An optimised protocol for detection of SARS-COV-2 in stool (preprint)

AimSARS-CoV-2 has been detected in stool samples of COVID-19 patients, with potential implications for faecal-oral transmission. Compared to swab samples, the complexity of the stool matrix poses a challenge in the detection of the virus that has not yet been solved. The aim of this study was to establish a sensitive and reliable method for detecting SARS-CoV-2 in stool samples. MethodsStool samples from individuals free of SARS-CoV-2 were homogenised in saline buffer and spiked with a known titre of inactivated virus ranging from 50 to 750 viral particles per 100 mg stool. Debris was removed via centrifugation and supernatants were concentrated by ultrafiltration. RNA was then extracted from the concentrated material using a commercial kit and SARS-CoV-2 was detected via real-time reverse-transcription polymerase chain reaction (RT-qPCR) using the CDC primers and probes. ResultsThe RNA extraction procedure we used allowed the detection of SARS-CoV-2 via RT-qPCR in most of the stool samples tested. We could detect as few as 50 viral particles per 100 mg of stool. However, high variability was observed across samples at low viral titres. The primer set targeting the N1 region provided more reliable and precise results and for this primer set our method had a limit of detection of 1 viral particle per mg of stool. ConclusionsHere we describe a sensitive method for detecting SARS-CoV-2 in stool samples. This method can be used to establish the persistence of SARS-CoV-2 in stool and ensure the safety of clinical practices such as faecal microbiota transplant (FMT).

Large scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management (preprint)

The COVID-19 pandemic has spread rapidly throughout the world. In the UK, the initial peak was in April 2020; in the county of Norfolk (UK) and surrounding areas, which has a stable, low-density population, over 3,200 cases were reported between March and August 2020. As part of the activities of the national COVID-19 Genomics Consortium (COG-UK) we undertook whole genome sequencing of the SARS-CoV-2 genomes present in positive clinical samples from the Norfolk region. These samples were collected by four major hospitals, multiple minor hospitals, care facilities and community organisations within Norfolk and surrounding areas. We combined clinical metadata with the sequencing data from regional SARS-CoV-2 genomes to understand the origins, genetic variation, transmission and expansion (spread) of the virus within the region and provide context nationally. Data were fed back into the national effort for pandemic management, whilst simultaneously being used to assist local outbreak analyses. Overall, 1,565 positive samples (172 per 100,000 population) from 1,376 cases were evaluated; for 140 cases between two and six samples were available providing longitudinal data. This represented 42.6% of all positive samples identified by hospital testing in the region and encompassed those with clinical need, and health and care workers and their families. 1,035 cases had genome sequences of sufficient quality to provide phylogenetic lineages. These genomes belonged to 26 distinct global lineages, indicating that there were multiple separate introductions into the region. Furthermore, 100 genetically-distinct UK lineages were detected demonstrating local evolution, at a rate of ~2 SNPs per month, and multiple co-occurring lineages as the pandemic progressed. Our analysis: identified a sublineage associated with 6 care facilities; found no evidence of reinfection in longitudinal samples; ruled out a nosocomial outbreak; identified 16 lineages in key workers which were not in patients indicating infection control measures were effective; found the D614G spike protein mutation which is linked to increased transmissibility dominates the samples and rapidly confirmed relatedness of cases in an outbreak at a food processing facility. The large-scale genome sequencing of SARS-CoV-2-positive samples has provided valuable additional data for public health epidemiology in the Norfolk region, and will continue to help identify and untangle hidden transmission chains as the pandemic evolves.

CoronaHiT: large scale multiplexing of SARS-CoV-2 genomes using Nanopore sequencing (preprint)

The COVID-19 pandemic has spread to almost every country in the world since it started in China in late 2019. Controlling the pandemic requires a multifaceted approach including whole genome sequencing to support public health interventions at local and national levels. One of the most widely used methods for sequencing is the ARTIC protocol, a tiling PCR approach followed by Oxford Nanopore sequencing (ONT) of up to 24 samples at a time. There is a need for a higher throughput method to reduce cost per genome. Here we present CoronaHiT, a method capable of multiplexing up to 95 small genomes on a single Nanopore flowcell, which uses transposase mediated addition of adapters and PCR based addition of symmetric barcodes. We demonstrate the method using 48 and 94 SARS-CoV-2 genomes per flowcell, amplified using the ARTIC protocol, and compare performance with Illumina and ARTIC ONT sequencing. Results demonstrate that all sequencing methods produce inaccurate genomes when the RNA extract from SARS-CoV-2 positive clinical sample has a cycle threshold (Ct) >= 32. Results from set same set of 23 samples with a broad range of Cts show that the consensus genomes have >90% coverage (GISAID criteria) for 78.2% of samples for CoronaHiT-48, 73.9% for CoronaHiT-94, 78.2% for Illumina and 73.9% for ARTIC ONT, and all have the same clustering on a maximum likelihood tree. In conclusion, we demonstrate that CoronaHiT can multiplex up to 94 SARS-CoV-2 genomes per nanopore flowcell without compromising the quality of the resulting genomes while reducing library preparation complexity and significantly reducing cost. This protocol will aid the rapid expansion of SARS-CoV-2 genome sequencing globally, to help control the pandemic.