Report

Report 11: 8th September 2020 – COVID-19 Genomics UK (COG-UK) Consortium

Please Note: This report is provided at the request of SAGE and includes information on the ongoing state of the research being carried out. It should not be considered formal or informal advice. The conclusions of the ongoing scientific studies may be subject to change as further evidence becomes available and as such any firm conclusions would be premature.

Executive Summary

  • COG-UK researchers have collaborated with PHAs to use rapid genome sequencing to understand the dynamics underpinning a growing number of local SARS-CoV-2 outbreaks across the UK during the summer.
  • Rapid genome sequencing coupled with integration of epidemiological data has enabled the identification of transmission points and informed intervention measures during outbreaks among highly vulnerable patients in renal dialysis units (RDUs) in Scotland and the East of England.
  • In addition to existing COG-UK tools and pipelines, a newly developed sequence reporting tool that has been applied to the RDU outbreaks will provide simple statistical support to public health workers and clinicians seeking to understand whether infections seen at a local level represent transmissions within a given healthcare setting or transmission from the surrounding community.

 

COG-UK genomic surveillance in action

The sequencing pipelines, genomic data and tools that have been created during the six months since the establishment of COG-UK provide the foundations necessary for the consortium to transition into a new phase, that continues longer-term scientific research and development activities but increasingly also moves towards providing support for the operational genomic surveillance of local SARS-CoV-2 outbreaks and actionable feedback to infection management teams handling live outbreaks.

Accordingly, PHAs have been working to with COG-UK researchers to apply the national dataset to the investigation of situations and outbreaks and to identify the needs for analytic tool development. Further work is needed to strengthen and expand this work, and to ensure that data and metadata are available at a pace which can provide relevant insights to inform the local and national response.

The COG-UK phylogenetics pipeline based on the CLIMB infrastructure provides tools for public health workers to rapidly make assessments of potential outbreaks based on all COG-UK genome sequences and associated metadata. The Civet tool (see ‘Summary of major tools and pipelines developed by COG-UK’ COG-UK report #10 – 11th August) provides clear analytical reports that can put putative outbreaks in context, find links between clusters, and allows secure integration of private data about individual cases. An example of an analytic report from Civet is included in the Appendix.

For hospital linked outbreaks the ‘Sequence Reporting Tool’ (SRT; see ‘Development of a Sequence Reporting Tool’ COG-UK report #10 – 11th August) uses a probability model to integrate viral genome sequences and patient metadata to provide a simple statistical metric that can inform on the likelihood of a particular infection owing to transmission within a ward, in the wider hospital, or in the community. The SRT has been applied for the first time to an analysis of potential outbreaks in RDUs in Scotland (see ‘Using genomic epidemiology to tackle SARS-CoV-2 outbreaks in Renal Dialysis Units in Scotland and the East of England’, below).

Public Health Wales (PHW) has integrated the genomic data and analysis generated for Welsh sequences into outbreak responses, as a requestable service provided to epidemiologists within the NHS in Wales. In North Wales, genomic data is integrated into a real-time surveillance system which enables the analysis and comparison of cases in the community and hospitals. This tool has been used to examine hospital outbreaks, and to define the extent of outbreaks in hospitals in Wales (see ‘Integration of genomics and clinical metadata in real time to support outbreak management and response’ COG-UK report #9 – 25th June). More generally, within PHW, the outputs of COG-UK are fed to a network of Healthcare Epidemiologists who make use of a combination of MicroReact visualisations (running on a MicroReact instance within PHW), lineage assignments/phylotypes and trees to support local outbreak investigations. This enables the rapid/real time use of genomics as soon as an outbreak is identified. Where more substantial outbreaks are concerned, detailed analysis and interpretation is performed by the Pathogen Genomics team within PHW, with genomics reports being fed back to the relevant outbreak management team in real time.

In addition to the use of data within PHW to identify outbreaks, PHW also generates four indicators from genomic data in close-to-real-time which feed into the Welsh Government COVID-19 circuit breakers and early warning indicators (https://gov.wales/covid-19-circuit-breakers-and-early-warning-indicators).


 

Analysis updates

Using genomic epidemiology to tackle SARS-CoV-2 outbreaks in Renal Dialysis Units in Scotland and the East of England.

Study leads

Emma Thomson1,3, Patrick Mark2,3, Kathy Li1, Joseph Hughes1, Ben Warne4,5, Will Hamilton4,5, Ian Goodfellow6, Estée Török4,5
1. MRC-University of Glasgow Centre for Virus Research
2. Institute of Cardiovascular and Medical Sciences, University of Glasgow
3. NHS Greater Glasgow & Clyde
4. University of Cambridge Department of Medicine
5. Cambridge University Hospitals NHS Foundation Trust
6. University of Cambridge Division of Virology

Question addressed

Renal dialysis patients are among the most vulnerable to COVID-19 (with up to 30% mortality; https://renal.org/covid-19/data/ and Table 1, below). Renal dialysis units (RDUs) may provide a conduit between hospitals and the community environment owing to prolonged episodes of outpatient medical care. Most RDUs consist of large open rooms with no barriers separating patients, thus providing challenges for infection control. Furthermore, shared hospital transport and admission into other hospital units for co-morbid conditions may increase the risk of acquisition of infection. Studies in two cities (Glasgow and Cambridge) set out to determine how genomic epidemiology can be used to identify the likely source of SARS-CoV-2 infections in RDUs, understand the dynamics of transmission in potential outbreaks, assess the clinical impact on patients and inform infection control measures to protect this highly vulnerable group of patients.

