COVID-19

University of Sheffield scientists are contributing to the fight against coronavirus.

Microscopic illustration of the coronavirus that was discovered in Wuhan, China - iimage is an artisic but scientific interpretation, with all relevant surface details of this particular virus in place
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After sequencing the whole-genomes of SARS-CoV-2 from two local COVID-19 positive patient samples1, University of Sheffield researchers are now involved in generating and sharing sequence data as part of the  COVID-19 Genomics UK (COG-UK) – Consortium2.

COG-UK Consortium

The Government, NHS, Public Health England, UK Research and Innovation and the Wellcome Trust are collectively investing £20 million into sequencing the UK SARS-CoV-2 epidemic at institutions across the country2.

This money will drive the sequencing capabilities of Public Health England, Wellcome Sanger Institute and centres in Belfast, Birmingham, Cambridge, Cardiff, Edinburgh, Exeter, Glasgow, Liverpool, London, Norwich, Nottingham, Oxford and Sheffield.

At the University of Sheffield, the ARTIC pipeline will be used to sequence the SARs-CoV-2 virus, using protocols and bioinformatics based on the Oxford Nanopore Technologies fourth-generation sequencers3.

COG-UK expect to generate thousands of SARS-CoV-2 genomes per week and the resulting genomic data will be used to map out the epidemiology and phylogeny of the virus.

The results from sequencing across UK centres will be condensed and reported to the government weekly, to provide information on virus progression and the potential impact of public health measures to limit the spread of SARS-CoV-2 in the UK.

These enhanced capabilities will remain in place for future research and pandemic preparedness.

The University of Sheffield team

Management

  • Thushan de Silva, Senior Clinical Lecturer and Honorary Consultant Physician in Infectious Diseases from the University of Sheffield’s Infection, Immunity and Cardiovascular Diseases (IICD) Department, is leading a team of individuals from across IICD, the Sheffield Institute for Translational Neuroscience (SITraN), The Department of Computer Science and Molecular Biology and Biotechnology (MBB), including the Department of Animal and Plant Sciences (APS).
  • Matthew Parker from SITraN and the University of Sheffield's Bioinformatics Core is leading the data organisation and analysis, locally.
  • Nikki Smith from IICD is acting as project manager for the Sheffield COG-UK team, supported by Katie Cooke from the IICD technician team.

Diagnostics

  • Consultant Virologists Cariad EvansMohammad Raza and Alison Cope are leading the testing of COVID-19 patients and staff at the Sheffield Teaching Hospitals (STH) NHS Foundation Trust Virology Department, and collaborating on this project.
  • Alex Keeley, an Academic Clinical Fellow in Infectious Diseases and Dave Partridge, Consultant Microbiologist, are coordinating the use of patient samples for sequencing, managing the corresponding clinical data, along with the STH IT department.

Sequencing and Analysis

  • Sheffield Institute of Translational Neuroscience/Sheffield Biomedical Research Centre: Matthew Wyles oversees the running of the UoS Next Generation Sequencing core facility, and has facilitated the upscaling of sequencing capabilities at the IICD Department. In addition to Matthew Parker and Dennis Wang (Departments of Computer Science and Neuroscience), Bioinformatics core director Mark Dunning and PhD researchers Sokratis Karitois and Katjusa Koler, have helped to develop the bioinformatics pipelines and interfaces which will be used by the University of Sheffield.
  • IICD: Post-Doctoral Research Associates Adri Angyal, Rebecca Brown and Luke Green have been leading the laboratory work, along with research technicians Danielle Groves and Hailey HornsbyBen Lindsey, an Academic Clinical Fellow in Infectious Diseases and Virology is helping with local phylogenetic analysis.
  • APS: Senior Research Technicians Rachel Tucker and Paul Parsons have kindly joined the team after the offer of help from Prof. Terry Burke, and are helping to scale up the process to generate data for up to 230 genomes per week.

