Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis

Abstract

We report unbiased metagenomic detection of chikungunya virus (CHIKV), Ebola virus (EBOV), and hepatitis C virus (HCV) from four human blood samples by MinION nanopore sequencing coupled to a newly developed, web-based pipeline for real-time bioinformatics analysis on a computational server or laptop (MetaPORE). At titers ranging from 10(7)-10(8) copies per milliliter, reads to EBOV from two patients with acute hemorrhagic fever and CHIKV from an asymptomatic blood donor were detected within 4 to 10 min of data acquisition, while lower titer HCV virus (1 × 10(5) copies per milliliter) was detected within 40 min. Analysis of mapped nanopore reads alone, despite an average individual error rate of 24 % (range 8-49 %), permitted identification of the correct viral strain in all four isolates, and 90 % of the genome of CHIKV was recovered with 97-99 % accuracy. Using nanopore sequencing, metagenomic detection of viral pathogens directly from clinical samples was performed within an unprecedented <6 hr sample-to-answer turnaround time, and in a timeframe amenable to actionable clinical and public health diagnostics.

Fig. 1

Metagenomic sequencing workflow for MinION nanopore sequencing compared to Illumina MiSeq sequencing. a Overall workflow. b Steps in the MetaPORE real-time analysis pipeline. The turnaround time for sample-to-detection nanopore sequencing, defined here as the cumulative time taken for nucleic acid extraction, reverse transcription, library preparation, sequencing, MetaPORE bioinformatics analysis, and pathogen detection, was under 6 hr, while Illumina sequencing took over 20 hr. The time differential is accounted for by increased times for library quantitation, sequencing, and bioinformatics analysis with the Illumina protocol. *Assumes a 12-hr 50-bp single-end MiSeq run of ~12–15 million reads, with 50 bp the minimum estimated read length needed for accurate pathogen identification. **Denotes estimated average SURPI bioinformatics analysis run length for MiSeq data [19]. The stopwatch is depicted as a 12-hr clock

Fig. 2

Metagenomic identification of CHIKV and EBOV from clinical blood samples by nanopore sequencing. a Time line of sequencing runs on flow cell #1 with sample reloading, plotted as a function of elapsed time in hours since the start of flow cell sequencing. b Cumulative numbers of all sequenced reads (black line) and target viral reads (red line) from the Chik1 run (left panel) and Ebola1 run (right panel), plotted as a function of individual sequencing run time in minutes. c Taxonomic donut charts generated using the MetaPORE bioinformatics analysis pipeline from the Chik1 run (left panel) and Ebola1 run (right panel). The total number of reads analyzed is shown in the center of the donut. d Coverage plots generated in MetaPORE by mapping reads aligning to CHIKV (left, Chik1 run) or EBOV (right, Ebola1 run) to the closest matching reference genome ((e), asterisk). A corresponding pairwise identity plot is also shown for CHIKV, for which there is sufficient coverage. e Whole-genome phylogeny of CHIKV. Representative CHIKV genome sequences from the Asian-Pacific clade, including the Puerto Rico PR-S6 strain recovered by nanopore and MiSeq sequencing, or all available 188 near-complete or complete CHIKV genomes (inset), are included. Branch lengths are drawn proportionally to the number of nucleotide substitutions per position, and support values are shown for each node. were was analyzed in MetaPORE on a 64-core Ubuntu Linux server using the June 2014 and January 2015 NT databases as the reference databases for the CHIKV and EBOV samples, respectively

Fig. 3

MetaPORE analysis of Illumina MiSeq data from samples containing CHIKV and EBOV. Taxonomic donut charts were generated from Illumina MiSeq data corresponding to the Chik1 run (a) and Ebola1 run (b) using the MetaPORE bioinformatics analysis pipeline. The total number of MiSeq reads analyzed is shown in the center of the donut. Note that given computational time constraints, only a subset of reads (n = 100,000) was analyzed using MetaPORE. Coverage and pairwise identity plots were generated from MiSeq CHIKV reads from the Chik1 sample (248,677 of 3,235,099 reads, 7.7 %) (c), or EBOV reads from the Ebola1 sample (20,820 of 2,743,589 reads, 0.76 %)  (d), identified using SURPI analysis and LASTZ mapping {Harris, 2007 #34} at an e-value of 10-5 to the closest matching reference genome. Data were analyzed in MetaPORE on a 64-core Ubuntu Linux server using the June 2014 and January 2015 NT databases as the reference databases for the CHIKV and EBOV samples, respectively.

Fig. 4

Metagenomic identification of HCV from a clinical serum sample by nanopore sequencing. a Time line of sequencing runs on flow cell #2 with HepC1 sample reloading, plotted as a function of elapsed time in hours since the start of flow cell sequencing. b Cumulative number of all sequenced reads (black line) and HCV viral reads (red line), plotted as a function of individual sequencing run time in minutes. c Taxonomic donut charts generated using the MetaPORE bioinformatics analysis pipeline. The total number of reads analyzed is shown in the center of the donut. d Coverage and pairwise identity plots generated in MetaPORE by mapping reads aligning to HCV to the closest matching reference genome. Data were analyzed in MetaPORE on a 64-core Ubuntu Linux server using the January 2015 NT reference database

Fig. 5

Metagenomic identification of EBOV from a clinical blood sample by nanopore sequencing and MetaPORE real-time bioinformatics analysis. Nanopore data generated from the Ebola2 library and sequenced on flow cell #3 were analyzed in real time using the MetaPORE bioinformatics analysis pipeline, and compared to corresponding Illumina MiSeq data. a Time line of nanopore sequencing runs on flow cell #3 with sample reloading, plotted as a function of elapsed time in hours since the start of flow cell sequencing. b Cumulative numbers of all sequenced reads (black line) and target viral reads (red line) from the nanopore run (left panel) or MiSeq run (right panel), plotted as a function of individual sequencing run time in minutes. c Taxonomic donut charts generated by real-time MetaPORE analysis of the nanopore reads (left panel) and post-run analysis of the MiSeq reads (right panel). The total number of reads analyzed is shown in the center of the donut. Note that given computational time constraints, only a subset of MiSeq reads (n = 100,000) was analyzed using MetaPORE. d Coverage and pairwise identity plots generated from nanopore (left panel) or MiSeq data (right panel) by mapping reads aligning to EBOV to the closest matching reference genome ((e), asterisk).  e Whole-genome phylogeny of EBOV. Representative EBOV genome sequences, including those from the 2014-2015 West Africa outbreak (tan) and 2014 DRC outbreak (pink), are included. Branch lengths are drawn proportionally to the number of nucleotide substitutions per position, and support values are shown for each node. Data were analyzed in MetaPORE on a 64-core Ubuntu Linux server using the January 2015 NT reference database.