Methodology

Electronic patient records were used to identify anonymised clinical data for RDU patients with a positive diagnostic test for SARS-CoV-2 infection. SARS-CoV-2 genomes were sequenced for each infection according to COG-UK protocols and sequence data uploaded to CLIMB and GISAID. Phylogenetic analysis of the whole genome sequences were performed using established approaches (see ‘Summary of major tools and pipelines developed by COG-UK’ COG-UK report #10 – 11th August). In the Scotland study, a novel ‘Sequence Reporting Tool’ was also used for estimating the probability that an infection was acquired in the community or in a healthcare setting (either hospital or specific RDU ward; see ‘Development of a Sequence Reporting Tool’, COG-UK report #10 – 11th August). Comparisons were made between patients who lived and died following infection.

Findings

Data from both cities are presented separately below, although the patterns observed and implications for infection control were similar across both sites.

Cambridge RDU outbreak

Figure 1: Phylogenetic tree of Cambridge University Hospitals NHS Foundation Trust samples showing dialysis unit cases in yellow.
Figure 1: Phylogenetic tree of Cambridge University Hospitals NHS Foundation Trust samples showing dialysis unit cases in yellow.

During a wider prospective genomic surveillance study of SARS-CoV-2 infections in patients at Cambridge University Hospitals NHS Foundation Trust (Ref 1), six patients with end-stage renal failure were diagnosed with SARS-CoV-2 between the 1st and 20th April 2020. These six patients were spread across several locations, including the emergency departments and an acute admissions ward. The patients’ diagnostic samples were tested at the PHE Clinical Microbiology and Public Health Laboratory and underwent nanopore sequencing in the Department of Virology, University of Cambridge. The SARS-CoV-2 genomes were found to cluster together on a phylogenetic tree (Figure 1) and were identical at the genomic level, with zero SNP differences between them, indicating the potential that a common source of infection was shared by these six patients.

Epidemiological investigation revealed that all six patients dialysed at the same outpatient RDU on the same days of the week (Figure 2), suggesting linked recent transmission of community-onset, healthcare-associated infections.

Figure 2: Cambridge University Hospitals NHS Foundation Trust RDU outbreak timeline. Sample dates are shown in yellow circles. Six patients with end-stage renal failure were diagnosed with COVID-19 between 1 and 20 April 2020 (yellow circle with a letter indicating the cluster number). Yellow circles without a letter indicate patients diagnosed with COVID-19 found not to be related to the dialysis unit clusters. Black triangles indicate patient deaths. The darker green blocks represent the dialysis unit with suspected transmission; the light green and grey blocks represent different dialysis units. The renal ward is shown in blue and the emergency department in red. Other wards are shown in grey.
Figure 2: Cambridge University Hospitals NHS Foundation Trust RDU outbreak timeline. Sample dates are shown in yellow circles. Six patients with end-stage renal failure were diagnosed with COVID-19 between 1 and 20 April 2020 (yellow circle with a letter indicating the cluster number). Yellow circles without a letter indicate patients diagnosed with COVID-19 found not to be related to the dialysis unit clusters. Black triangles indicate patient deaths. The darker green blocks represent the dialysis unit with suspected transmission; the light green and grey blocks represent different dialysis units. The renal ward is shown in blue and the emergency department in red. Other wards are shown in grey.

These findings led to a review of infection control procedures in the dialysis patients and identified shared patient transportation and neighbouring dialysis chairs as risk factors for transmission. Interventions included introduction of universal mask use for patients and staff (in particular during patient transportation), closure of the waiting room area, and improved social distancing measures.

Genomics was also found to be useful for “ruling out” linked transmission to other renal patients. For example, the renal ward (which shares patients with the outpatient dialysis unit) also had a group of COVID-19 cases at around the same time. However, the dialysis unit genomes belonged to lineage B.2 (relatively rare in the East of England), whereas the renal ward genomes were the common B.1 lineage, making it very unlikely that infections between the two patient groups were related.

Scotland RDU outbreak

Rapid whole-genome sequencing of SARS-CoV-2 generated by COG-UK was used to improve the understanding of transmission risks in high-risk renal haemodialysis cohorts at the six Scottish RDUs (Ref 2). Epidemiological, geographical, temporal and genetic sequence data from the community and hospital setting were analysed to estimate the probability of infection originating from within the dialysis unit, the wider hospital or the local community using Bayesian statistical modelling.

Of 671 patients, 60 (8.9%) became infected with SARS-CoV-2 and 16 (27%) died. 44 of the 60 patients were diagnosed as being infected with SARS-CoV-2 within a 14-day window of infection of another patient in the cohort attending the same dialysis shift/site, suggesting that within RDU transmission was potentially occurring while patients attended for dialysis, in addition to likely community spread outside of the RDU. Eight patients who were diagnosed with COVID-19 shared transport with another infected patient, during the same 14-day period of likely infectivity.

39 SARS-CoV-2 genome sequences from this cohort (plus one from a healthcare worker) were of sufficient quality for further analysis. 13 different UK SARS-CoV-2 lineages were detected. A number of patients were infected with near identical sequences from the same lineage, suggestive of linked transmission, while others did not cluster phylogenetically, suggesting community transmission. Epidemiological investigation identified clusters of SARS-CoV2 positive patients as sharing dialysis sessions and sometimes transport. Analysis of the phylogenetic and epidemiological data together using the novel ‘sequence reporting tool’ demonstrated that of the six RDUs, five look convincingly to have had unit-linked transmission events (Figure 3).

