An outbreak of betacoronavirus severe acute respiratory syndrome (SARS)-CoV-2 began in Wuhan, China in December 2019. COVID-19, the disease associated with SARS-CoV-2 infection, rapidly spread to produce a global pandemic. We report development of a rapid (<40 min), easy-to-implement and accurate CRISPR-Cas12-based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated our method using contrived reference samples and clinical samples from patients in the United States, including 36 patients with COVID-19 infection and 42 patients with other viral respiratory infections. Our CRISPR-based DETECTR assay provides a visual and faster alternative to the US Centers for Disease Control and Prevention SARS-CoV-2real-time RT-PCR assay, with 95% positive predictive agreement and 100% negative predictive agreement.

Recent reports of mild to severe influenza-like illness in humans caused by a novel swine-origin 2009 A(H1N1) influenza virus underscore the need to better understand the pathogenesis and transmission of these viruses in mammals. In this study, selected 2009 A(H1N1) influenza isolates were assessed for their ability to cause disease in mice and ferrets and compared with a contemporary seasonal H1N1 virus for their ability to transmit to naïve ferrets through respiratory droplets. In contrast to seasonal influenza H1N1 virus, 2009 A(H1N1) influenza viruses caused increased morbidity, replicated to higher titers in lung tissue, and were recovered from the intestinal tract of intranasally inoculated ferrets. The 2009 A(H1N1) influenza viruses exhibited less efficient respiratory droplet transmission in ferrets in comparison with the highly transmissible phenotype of a seasonal H1N1 virus. Transmission of the 2009 A(H1N1) influenza viruses was further corroborated by characterizing the binding specificity of the viral hemagglutinin to the sialylated glycan receptors (in the human host) by use of dose-dependent direct receptor-binding and human lung tissue-binding assays.

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.

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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.