ABSTRACT
Timely diagnosis and treatment of sepsis is a major challenge faced by critical care specialists around the world. The traditional blood culture methods have a significant turnaround time which delays targeted therapy leading to poor prognosis. In the current study, we highlight the clinical utility of a genomics solution for diagnosis and management of bloodstream infections by combining the real-time DNA sequencing of Oxford Nanopore Technology with an automated genomic data analysis software. We identify a carbapenem-resistant Klebsiella pneumoniae directly from a blood sample in <24 hours and thereby prove the effectiveness of the test in early diagnosis of sepsis.
Subject(s)
Carbapenems , Genomics , Klebsiella Infections , Klebsiella pneumoniae , Humans , Klebsiella pneumoniae/genetics , Klebsiella pneumoniae/drug effects , Klebsiella pneumoniae/isolation & purification , Klebsiella Infections/microbiology , Klebsiella Infections/diagnosis , Klebsiella Infections/drug therapy , Genomics/methods , Carbapenems/pharmacology , Bacteremia/microbiology , Bacteremia/diagnosis , Bacteremia/drug therapy , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Carbapenem-Resistant Enterobacteriaceae/genetics , Carbapenem-Resistant Enterobacteriaceae/isolation & purification , Carbapenem-Resistant Enterobacteriaceae/drug effects , Sepsis/microbiology , Sepsis/diagnosis , MaleABSTRACT
AIMS: The use of metagenomics for pathogen identification in clinical practice has been limited. Here we describe a workflow to encourage the clinical utility and potential of NGS for the screening of bacteria, fungi, and antimicrobial resistance genes (ARGs). METHODS AND RESULTS: The method includes target enrichment, long-read sequencing, and automated bioinformatics. Evaluation of several tools and databases was undertaken across standard organisms (n = 12), clinical isolates (n = 114), and blood samples from patients with suspected bloodstream infections (n = 33). The strategy used could offset the presence of host background DNA, error rates of long-read sequencing, and provide accurate and reproducible detection of pathogens. Eleven targets could be successfully tested in a single assay. Organisms could be confidently identified considering ≥60% of best hits of a BLAST-based threshold of e-value 0.001 and a percent identity of >80%. For ARGs, reads with percent identity of >90% and >60% overlap of the complete gene could be confidently annotated. A kappa of 0.83 was observed compared to standard diagnostic methods. Thus, a workflow for the direct-from-sample, on-site sequencing combined with automated genomics was demonstrated to be reproducible. CONCLUSION: NGS-based technologies overcome several limitations of current day diagnostics. Highly sensitive and comprehensive methods of pathogen screening are the need of the hour. We developed a framework for reliable, on-site, screening of pathogens.