Figure 3: Timeline of detection of first SARS-CoV-2 positive results in Scottish haemodialysis patients in RDUs with details of dialysis sessions and shared patient transport in relation to the UK cluster. Circled number in the phylogenetic tree represents the number of identical sequences from Scotland for the given node on the phylogeny. The numerical suffixes of the CVR identifier indicate the posterior probability (as a percentage) of the patient acquiring SARS-CoV2 from the RDU or from another healthcare-related infection (i.e., hospital where dialysis takes place and ward and/or hospital they have been admitted to), respectively. The scale bar indicates substitutions per nucleotide site.
Figure 3: Timeline of detection of first SARS-CoV-2 positive results in Scottish haemodialysis patients in RDUs with details of dialysis sessions and shared patient transport in relation to the UK cluster. Circled number in the phylogenetic tree represents the number of identical sequences from Scotland for the given node on the phylogeny. The numerical suffixes of the CVR identifier indicate the posterior probability (as a percentage) of the patient acquiring SARS-CoV2 from the RDU or from another healthcare-related infection (i.e., hospital where dialysis takes place and ward and/or hospital they have been admitted to), respectively. The scale bar indicates substitutions per nucleotide site.

In RDU1, samples from seven patients and one healthcare worker fall within the UK40 lineage, with four sequences being identical to each other, was initially suggestive of within RDU transmission. However, lack of definitive epidemiological exposure and similarity to community sequences reduced confidence that the infections tool place in the RDU, with probabilities ranging from 0.53 to 0.68 (Figure 3). Early institution of masks during travel and dialysis for all patients in RDU1 was associated with a cessation of transmission, with the final case occurring nine days after enhanced PPE implementation.

RDU2 showed evidence of five SARS-CoV-2 introductions from the community resulting in the spread of two lineages within the RDU (or during transport to the unit) with 100% statistical probability. For one of the lineages, a patient with 100% probability of RDU transmission had separate dialysis sessions to the other two patients, suggesting an unknown transmission route (perhaps via an untested staff member).

RDUs 3-5 showed a mixture of community and RDU transmission events, albeit often via an unknown transmission route in the latter case. By contrast, 9 of the 16 patients from RDU6 for which a sequence was obtained were from the UK249 lineage and sequences were identical among patients who shared dialysis sessions. Statistical probabilities of within-unit transmission for infections in this RDU ranged from 0.85 to 0.97.

Key Conclusions

RDUs are high-risk environments for transmission of respiratory infections, with a high consequent risk of mortality (Table 1). Integration of rapid genome sequencing and epidemiology can identify multiple points at which transmission can occur, e.g. on the ward and on shared hospital transport. It can also be used to identify gaps in understanding of the transmission routes affecting an RDU. Interventions based on these insights can lead to cessation in transmission and should include reduction of risk in the ward setting, on hospital transport and within the community.

Table 1: COVID-19 incidence and mortality in RDU patients. UK Renal Registry; ERA-EDTA registry; Xiong et al JASN 2020; Alberici et al Kidney Int 2020; Valeri et al, JASN 2020; Fisher et al, Kidney360, 2020; Corbett et al, JASN 2020; Bell et al, MedRxiv 2020; Tortonese et al, KI Reports, 2020; Roper et al KI Reports 2020
Table 1: COVID-19 incidence and mortality in RDU patients. UK Renal Registry; ERA-EDTA registry; Xiong et al JASN 2020; Alberici et al Kidney Int 2020; Valeri et al, JASN 2020; Fisher et al, Kidney360, 2020; Corbett et al, JASN 2020; Bell et al, MedRxiv 2020; Tortonese et al, KI Reports, 2020; Roper et al KI Reports 2020

Discussion

Full viral genome sequences alone are not always sufficient to determine the source of a transmission, which will also depend on the frequency of different variants circulating in the community and in the hospital. As such, the incorporation of additional epidemiological information (such as dialysis session, shared transportation, information on negative screen and geographical data on cases in the community) can help to identify the source of transmissions in RDUs with confidence.

Furthermore, RDUs are representative of any hospital ward/clinic which necessitates that patients continue coming to hospital for a life-sustaining treatment, irrespective of quarantine or lockdown. The approaches developed for analysis of RDUs in these studies, only made possible by the fact that to stop dialysis would have led to the patients dying, could therefore be applied to monitoring and preventing SARS-CoV-2 transmission during chemotherapy and other life-sustaining treatments requiring hospital attendance.

Finally, while the approach used for integrating genomic and epidemiological data provides actionable information for investigating and tackling COVID-19 outbreaks, the SARS-CoV-2 virus has a relatively low evolutionary rate (approx. 0.8 x 10-3 substitutions/site/year). The genomic epidemiology analyses undertaken here would therefore be even more powerful for a virus with a higher evolutionary rate, such as influenza virus.

References

1. Meredith LW, Hamilton WL, Warne B, Houldcroft CJ, Hosmillo M, Jahun AS, Curran MD, Parmar S, Caller LG, Caddy SL, Khokhar FA, Yakovleva A, Hall G, Feltwell T, Forrest S, Sridhar S, Weekes MP, Baker S, Brown N, Moore E, Popay A, Roddick I, Reacher M, Gouliouris T, Peacock SJ, Dougan G, Török ME, Goodfellow I. Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study. Lancet Infect Dis. 2020 Jul 14: S1473-3099(20)30562-4. doi: 10.1016/S1473-3099(20)30562-4. PMID: 32679081.