IT Support

  • IT Services: IT services are providing infrastructure and high performance computing capabilities which enable analysis for the project to be performed. 
  • Department of Computer Science: Computer science have provided IT equipment and expertise through Dennis Wang to allow researchers on the project complete the complex analysis required to produce meaningful results from the viral sequencing data.

How does it work?

Patient samples are taken, most commonly from the nose or throat, before they are purified and the RNA is amplified to identify the genes unique to coronavirus4.  

The COVID-19 genome is encoded by RNA and is approximately 30,000 bases in length. RNA samples are converted to complementary DNA (cDNA) using a reverse transcriptase enzyme. cDNA is amplified into short overlapping DNA fragments repeatedly by polymerase chain reaction (PCR)5. Genome sequencing requires many copies of DNA to produce many iterations of the genome. The number of times a particular area of the genome has been sequenced is known as the ‘coverage’, and the greater the coverage the more confidence we have in the output.

Up to 23 genomes can be sequenced in one run by adding specific barcodes to the overlapping DNA sequences. Adaptor sequences are also added to ensure the DNA passes through the pores of the MinION sequencer (https://nanoporetech.com/how-it-works). As the DNA passes through the pore this causes a disruption in the electric current running through the solution, the resulting electrograms can be converted by an algorithm to give the sequence of nucleic acids5. The sequences are separated by barcode and aligned to previously sequenced COVID-19 sequences using Read Assignment, Mapping, and Phylogenetic Analysis in Real Time (RAMPART)6

The sequence data generated will be uploaded to https://www.gisaid.org/ and other public data repositories for sharing with the wider scientific community7.

Big Picture

Viral genomes are constantly accumulating mutations and evolving. Tracking these changes can build a picture of the spread of the virus, identifying transmission chains, including estimates of asymptomatic spread8.

This information could be used retrospectively to identify how the SARs-CoV-2 virus entered the human population and its subsequent evolution. The phylogeny of the virus is being mapped out on https://nextstrain.org/ncov9 and http://virological.org/10 and could be used to identify evolutionary drivers, such as the impact of public health interventions, natural immunity or treatment interventions11.

COVID-19 Map
COVID-19 chart

References

  1. Whole genomes of coronavirus from UK patients sequenced by Sheffield scientists - Latest - News - The University of Sheffield. Available at: https://www.sheffield.ac.uk/news/nr/coronavirus-genomes-sequenced-sheffield-1.883916. (Accessed: 2nd April 2020)
  2. UK launches whole genome sequence alliance to map spread of coronavirus - GOV.UK. Available at: https://www.gov.uk/government/news/uk-launches-whole-genome-sequence-alliance-to-map-spread-of-coronavirus. (Accessed: 2nd April 2020)
  3. Artic Network. Available at: https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html. (Accessed: 1st April 2020)
  4. WHO. World Health Organization. Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases. Interim Guid. 1–7 (2020).
  5. Quick, J., Barnes, K. & Quick, J. nCoV-2019 sequencing protocol. 1–24 (2020).
  6. GitHub - artic-network/rampart: Read Assignment, Mapping, and Phylogenetic Analysis in Real Time. Available at: https://github.com/artic-network/rampart. (Accessed: 1st April 2020)
  7. Shu, Y. & McCauley, J. GISAID: Global initiative on sharing all influenza data – from vision to reality. Eurosurveillance 22, (2017).
  8. Grubaugh, N. D. et al. Tracking virus outbreaks in the twenty-first century. Nature Microbiology 4, 10–19 (2019).
  9. Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
  10. Phylodynamic Analysis | 176 genomes | 6 Mar 2020 - Novel 2019 coronavirus / nCoV-2019 Genomic Epidemiology - Virological. Available at: http://virological.org/t/phylodynamic-analysis-176-genomes-6-mar-2020/356. (Accessed: 1st April 2020)
  11. Grenfell, B. T. et al. Unifying the Epidemiological and Evolutionary Dynamics of Pathogens. Science 303, 327–332 (2004).

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