The scale of data produced during the SARS-CoV-2 pandemic has been unprecedented, with more than 13 million sequences shared publicly at the time of writing. This wealth of sequence data provides important context for interpreting local outbreaks. However, placing sequences of interest into national and international context is difficult given the size of the global dataset. Often outbreak investigations and genomic surveillance efforts require running similar analyses again and again on the latest dataset and producing reports. We developed civet (cluster investigation and virus epidemiology tool) to aid these routine analyses and facilitate virus outbreak investigation and surveillance. Civet can place sequences of interest in the local context of background diversity, resolving the query into different 'catchments' and presenting the phylogenetic results alongside metadata in an interactive, distributable report. Civet can be used on a fine scale for clinical outbreak investigation, for local surveillance and cluster discovery, and to routinely summarise the virus diversity circulating on a national level. Civet reports have helped researchers and public health bodies feedback genomic information in the appropriate context within a timeframe that is useful for public health.

COVID-19 transmission rates are often linked to locally circulating strains of SARS-CoV-2. Here we describe 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in Rwanda from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the B.1.1.7 and B.1.351 variants of concern among incoming travelers tested at Kigali International Airport. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences were available. Our results highlight the importance of systematic genomic surveillance and regional collaborations for a durable response towards combating COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 19 (COVID-19), is a single-stranded positive-sense ribonucleic acid (RNA) virus that typically undergoes one to two single nucleotide mutations per month. COVID-19 continues to spread globally, with case fatality and test positivity rates often linked to locally circulating strains of SARS-CoV-2. Furthermore, mutations in this virus, in particular those occurring in the spike protein (involved in the virus binding to the host epithelial cells) have potential implications in current vaccination efforts. In Rwanda, more than twenty thousand cases have been confirmed as of March 14th 2021, with a case fatality rate of 1.4% and test positivity rate of 2.3% while the recovery rate is at 91.9%. Rwanda started its genomic surveillance efforts, taking advantage of pre-existing research projects and partnerships, to ensure early detection of SARS-CoV-2 variants and to potentially contain the spread of variants of concern (VOC). As a result of this initiative, we here present 203 SARS-CoV-2 whole genome sequences analyzed from strains circulating in the country from May 2020 to February 2021. In particular, we report a shift in variant distribution towards the newly emerging sub-lineage A.23.1 that is currently dominating. Furthermore, we report the detection of the first Rwandan cases of the VOCs, B.1.1.7 and B.1.351, among incoming travelers tested at Kigali International Airport. We also discuss the potential impact of COVID-19 control measures established in the country to control the spread of the virus. To assess the importance of viral introductions from neighboring countries and local transmission, we exploit available individual travel history metadata to inform spatio-temporal phylogeographic inference, enabling us to take into account infections from unsampled locations during the time frame of interest. We uncover an important role of neighboring countries in seeding introductions into Rwanda, including those from which no genomic sequences are currently available or that no longer report positive cases. Our results point to the importance of systematically screening all incoming travelers, regardless of the origin of their travels as well as regional considerations for durable response to COVID-19.

The second SARS virus, SARS-CoV-2, emerged in December 2019, and within a month was globally distributed. It was first introduced into Scotland in February 2020 associated with returning travellers and visitors. By March it was circulating in communities across the UK, and to control COVID-19 cases, and prevent overwhelming of the National Health Service (NHS), a 'lockdown' was introduced on 23rd March 2020 with a restriction of people's movements. To augment the public health efforts a large-scale genome epidemiology effort (as part of the COVID-19 Genomics UK (COG-UK) consortium) resulted in the sequencing of over 5000 SARS-CoV-2 genomes by 18th August 2020 from Scottish cases, about a quarter of the estimated number of cases at that time. Here we quantify the geographical origins of the first wave introductions into Scotland from abroad and other UK regions, the spread of these SARS-CoV-2 lineages to different regions within Scotland (defined at the level of NHS Health Board) and the effect of lockdown on virus 'success'. We estimate that approximately 300 introductions seeded lineages in Scotland, with around 25% of these lineages composed of more than five viruses, but by June circulating lineages were reduced to low levels, in line with low numbers of recorded positive cases. Lockdown was, thus, associated with a dramatic reduction in infection numbers and the extinguishing of most virus lineages. Unfortunately since the summer cases have been rising in Scotland in a second wave, with >1000 people testing positive on a daily basis, and hospitalisation of COVID-19 cases on the rise again. Examining the available Scottish genome data from the second wave, and comparing it to the first wave, we find that while some UK lineages have persisted through the summer, the majority of lineages responsible for the second wave are new introductions from outside of Scotland and many from outside of the UK. This indicates that, while lockdown in Scotland is directly linked with the first wave case numbers being brought under control, travel-associated imports (mostly from Europe or other parts of the UK) following the easing of lockdown are responsible for seeding the current epidemic population. This demonstrates that the impact of stringent public health measures can be compromised if following this, movements from regions of high to low prevalence are not minimised.

