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Next-generation sequencing diagnostics of bacteremia in septic patients

Abstract

Background Bloodstream infections remain one of the major challenges in intensive care units, leading to sepsis or even septic shock in many cases. Due to the lack of timely diagnostic approaches with sufficient sensitivity, mortality rates of sepsis are still unacceptably high. However a prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bloodstream infections. Although various targeted molecular tests for blood samples are available, time-consuming blood culture-based approaches still represent the standard of care for the identification of bacteria. Methods Here we describe the establishment of a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing. Results We found significant levels of DNA fragments derived from pathogenic bacteria in samples from septic patients. Quantitative evaluation of normalized read counts and introduction of a sepsis indicating quantifier (SIQ) score allowed for an unambiguous identification of Gram-positive as well as Gram-negative bacteria that exactly matched with blood cultures from corresponding patient samples. In addition, we also identified species from samples where blood cultures were negative. Reads of non-human origin also comprised fragments derived from antimicrobial resistance genes, showing that, in principle, prediction of specific types of resistance might be possible. Conclusions The complete workflow from sample preparation to species identification report could be accomplished in roughly 30 h, thus making this approach a promising diagnostic platform for critically ill patients suffering from bloodstream infections.

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Authors
  • Silke Grumaz
  • Philip Stevens
  • Christian Grumaz
  • Sebastian O. Decker
  • Markus A. Weigand
  • Stefan Hofer
  • Thorsten Brenner
  • Arndt von Haeseler
  • Kai Sohn
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Citation
Category
Journal Paper
Divisions
Bioinformatics and Computational Biology
Journal or Publication Title
Genome Medicine
ISSN
1756-994X
Publisher
BioMed Central (Springer Nature)
Place of Publication
LONDON WC1X 8HL, ENGLAND
Number
1
Volume
8:73
Date
July 2016
Official URL
http://dx.doi.org/10.1186/s13073-016-0326-8
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