Next-generation diagnostics of bloodstream infections enabled by rapid whole-genome sequencing of bacterial cells purified from blood cultures.

Abstract

Blood culture (BC) remains the cornerstone for diagnosis of bloodstream infections (BSI), but the long turn-around time (TAT) hampers timely selection of appropriate chemotherapy. Novel molecular approaches have been developed to provide faster results but are also affected by limitations. We developed a analytical workflow named LC-WGS (Whole-Genome Sequencing of Liquid Colony) for rapid whole-genome sequencing-based diagnosis of BSI, evaluating its accuracy performance over standard of care (SoC) diagnostic procedures. A total of 85 prospectively collected positive BC were processed in parallel with SoC (subculturing, identification by MALDI-ToF, antimicrobial susceptibility testing by reference broth microdilution, usage of syndromic panels) and LC-WGS, which relied on automated purification of microbial cells (Qvella FAST system, Qvella Corp.), DNA purification, and real-time sequencing with the Oxford Nanopore MinION. A streamlined analysis pipeline was designed for pathogen identification (Kraken2), detection of resistance markers (KmerResistance, AMRFinderPlus), virulome profiling (abricate, VFDB), phylogenetic analysis (snippy, IQ-TREE), and pathogen subtyping (Meningotype). Compared with SoC, LC-WGS returned accurate species-level identification for 98% (65/66) of monomicrobial and 88% (14/16) of polymicrobial BCs, with a TAT as short as ∼2·6 h. Accurate resistome profiling (allelic variants) was achieved for 94% (58/62) of the most clinically-relevant resistance profiles in ∼4·2 h. In silico serotying (Neisseria meningitidis), virulotyping (Escherichia coli, Klebsiella pneumoniae) and comparative phylogenomics for outbreak investigation (K. pneumoniae) proved also feasible. In this proof-of-concept study, we proved that diagnosis of BSI can be significantly shortened using an optimised workflow based on real-time sequencing, providing rapid, actionable clinical microbiological data in support of timely selection of appropriate chemotherapy. LC-WGS proved also useful as molecular epidemiology tool for public health and infection control applications. This study was partially supported by an investigator-initiated grant from Qvella Corporation.