembly contiguity = 0.89) were generated for 96 Escherichia coli isolates with associated short-read sequencing data. The assembly contiguity of the plasmid replicons was even higher (0.98) which indicated the suitability of the multiplexing strategy for studies focused on resolving plasmid sequences. We benchmarked hybrid and ONT-only assemblies and showed that the combination of ONT sequencing data with short-read sequencing data is still highly desirable: (i) to perform an unbiased selection of isolates for long-read sequencing, (ii) to achieve an optimal genome accuracy and completeness, and (iii) to include small plasmids underrepresented in the ONT library. ConclusionsThe proposed long-read isolate selection ensures completing bacterial genomes of isolates that span the genome diversity inherent in large collections of bacterial isolates. We show the potential of using this multiplexing approach to close bacterial genomes on a high-throughput basis.

microbiology↗

gplas: a comprehensive tool for plasmid analysis using short-read graphs

SummaryPlasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data is often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and clustering based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short read sequence data.\n\nAvailability and implementationGplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git\n\nContacta.c.schurch@umcutrecht.nl

bioinformatics↗

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↗

Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome

Enterococcus faecium is a gut commensal of many mammals but is also recognized as a major nosocomial human pathogen, as it is listed on the WHO global priority list of multi-drug resistant organisms. Previous research has suggested that nosocomial strains have multiple zoonotic origins and are only distantly related to those involved in human commensal colonization. Here we present the first comprehensive population-wide joint genomic analysis of hospital, commensal and animal isolates using both short- and long-read sequencing techniques. This enabled us to investigate the population plasmidome, core genome variation and genome architecture in detail, using a combination of machine learning, population genomics and genome-wide co-evolution analysis. We observed a high level of genome plasticity with large-scale inversions and heterogeneous chromosome sizes, collectively painting a high-resolution picture of the adaptive landscape of E. faecium, and identified plasmids as the main indicator for host-specificity. Given the increasing availability of long-read sequencing technologies, our approach could be widely applied to other human and animal pathogen populations to unravel fine-scale mechanisms of their evolution.

genomics↗