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Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection


The gold standard for clinical diagnosis of bacterial lower respiratory infections (LRIs) is culture, which has poor sensitivity and is too slow to guide early, targeted antimicrobial therapy. Metagenomic sequencing could identify LRI pathogens much faster than culture, but methods are needed to remove the large amount of human DNA present in these samples for this approach to be feasible. We developed a metagenomics method for bacterial LRI diagnosis that features efficient saponin-based host DNA depletion and nanopore sequencing. Our pilot method was tested on 40 samples, then optimized and tested on a further 41 samples. Our optimized method (6 h from sample to result) was 96.6% sensitive and 41.7% specific for pathogen detection compared with culture and we could accurately detect antibiotic resistance genes. After confirmatory quantitative PCR and pathobiont-specific gene analyses, specificity and sensitivity increased to 100%. Nanopore metagenomics can rapidly and accurately characterize bacterial LRIs and might contribute to a reduction in broad-spectrum antibiotic use.

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Data availability

All clinical sample sequence data and assemblies are available via European Nucleotide Archive (ENA) under study accession number PRJEB30781.


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This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Program Grants for Applied Research Program (reference no. RP-PG-0514-20018, J.O.G., D.M.L., R.B. and H.R.), the UK Antimicrobial Resistance Cross Council Initiative (no. MR/N013956/1, J.O.G. and G.L.K.), Rosetrees Trust (no. A749, J.O.G.), the University of East Anglia (to J.O.G. and T.C.), Oxford Nanopore Technologies (to J.O.G., T.C., A.A. and D.J.T.), the Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Microbes in the Food Chain BB/R012504/1 and its constituent projects BBS/E/F/000PR10348 and BBS/E/F/000PR10349 (J.O.G., J.W. and G.L.K.), MRC Doctoral Antimicrobial Research Training (DART) Industrial CASE Programme grant number MR/R015937/1 (J.O.G. and A.A.) and BBSRC grants (nos. BB/N023196/1 and BB/CSP17270/1, to R.M.L.). Part of the bioinformatics analysis was run on CLIMB-computing servers, an infrastructure supported by a grant from the UK Medical Research Council (no. MR/L015080/1).

Author information

The study was devised by J.O.G., J.W. and D.J.T. Laboratory work and data analysis were performed by T.C., G.L.K., A.A., H.R., R.B., D.M.L., R.M.L. and J.O.G. Clinical samples were collected and analyzed by C.J., S.G. and D.R. All authors contributed to writing and reviewing the manuscript.

Correspondence to Justin O’Grady.

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Competing interests

J.O.G., R.M.L., G.L.K. and T.C. received financial support for attending ONT and other conferences and/or an honorarium for speaking at ONT headquarters. J.O.G., A.A. and T.C. received funding and consumable support from ONT for PhD studentships. D.J.T. is a full-time employee and share-option holder of Oxford Nanopore Technologies Ltd. R.M.L. and J.O.G. received free flow cells as part of the MAP and MARC programs.

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Fig. 1: Schematic representation of the metagenomic pipeline.
Fig. 2: Bacterial genome assembly, genome coverage and antibiotic gene detection with depleted versus undepleted samples.