High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies

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.

Keywords: MinION long-read sequencing; MinION nanopore device®; antimicrobial resistance (AMR); constraint-based model; metabolic model reconstruction; nanopore sequencing.