2. Kathy K Li,Y. Mun Woo, , Antonia Ho1, Joseph Hughes, Oliver Stirrup, Alison H.M. Taylor, Zoe Cousland, Jonathan Price, Jennifer S. Lees, Timothy Jones, Carlo Varon Lopez, Scott T.W. Morris, Peter C. Thomson, Colin C Geddes, Jamie P. Traynor, Emma C. Thomson, Patrick B. Mark. Genetic epidemiology of COVID-19 infection in patients requiring haemodialysis renal replacement therapy in Scotland. Manuscript in progress. 2020


 

Appendix

Example analytical report from Civet

 

Example analytical report from Civet
Example analytical report from Civet
Tree 1 - 17 sequences of interest
Tree 1 – 17 sequences of interest
Figure 2 - Nucleotide variation in sequences of interest
Figure 2 – Nucleotide variation in sequences of interest
Figure 3 - The relative proportion of assigned UK-Lineages for samples collected and sequenced within the central healthboard region for the defined time-frame.
Figure 3 – The relative proportion of assigned UK-Lineages for samples collected and sequenced within the central healthboard region for the defined time-frame.
Figure 4 - The relative proportions of assigned UK-Lineages for samples collected from the focal, and neighbouring healthboard regions for the defined time-frame with the regional context. Plot-size demonstrates relative numbers of sequences across given NHS healthboards.
Figure 4 – The relative proportions of assigned UK-Lineages for samples collected from the focal, and neighbouring healthboard regions for the defined time-frame with the regional context. Plot-size demonstrates relative numbers of sequences across given NHS healthboards.
Appendix of Example analytical report from Civet
Appendix of Example analytical report from Civet

 

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Latest news

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    [secondary_title] => Read the Wellcome Sanger Insitute's Blog post about their work and watch their video
    [excerpt] => Read the Wellcome Sanger Insitute's Blog post about their work and watch their video
    [byline_text] => Blog post and video by the Sanger Institute, a COG-UK Consortium partner
    [byline_date] => 20201023
    [main_image] => 1221
    [teaser_image] => 
    [content] => In a <strong><a href="https://sangerinstitute.blog/2020/10/22/sequencing-covid-19-at-the-sanger-institute/" target="_blank" rel="noopener">blog post</a></strong> and <strong><a href="https://youtu.be/Fd40gunBTN0" target="_blank" rel="noopener">video</a></strong>, the Wellcome Sanger Institute takes you behind the scenes of its work to provide large-scale genome sequencing of thousands of COVID-19 samples as part of the COVID-19 Genomics UK (COG-UK) consortium.
To read the blog post, please click here: <strong><a href="https://sangerinstitute.blog/2020/10/22/sequencing-covid-19-at-the-sanger-institute/" target="_blank" rel="noopener">Sequencing COVID-19 at the Sanger Institute</a></strong>
The video is available below:
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Wellcome Sanger Institute
Blog23 Oct 2020

How a COG-UK Partner is helping to sequence tens of thousands of COVID-19 samples

Read the Wellcome Sanger Insitute's Blog post about their work and watch their video

