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SUMMARY Background Enterovirus D68 (EV-D68) is implicated in a widespread 2014 outbreak of severe respiratory illness across the United States, and has also been sporadically reported in patients with acute flaccid myelitis (AFM). The association between EV-D68 infection and AFM remains unclear. Methods Here we report metagenomic and molecular epidemiological analyses of 25 AFM cases in California and Colorado from 2012−2014. Findings EV-D68 was detected in respiratory secretions from 7 of 11 (64%) patients comprising two temporally and geographically linked AFM clusters at the height of the 2014 outbreak, and from 12 of 25 (48%) investigated AFM cases overall. Phylogenetic analysis revealed that all AFM-associated EV-D68 sequences grouped into a single novel clade B1 strain that originally emerged in 2010. Out of six observed coding polymorphisms in the clade B1 EV-D68 polyprotein, 5 of 6 polymorphisms were shared between neuropathogenic poliovirus and/or EV-D70. One child with AFM and a sibling with only upper respiratory illness were both infected by identical EV-D68 strains, suggesting a potential role for host-specific factors in differential responses to EV-D68 infection. Notably, EV-D68 viremia was identified in a child experiencing acute neurologic progression of his paralytic illness. Deep metagenomic sequencing of CSF from 14 AFM cases failed to reveal evidence of an alternative infectious etiology to EV-D68. Interpretation Taken together, these findings strengthen the putative association between EV-D68 and AFM, as well as the contention that AFM is a rare yet severe clinical manifestation of EV-D68 infection in susceptible hosts.

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (''sequence-based ultrarapid pathogen identification''), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.

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 107–108 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 × 105 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0220-9) contains supplementary material, which is available to authorized users.