Abstract
Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of Escherichia coli K−12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, in situ characterization of microbial metabolism.
Highlights
Recent advancements in sequencing technologies have opened up the possibility of in situ analysis of genomes and transcriptomes
The sequencing library was prepared using the ONT Rapid Barcoding Sequencing kit (SQK-RBK004) according to the manufacturer’s protocol with the following modification: the gDNA input was increased to 800 ng genomic DNA and the optional SPRI bead cleanup was omitted
Our objective was to assess the quality of genome-scale metabolic network reconstructions resulting from MinION-based assemblies
Summary
Recent advancements in sequencing technologies have opened up the possibility of in situ analysis of genomes and transcriptomes. The platform generates relatively long sequencing reads enabling the assembly of genomes at low coverage depth (Wick and Holt, 2019). This reduces the computational resources required to assemble genomes from nanopore reads, facilitating bioinformatic pipelines that can be run on a personal laptop (Castro-Wallace et al, 2017). Despite advances in reagent chemistry, flowcell design and computational basecalling algorithms, the technology suffers from lower consensus genome accuracy compared with short-read sequencing techniques, especially in homopolymer regions (Gargis et al, 2019). Computational techniques exist to correct the frame-shift mutations that arise from these sequencing errors (Arumugam et al, 2019); resource intensive techniques may not be accessible in austere locales where the MinION device’s portability is a unique capability
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