Array
(
    [secondary_title] => Commentary for report 12 &ndash; 15th October 2020
    [excerpt] => As of 22nd October, the COVID-19 Genomics UK (COG-UK) Consortium has sequenced more than 81,000 SARS-CoV-2 virus genomes from the UK, representing about 45 per cent of the global total.
    [byline_text] => Commentary
    [byline_date] => 20201023
    [main_image] => 1225
    [teaser_image] => 
    [content] => As of 22<sup>nd</sup> October, the COVID-19 Genomics UK (COG-UK) Consortium has sequenced more than 81,000 SARS-CoV-2 virus genomes from the UK, representing about 45 per cent of the global total.
Report 12 presents details of three separate studies using the genome data, and a summary of nationwide outbreak investigations that have used COG-UK genome data.
The information shows the ongoing state of the research, and has not been peer-reviewed. Conclusions may change as more evidence becomes available.
The first study is into a genetic mutation of the virus, termed N439K. This mutation results in a change to the Spike protein of the virus – the protein it uses to enter human cells. Because of its location, this mutation could affect a person’s immune response to the virus. The report stresses the need for systematic monitoring of virus mutations, especially when vaccinations begin.
The second study details the use of viral genomic data in Norfolk, where the data has enabled local outbreak management. The third study assessed respiratory disease in cats, associated with human-to-cat transmission of SARS-CoV-2. Finally, there is a summary showing COG-UK data and tools have been used in over 120 SARS-CoV-2 outbreak investigations in the UK.
<h3>N439K Mutation</h3>
Researchers around the world have been monitoring genetic changes in the virus, which accumulate as it replicates and transmits. The aim is to understand if any changes, or mutations, have an effect on the virus’s function, the severity of disease it causes, or transmission. So far, no mutation seen has altered the disease severity that the virus causes.
There is a focus on changes that affect the virus’s characteristic Spike protein. Like all coronaviruses, the SARS-CoV-2 virus has proteins that stick out of its core. These Spike proteins are the ‘crowns’ that give coronaviruses their name. It is this protein that allows the virus to attach to, and then enter, human cells. Any mutations that affect the Spike protein are vital to monitor, as they could potentially affect its function. It is also a part of the virus that our immune systems can ‘see’ and respond to. It <a href="https://www.cogconsortium.uk/news_item/commentary-cog-uk-report-9-25th-june-2020/">was reported previously</a> that a mutation in the Spike protein called D614G has increased in circulating viruses, in a way that is consistent with a slight increase in the rate of transmission.
COG-UK researchers, led by <a href="https://www.gla.ac.uk/researchinstitutes/iii/staff/davidrobertson/">Professor David Robertson</a> and <a href="https://www.gla.ac.uk/researchinstitutes/iii/staff/emmathomson/">Professor Emma Thomson</a> at the University of Glasgow, assessed all mutations in the Spike protein. One of these - N439K – was of particular interest. N439K results in a change to the part of the Spike protein that binds to human cells. It is stable in the current virus population; it was seen in over 500 samples in Scotland early on in the UK epidemic, but then died out following the lockdown in March. It is now present again in virus samples from across Europe, the UK and the US. The wide spread of this mutation indicates the virus can thrive with such mutations – there is no detriment to its function.
Once vaccinations begin at scale, the Spike protein will be under ‘selective pressure’, as many vaccines are targeted against it. It is possible that viruses with certain mutations will fare better under a vaccine-primed immune response than others. Ongoing, early-stage, laboratory studies support this hypothesis. Work has shown that virus with the N439K mutation is able to resist monoclonal antibodies – like those being trialled as treatments - yet still bind to its target on human cells. But unlike a monoclonal antibody treatment, vaccines will prompt the immune system to make a range of antibodies, and so even if the Spike protein can resist one, it is likely another will be able to neutralise the virus.
The researchers stress the importance of systematically monitoring Spike protein mutations, finding and assessing new ones to understand their effects. This will be essential in the run up to any vaccine being introduced. The COG-UK team has set up a group, and systems, with the dedicated aim of monitoring new and existing mutations, prioritising those that need in-depth analyses.
<h3>Local Outbreak Management</h3>
Researchers led by <a href="https://quadram.ac.uk/people/andrew-page/">Dr Andrew Page at the Quadram Institute</a> in Norwich, report on the analysis of 1,035 SARS-CoV-2 genomes collected in Norfolk between March and August 2020.
They combined genome sequence data with clinical data to understand the origin, genetic variation, transmission and spread of SARS-CoV-2 in the region. They confirmed an outbreak at a food-processing facility and ruled out a hospital setting as the source of another outbreak. Long term follow up found no evidence of reinfection.
The team also found 16 viral variants, or lineages, in health care workers that were not present in patients, showing that PPE and infection control measures work to stop transmission of the virus.
For full details, view the study on <a href="https://doi.org/10.1101/2020.09.28.20201475">MedRxiv</a>.
<h3>Human to cat transmission</h3>
<a href="https://www.gla.ac.uk/researchinstitutes/iii/staff/margarethosie/">Professor Margaret Hosie at the University of Glasgow</a> and colleagues looked at transmission of SARS-CoV-2 between humans and cats. Two cats from different COVID-19 infected households in the UK were shown to be infected with SARS-CoV-2 from humans.
There is no evidence of cat-to-human transmission, or that any domestic animals play a role in the epidemiology of human infections with SARS-CoV-2.
For full details, view the study on <a href="https://www.biorxiv.org/content/10.1101/2020.09.23.309948v1.full">BioRxiv</a>.
<h3>Summary of studies using COG-UK genomic data</h3>
COG-UK data and tools have been used in more than 120 live and retrospective SARS-CoV-2 outbreak investigations. This includes outbreaks in hospitals, communities, workplaces and settings managed by local authorities. For examples, see ‘<a href="https://www.cogconsortium.uk/wp-content/uploads/2020/09/8th-September-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf">COG-UK genomic surveillance in action</a>’.
As the speed and scale of SARS-CoV-2 genome sequencing increases, the ability to use genomic data to investigate a wide range of scientific and public health questions is also growing. COG-UK aims to provide a large scale genomic surveillance service to support the investigation of more live outbreaks in the UK.
    [options_news_type] => Blog
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    [post_title] => Commentary: COG-UK report 12 – 15th October 2020
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Hannah Olinger on Unsplash.com
Blog23 Oct 2020

Commentary: COG-UK report 12 – 15th October 2020

As of 22nd October, the COVID-19 Genomics UK (COG-UK) Consortium has sequenced more than 81,000 SARS-CoV-2 virus genomes from the UK, representing about 45 per cent of the global total.

