Project description:As the year 2020 came to a close, several new strains have been reported of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent responsible for the coronavirus disease 2019 (COVID-19) pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the pandemic. Availability: The tool used to perform the reported mutation analysis results, ntEdit, is available from GitHub. Genome mutation reports are available for download from BCGSC. Mutation time maps are available from https://bcgsc.github.io/SARS2/.
Project description:As the year 2020 draws to an end, several new strains have been reported for the SARS-CoV-2 coronavirus, the agent responsible for the COVID-19 pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the year. Availability: Mutation time maps are available from https://bcgsc.github.io/SARS2/.
Project description:With the continued spread of SARS-CoV-2 virus around the world, researchers often need to quickly identify novel mutations in newly sequenced SARS-CoV-2 genomes for studying the molecular evolution and epidemiology of the virus. We have developed a Python package, MicroGMT, which takes either raw sequence reads or assembled genome sequences as input and compares against database sequences to identify and characterize indels and point mutations. Although our default setting is optimized for SARS-CoV-2 virus, the package can be also applied to any other microbial genomes. The software is freely available at Github URL https://github.com/qunfengdong/MicroGMT.
Project description:Due to the COVID-19 pandemic the virus responsible, SARS-CoV-2, became a source of intense interest for non-expert audiences. The viral spike protein gained particular public interest as the main target for protective immune responses, including those elicited by vaccines. The rapid evolution of SARS-CoV-2 resulted in variations in the spike that enhanced transmissibility or weakened vaccine protection. This created new variants of concern (VOCs). The emergence of VOCs was studied using viral sequence data which was shared through portals such as the online Mutation Explorer of the COVID-19 Genomics UK consortium (COG-UK/ME). This was designed for an expert audience, but the information it contained could be of general interest if suitably communicated. Visualisations, interactivity and animation can improve engagement and understanding of molecular biology topics, and so we developed a graphical educational resource, the SARS-CoV-2 Spike Protein Mutation Explorer (SSPME), which used interactive 3D molecular models and animations to explain the molecular biology underpinning VOCs. User testing showed that the SSPME had better usability and improved participant knowledge confidence and knowledge acquisition compared to COG-UK/ME. This demonstrates how interactive visualisations can be used for effective molecular biology communication, as well as improving the public understanding of SARS-CoV-2 VOCs.
Project description:There is an urgent need to understand the functional effects of mutations in emerging variants of SARS-CoV-2. Variants of concern (alpha, beta, gamma and delta) acquired four patterns of spike glycoprotein mutations that enhance transmissibility and immune evasion: 1) mutations in the N-terminal domain (NTD), 2) mutations in the Receptor Binding Domain (RBD), 3) mutations at interchain contacts of the spike trimer, and 4) furin cleavage site mutations. Most distinguishing mutations among variants of concern are exhibited in the NTD, localized to sites of high structural flexibility. Emerging variants of interest such as mu, lambda and C.1.2 exhibit the same patterns of mutations as variants of concern. There is a strong likelihood that SARS-CoV-2 variants will continue to emerge with mutations in these defined patterns, thus providing a basis for the development of next line antiviral drugs and vaccine candidates.
Project description:hACE2 transgenic mice were infected with the original SARS-CoV-2 strain (B.1) and the Beta (B.1.351) variant. Lung and spleen samples were collected 1 day post infection (DPI), 3 DPI and 5 DPI, and mRNA was sequenced.
Project description:The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.