No abstract

Whole-genome sequencing (WGS) is rapidly becoming the method of choice for outbreak investigations and public health surveillance of microbial pathogens. The combination of improved cluster resolution and prediction of resistance and virulence phenotypes provided by a single tool is extremely advantageous. However, the data produced are complex, and standard bioinformatics pipelines are required to translate the output into easily interpreted epidemiologically relevant information for public health action. The main aim of this study was to validate the implementation of WGS at the Scottish O157/STEC Reference Laboratory (SERL) using the Public Health England (PHE) bioinformatics pipeline to produce standardized data to enable interlaboratory comparison of results generated at two national reference laboratories. In addition, we evaluated the BioNumerics whole-genome multilocus sequence typing (wgMLST) and genotyping plug-in tools using the same data set. A panel of 150 well-characterized isolates of Shiga toxin-producing (STEC) that had been sequenced and analyzed at PHE using the PHE pipeline and database (SnapperDB) was assembled to provide identification and typing data, including serotype (O:H type), sequence type (ST), virulence genes ( and Shiga toxin [] subtype), and a single-nucleotide polymorphism (SNP) address. To validate the implementation of sequencing at the SERL, DNA was reextracted from the isolates and sequenced and analyzed using the PHE pipeline, which had been installed at the SERL; the output was then compared with the PHE data. The results showed a very high correlation between the data, ranging from 93% to 100%, suggesting that the standardization of WGS between our reference laboratories is possible. We also found excellent correlation between the results obtained using the PHE pipeline and BioNumerics, except for the detection of and when these subtypes are both carried by strains.

Background The Alpha (B.1.1.7) SARS-CoV-2 variant of concern has been associated with increased transmission and increased 28-day mortality. We aimed to investigate the impact of infection on clinical severity of illness, including the need for oxygen or ventilation in a national cohort study. Methods In this prospective clinical cohort study, 1475 SARS-CoV-2 sequences were obtained from patients infected in Scotland, UK between the 1st November 2020 and 30th January 2021 and matched to clinical outcomes as the lineage became dominant in Scotland. We modelled the association between B.1.1.7 infection and severe disease using a cumulative generalised linear mixed model employing a 4-point scale of maximum severity based on requirement of respiratory support at 28 days. We also estimated the growth rate of B.1.1.7-associated infections as it emerged in Scotland using a phylogenetic exponential growth rate population model. Results The B.1.1.7 lineage was responsible for a third wave of SARS-CoV-2 infection in Scotland in association with a transmission rate 5-fold higher than the preceding second wave B.1.177 lineage. Of 1475 patients, 364 were infected with B.1.1.7, 1030 with B.1.177 and 81 with other lineages. Our analysis found a positive association between increased clinical severity and lineage (B.1.1.7 versus non-B.1.1.7; cumulative odds ratio: 1.40, 95% CI: 1.02, 1.93). Viral load was higher in B.1.1.7 samples than in non-B.1.1.7 samples, as measured by cycle threshold (Ct) value (mean Ct change: -2.46, 95% CI: -4.22, -0.70). Conclusions The B.1.1.7 lineage was associated with more severe clinical disease in Scottish patients than co-circulating lineages.

No abstract