Array
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    [secondary_title] => <b>Please Note:</b> This report is provided at the request of SAGE and includes information on the ongoing state of the&nbsp;<span title="... research being carried out. It should not be considered formal or informal advice. The conclusions of the ongoing scientific studies may be subject to change as further evidence becomes available and as such any firm conclusions would be premature.">...</span>
    [excerpt] => COG-UK genome sequence data and tools have been used in more than 120 retrospective and live public health outbreak investigations in the UK since March 2020. Analysis of COG-UK and GISAID data highlights the need to establish a systematic approach for monitoring the appearance and spread of all variants of the SARS-CoV-2 virus
    [byline_text] => Report by the COVID-19 Genomics UK (COG-UK) Consortium
    [byline_date] => 20201016
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    [teaser_image] => 
    [content] => <h2>Executive Summary</h2>
<ul>
 	<li>COG-UK genome sequence data and tools have been used in more than 120 retrospective and live public health outbreak investigations in the UK since March 2020.</li>
 	<li>A viral lineage carrying a mutation, N439K, a probable antigenic variant owing to its location in the receptor binding motif of the SARS-CoV-2 spike protein, is now spreading in Europe (including 500+ infections in the UK). While there is no evidence that this variant will affect the efficacy of vaccines currently in development, it does highlight the need to establish a systematic approach for monitoring the appearance and spread of all variants and prioritising mutations of interest for further characterisation, in particular when selective pressure from mass vaccination programmes begins.</li>
</ul>
<hr />
&nbsp;
<h2>COG-UK summary</h2>
As described in <a href="https://www.cogconsortium.uk/wp-content/uploads/2020/09/8th-September-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf">COG-UK Report #11</a>, in addition to retrospective investigations, the consortium has been providing crucial support for the genomic surveillance of active SARS-CoV-2 outbreaks. An email survey of COG-UK site leads was used to collate information on the number of outbreaks in which investigations by the consortium using genomics have added value (Table 1). In total, COG-UK data and tools have been used in 120-166 live and retrospective SARS-CoV-2 outbreak investigations in public health and hospital settings in the UK to date.
[caption id="attachment_1162" align="aligncenter" width="1024"]<a href="https://www.cogconsortium.uk/wp-content/uploads/2020/10/table1.jpg"><img class="wp-image-1162 size-large" src="https://www.cogconsortium.uk/wp-content/uploads/2020/10/table1-1024x143.jpg" alt="" width="1024" height="143" /></a> <strong>Table 1:</strong> A summary of live and retrospective outbreaks in the UK for which COG-UK and PHA researchers have used consortium genomic data and tools. Exceedance describes defined outbreaks, for instance in a workplace. Surveillance programmes describe requests to look at everything in a local authority area, school, care home etc. Ranges are reported as some sites were only able to provide estimates.[/caption]
For a description of specific examples of the value added through the use of genomics by consortium members during outbreak investigations see <a href="https://www.cogconsortium.uk/wp-content/uploads/2020/09/8th-September-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf" target="_blank" rel="noopener">‘COG-UK genomic surveillance in action’, COG-UK Report #11</a>.
As the transition to a new operational phase continues through late 2020 and into 2021, COG-UK will provide large scale genomic surveillance service to support the investigation of a growing proportion of live outbreaks in the UK.
As described in the recent NERVTAG paper to SAGE, co-authored by COG-UK (<em>Is there evidence for genetic change in SARS-CoV-2 and if so, do mutations affect virus phenotype?</em>), there is increasing interest in monitoring mutations arising in the SARS-CoV-2 genome and determining whether these mutations have an impact on the biology of the virus, its transmission and the severity of the disease that it causes (See also ‘<em>A preliminary analysis of SARS-CoV-2 spike protein N439K lineages and surveillance of receptor binding mutations.</em>’ below). A COG-UK working group is being established to ensure that new and existing mutations are monitored in a systematic manner and that mutations of particular interest are prioritised for in depth analysis.
As the speed and scale of SARS-CoV-2 genome sequencing increases, the ability to use genomic data to investigate a wide range of scientific and public health questions is also growing. Accordingly, COG-UK is strengthening its interactions with other SAGE sub-groups, research consortia and public health bodies in the UK, and globally, so that the opportunities provided by integrating genomics can be realised. Dr Andrew Page of the Quadram Institute, Norwich has recently agreed to represent COG-UK on the SAGE sub-group focussed on social care homes. Dr Ewan Harrison of the Wellcome Sanger Institute represents the consortium on the sub-group focussed on ethnicity, and  Professor Judith Breuer of University College London on the nosocomial infection sub-group.
All five Health and Social Care Trusts in Northern Ireland are joining the SIREN study (the PHE priority study to determine if prior SARS-CoV-2 infection in health care workers confers future immunity to re- infection), and the Belfast Health and Social Care Trust is expected to join COG-UK’s Hospital-Onset COVID-19 Infections study. Northern Ireland will therefore be represented in both of these important UK-wide studies, with viral genome sequencing/analysis via COG-UK.
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<h2>Analysis updates</h2>
<h3>A preliminary analysis of SARS-CoV-2 spike protein N439K lineages and surveillance of receptor binding domain mutations.</h3>
<h4>Study leads</h4>
David L Robertson<sup>1</sup>, Sebastian Maurer-Stroh<sup>2</sup>, Ana da Silva Filipe<sup>1</sup> and Emma C Thomson<sup>1</sup>
1. MRC-University of Glasgow Centre for Virus Research;
2. Bioinformatics Institute, Agency for Science, Technology and Research, Singapore
<h4>Question addressed</h4>
