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Rapid analysis of lower respiratory infection samples case study

Bacteria

Responsible for over three million deaths worldwide per year, lower respiratory infections (LRIs) are the leading cause of death from infectious disease1. A wide range of pathogens cause these infections but precise identification and characterisation of these organisms using current methodology can prove challenging.

The current ‘gold-standard’ approach for investigation of bacterial LRIs is culture, the results of which can be slow to obtain (48-72 hours) or uncertain2. Alternative PCR-based analysis methods reduce time to result but do not detect the whole spectrum of pathogens potentially present in a sample or their antimicrobial resistance (AMR) profiles. To address these challenges, researchers at the Quadram Institute Bioscience (QIB) in Norwich, and colleagues, assessed the utility of nanopore sequencing to provide rapid pathogen identification and antimicrobial resistance profiling from mixed, metagenomic samples2. According to the researchers: ‘Metagenomic sequencing based approaches, which make no presumptions about the organisms and resistance genes that may be present, have the potential to overcome the shortcomings of both culture and PCR, by combining speed with comprehensiveness’2

Key challenges of metagenomic sequencing include the presence of high levels of host DNA and lengthy time to result. To overcome these issues, the team developed a novel sequencing workflow that takes advantage of saponin-based host DNA depletion and the real-time data analysis afforded by nanopore sequencing. Using this workflow, up to 99.99% of host nucleic acids were removed from the samples, while pathogen detection was 96.6% concordant with traditional culture techniques (Table 1; Figure 1). Importantly, the entire process — from sample acquisition to pathogen and antibiotic resistance gene identification — was achieved within just six hours. Furthermore, continuation of the sequencing run allowed the generation of sufficient data for reference-based pathogen genome assemblies, which the researchers suggested would enable investigations into emergence and spread of pathogens2.

This novel nanopore sequencing workflow is currently being evaluated as part of the INHALE programme, which aims to assess the potential of several molecular techniques to rapidly characterise the organisms responsible for pneumonia.3

Table 1 High concordance was demonstrated between nanopore metagenomic sequencing and routine culture positive results. The metagenomic approach also allowed the identification of additional bacteria not reported using culture. Table adapted from Charalampous et al 2.

 

Figure 1 Real-time species identification and quantification was performed using the WIMP workflow from Oxford Nanopore, which enabled easy visualisation of results. Images courtesy of Dr. Justin O’Grady, University of East Anglia, UK.

A complementary approach to rapidly characterise antibiotic resistance in metagenomic LRI and other clinical research samples was recently demonstrated by Břinda et al4. This study presents a novel method for inferring antibiotic resistance based on the identification of DNA sequence variation detected in a previously characterised antibiotic-resistant bacteria. Using nanopore sequencing, the team was able to sequence all bacteria present in a sample in sufficient depth to identify a known antibiotic resistant strain within just 5 minutes.

This case study is taken from the clinical white paper.

Download the clinical research white paper

References

1. World Health Organization. The top 10 causes of death. Online. Available at: http://www.who.int/news-room/fact-sheets/detail/the-top-10-causesof-dea… [Accessed: 30 October 2018]

2. Charalampous, T. et al. Rapid diagnosis of lower respiratory infection using nanopore-based clinical metagenomics. bioRxiv 387548 (2018).

3. University College London. Inhale project overview. Online. Accessed: 31 October 2018]

4. Břinda, K. Lineage calling can identify antibiotic resistant clones within minutes. bioRxiv 403204 (2018).

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