SARS-CoV-2 is continually accruing mutations as it replicates and transmits among the human population. While the majority of observed mutations have no effect on the biological properties of the virus and the rate of change is relatively slow, it is important that there is a systematic approach to identify new genetic changes and to assess their biological significance. While much attention has been focussed on the SARS-CoV-2 spike protein D614G mutation (See <a href="https://www.cogconsortium.uk/wp-content/uploads/2020/05/14th-May-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf" target="_blank" rel="noopener">COG-UK report #6</a>, <a href="https://www.cogconsortium.uk/wp-content/uploads/2020/07/25th-June-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf" target="_blank" rel="noopener">report #9</a> and Ref 1), other mutations in the spike protein may be of epidemiological and clinical relevance. This analysis describes the assessment of transmission and likely biological significance of one such mutation: N439K in the spike receptor binding motif is an example of a stable and circulating mutation in the receptor binding motif that binds to the ACE2 receptor on the surface of host cells to enable viral entry.
<h4>Methodology</h4>
Preliminary assessment of sampling proportions, phylogenetic distribution and the relationship between N/K at position 439 of the spike protein.
<h4>Findings</h4>
N439K was initially identified in a single lineage first detected in March 2020 and until recently was almost unique to Scotland where it infected more than 500 individuals (Figure 1).
[caption id="attachment_1163" align="aligncenter" width="1024"]<a href="https://www.cogconsortium.uk/wp-content/uploads/2020/10/phylogenetic_tree_5000_scottish_genomes.jpg"><img class="wp-image-1163 size-large" src="https://www.cogconsortium.uk/wp-content/uploads/2020/10/phylogenetic_tree_5000_scottish_genomes-1024x395.jpg" alt="" width="1024" height="395" /></a> <strong>Figure 1:</strong> Phylogenetic tree of 5000 Scottish SARS-CoV-2 genomes from COG-UK dataset (17/07/20) highlighting D614G lineages (left panel) and N439K lineages (right panel). Note that the K-439 lineage and most N-439 lineages are also G-614.[/caption]
This lineage also carries the D614G variant that has been associated with an increase in frequency among the population.
In line with the cessation in viral transmission in Scotland coincident with the lockdown in spring 2020, this UK lineage is now extinct and has not been observed since the 20th June in South Lanarkshire. However, N439K has now been identified in another fast growing lineage that has been sampled between late June and mid-August in Romania, Norway, Switzerland, Ireland, Belgium, Germany and now in all parts of the UK (Figure 2). The apparent sudden rise in August/September appears to be linked to relaxation of  control measures, the degree of sampling in these countries and its recent emergence in the UK with a high sampling rate. N439K has also been detected in four linked infections in the US and sporadically in genome data from elsewhere.
[caption id="attachment_1164" align="aligncenter" width="1024"]<a href="https://www.cogconsortium.uk/wp-content/uploads/2020/10/phylogenetic_gisaid.jpg"><img class="wp-image-1164 size-large" src="https://www.cogconsortium.uk/wp-content/uploads/2020/10/phylogenetic_gisaid-1024x551.jpg" alt="" width="1024" height="551" /></a> <strong>Figure 2:</strong> A) Phylogenetic tree of SARS-CoV-2 genomes from the COG-UK data in the context of the GISAID dataset highlighting the original Scottish N439K lineage and the more recent and currently spreading European N439K lineage associated with multiple UK lineages. B) Number of weekly cases and country location of the two N439K lineages from mid-March to 02/10/2020.[/caption]
Investigation of clinical outcomes from &gt;1600 Scottish patients infected with either the lineage defined by 439K versus the wild-type lineage (439N) showed no significant difference in disease severity. Phylodynamic analysis demonstrated that the Scottish N439K lineage has a relatively fast growth rate in spreading through the population (analysis by Sam Lycett, Roslin Institute), but this is likely due to the D614G background  of this lineage (Ref 1). Competitive virus growth experiments of these different mutants are underway at the MRC-University of Glasgow Centre for Virus Research.
Investigation of clinical outcomes from &gt;1600 Scottish patients infected with either the lineage defined by 439K versus the wild-type lineage (439N) showed no significant difference in disease severity. Phylodynamic analysis demonstrated that the Scottish N439K lineage has a relatively fast growth rate in spreading through the population (analysis by Sam Lycett, Roslin Institute), but this is likely due to the D614G background of this lineage (Ref 1). Competitive virus growth experiments of these different mutants are underway at the MRC-University of Glasgow Centre for Virus Research.
<h4>Key Conclusions</h4>
While SARS-CoV-2 genetic variation is accumulating, it is relatively constrained for an RNA virus.
Some spike amino acid replacements do seem to be changing the biology of the virus (e.g. D614G), although there is no current evidence that N439K, or other variants in the receptor binding motif (such as T478I and V483I, shown to have antigenic significance) have increased the potential for transmission or altered disease severity.
Importantly, these spike receptor binding domain variants appear to be relatively stable amino acid replacements that are not detrimental to viral fitness and are well tolerated in circulating lineages in the UK. This is a potential concern as vaccination programmes designed using these regions as targets begin to apply selective pressure on these lineages (see below for further discussion).
<h4>Discussion</h4>
In addition to N439K, other mutations are being observed in the spike receptor binding motif: S477N (&gt;300 UK sequences), T478I (&gt;100), S494P (&gt;20), E484Q (&gt;10), S477I (&gt;10), E484Q (&gt;10) and others at lower frequencies (Figure 3).
[caption id="attachment_1165" align="aligncenter" width="1024"]<a href="https://www.cogconsortium.uk/wp-content/uploads/2020/10/fig3.jpg"><img class="wp-image-1165 size-large" src="https://www.cogconsortium.uk/wp-content/uploads/2020/10/fig3-1024x715.jpg" alt="" width="1024" height="715" /></a> <strong>Figure 3.</strong> Receptor binding surveillance for UK complete genomes. Mutations resulting in amino acid replacements in or near SARS-CoV-2’s spike receptor binding motif that have been observed by 2020-10-06 are shown. Replacements occurring at least twice are listed (top).[/caption]
Collectively these demonstrate that mutations in the spike receptor binding motif are tolerated. The circulation of the N439K lineages demonstrates these viruses do not necessarily exhibit any apparent fitness cost. This is potentially concerning as this region is soon to be under selective pressure from a range of vaccine programmes.
Some of the mutations in the receptor binding domain have been documented to confer resistance to neutralising antibodies and to influence interactions with the ACE2 receptor, which may facilitate the evolution of additional mutations in the surrounding region that can lead to viruses able circumvent the impact of those neutralizing antibodies. Support for this concern has been provide by laboratory experiments showing that it is possible to select for SARS-CoV-2 spike protein mutations in the receptor-binding domain (including N439K) that remain functional and able to bind ACE2 receptors but can confer resistance to monoclonal neutralising antibodies or convalescent plasma (Refs 2 and 3).
It is therefore essential that a systematic approach is taken to identify and assess new genetic changes, in particular in regions important for host infection, viral transmission and for antigenicity. Whilst limited genomic diversity has emerged to date, this may change in the next phase of the epidemic as selective pressures exerted by vaccines, treatments and non-pharmaceutical interventions increases. As such, it is particularly important that surveillance of antigenic change is established in the lead up to the roll out of a vaccination program in the UK, since many of the vaccines under development target the spike protein.
Accordingly COG-UK has established a working group to establish a mechanism to ensure that new and existing mutations are monitored in a systematic manner and that mutations of particular interest are prioritised for in depth genomic, phylodynamic and virological analyses.
<h4>References</h4>
1. Volz, E. M, <em>et al</em>. Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity (2020) <em>medRxiv</em>, doi: <a href="https://doi.org/10.1101/2020.07.31.20166082" target="_blank" rel="noopener">https://doi.org/10.1101/2020.07.31.20166082</a>.
2. Weisblum, Y., Schmidt, F., <em>et al</em>. Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants. (2020) <em>bioRxiv</em>, doi: <a href="https://doi.org/10.1101/2020.07.21.214759" target="_blank" rel="noopener">https://doi.org/10.1101/2020.07.21.214759</a>.
3. Li, Q. <em>et al</em>. The Impact of Mutations in SARS-CoV-2 Spike on Viral Infectivity and Antigenicity. (2020) <em>Cell</em>, doi: <a href="https://doi.org/10.1016/j.cell.2020.07.012" target="_blank" rel="noopener">https://doi.org/10.1016/j.cell.2020.07.012</a>
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<h2>COG-UK recent publications</h2>
<h3>Large scale sequencing of SARS-CoV-2 genomes from one region allows detailed epidemiology and enables local outbreak management</h3>
<em>MedRxiv</em> - doi: <a href="https://doi.org/10.1101/2020.09.28.20201475" target="_blank" rel="noopener">https://doi.org/10.1101/2020.09.28.20201475</a>
<h4>Authors:</h4>
Andrew J Page, Alison E Mather, Thanh Le Viet, Emma J Meader, Nabil-Fareed J Alikhan, Gemma L Kay, Leonardo de Oliveira Martins, Alp Aydin, David J Baker, Alexander J. Trotter, Steven Rudder, Ana P Tedim, Anastasia Kolyva, Rachael Stanley, Maria Diaz, Will Potter, Claire Stuart, Lizzie Meadows, Andrew Bell, Ana Victoria Gutierrez, Nicholas M Thomson, Evelien M Adriaenssens, Tracey Swingler, Rachel AJ Gilroy, Luke Griffith, Dheeraj K Sethi, Rose K Davidson, Robert A Kingsley, Luke Bedford, Lindsay J Coupland, Ian G Charles, Ngozi Elumogo, John Wain, Reenesh Prakash, Mark A Webber, SJ Louise Smith, Meera Chand, Samir Dervisevic, Justin O'Grady, The COVID-19 Genomics UK (COG-UK) consortium
<h4>Summary:</h4>
Between March and August 2020, over 3,200 COVID-19 cases were reported in Norfolk. 1565 positive clinical samples from 1376 cases were collected in four major hospitals, multiple minor hospitals, care facilities and community organisations within Norfolk and the surrounding area were collected and subjected to whole genome sequencing. 1035 cases resulted in genomes of sufficient quality for phylogenetic analysis, which revealed the presence of 26 distinct global lineages and 100 distinct UK lineages, with local evolution at a rate of 2 SNPs per month. Sequence data was combined with clinical metadata to understand the origin, genetic variation, transmission and spread of SARS-CoV-2 within the region. Highlights from this county-level analysis included the identification of a single sub-lineage associated with cases in 6 care facilities; confirming an outbreak at a food-processing facility; the ruling out of a nosocomial origin for another outbreak; and the identification of 16 lineages in health care workers not present in patients, demonstrating the effectiveness of infection control measures. The analysis also found that the D614G spike protein variant dominated in the samples, while longitudinal samples showed no evidence of reinfection.
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<h3>Respiratory disease in cats associated with human-to-cat transmission of SARS-CoV-2 in the UK</h3>
<em>BioRxiv</em> - doi: <a href="https://doi.org/10.1101/2020.09.23.309948" target="_blank" rel="noopener">https://doi.org/10.1101/2020.09.23.309948</a>
<h4>Authors:</h4>
Margaret J Hosie, Ilaria Epifano, Vanessa Herder, Richard J Orton, Andrew Stevenson, Natasha Johnson, Emma MacDonald, Dawn Dunbar, Michael McDonald, Fiona Howie, Bryn Tennant, Darcy Herrity, Ana Da Silva Filipe, Daniel G Streicker, Brian J Willett, Pablo R Murcia, Ruth F Jarrett, David L Robertson, William Weir, the COVID-19 Genomics UK (COG-UK) consortium
<h4>Summary:</h4>
During the COVID-19 pandemic, naturally occurring infections following transmission have been reported in domestic and non-domestic cats, dogs and mink. In this study, two cats from different COVID-19-infected households in the UK were shown to be infected with SARS-CoV-2 from humans. Infection was demonstrated using a range of approaches, including immunofluorescence, in situ hybridization, PCR testing. Post-mortem tissue samples for cat 1 displayed pathological and histological findings consistent with viral pneumonia, while cat 2 presented with rhinitis and conjunctivitis. Whole genome sequencing and analysis of the virus from cat 2 revealed five single nucleotide polymorphisms (SNPs) compared to the nearest sequenced UK human SARS-CoV-2 isolate (from the same UK county), although comparison with genomes from 9 other feline SARS-CoV-2 isolates revealed no shared cat-specific mutations. At present, there is no evidence of cat-to-human transmission or that cats, dogs or other domestic animals play any appreciable role in the epidemiology of human infections with SARS-CoV-2.
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<h3>Download a PDF of this Report</h3>
<ul>
 	<li><strong><a href="https://www.cogconsortium.uk/wp-content/uploads/2020/10/15th-October-2020-Report-–-COVID-19-Genomics-UK-COG-UK-Consortium.pdf" target="_blank" rel="noopener">15th October 2020 Report – COVID-19 Genomics UK (COG-UK) Consortium.pdf</a></strong></li>
</ul>
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Report16 Oct 2020

Report 12: 15th October 2020 – COVID-19 Genomics UK (COG-UK) Consortium

COG-UK genome sequence data and tools have been used in more than 120 retrospective and live public health outbreak investigations in the UK since March 2020. Analysis of COG-UK and GISAID data highlights the need to establish a systematic approach for monitoring the appearance and spread of all variants of the SARS-CoV-2 virus