A Correction to this paper has been published: https://doi.org/10.1038/s41564-021-00869-0 .

The free-living nematode Caenorhabditis elegans is a key laboratory model for metazoan biology. C. elegans has also become a model for parasitic nematodes despite being only distantly related to most parasitic species. All of the ∼65 Caenorhabditis species currently in culture are free-living, with most having been isolated from decaying plant or fungal matter. Caenorhabditis bovis is a particularly unusual species that has been isolated several times from the inflamed ears of Zebu cattle in Eastern Africa, where it is associated with the disease bovine parasitic otitis. C. bovis is therefore of particular interest to researchers interested in the evolution of nematode parasitism. However, as C. bovis is not in laboratory culture, it remains little studied. Here, by sampling livestock markets and slaughterhouses in Western Kenya, we successfully reisolated C. bovis from the ear of adult female Zebu. We sequenced the genome of C. bovis using the Oxford Nanopore MinION platform in a nearby field laboratory and used the data to generate a chromosome-scale draft genome sequence. We exploited this draft genome sequence to reconstruct the phylogenetic relationships of C. bovis to other Caenorhabditis species and reveal the changes in genome size and content that have occurred during its evolution. We also identified expansions in several gene families that have been implicated in parasitism in other nematode species. The high-quality draft genome and our analyses thereof represent a significant advancement in our understanding of this unusual Caenorhabditis species.

AbstractSARS-CoV-2, the causative agent of COVID-19, emerged in Wuhan, China in December 2019 and spread rapidly throughout the world. Understanding the introductions of this new coronavirus in different settings may assist control efforts and the establishment of frameworks to support rapid response in future infectious disease outbreaks.We investigated the first four weeks of emergence of the SARS-CoV-2 virus in Scotland after the first case reported on the 1st March 2020. We obtained full genome sequences from 452 individuals with a laboratory-confirmed diagnosis of COVID-19, representing 20% of all cases until 1st April 2020 (n=2310). This permitted a genomic epidemiology approach to study the introductions and spread of the SARS-2 virus in Scotland.From combined phylogenetic and epidemiological analysis, we estimated at least 113 introductions of SARS-CoV-2 into Scotland during this period. Clusters containing multiple sequences suggestive of onward transmission occurred in 48/86 (56%). 42/86 (51%) clusters had no known international travel history indicating undetected introductions.The majority of viral sequences were most closely related to those circulating in other European countries, including Italy, Austria and Spain. Travel-associated introductions of SARS-CoV-2 into Scotland predated travel restrictions in the UK and other European countries. The first local transmission occurred three days after the first case. A shift from travel-associated to sustained community transmission was apparent after only 11 days. Undetected introductions occurred prior to the first known case of COVID-19. Earlier travel restrictions and quarantine measures might have resulted in fewer introductions into Scotland, thereby reducing the number of cases and the subsequent burden on health services. The high number of introductions and transmission rates were likely to have impacted on national contact tracing efforts. Our results also demonstrate that local real-time genomic epidemiology can be used to monitor transmission clusters and facilitate control efforts to restrict the spread of COVID-19.FundingMRC (MC UU 1201412), UKRI/Wellcome (COG-UK), Wellcome Trust Collaborator Award (206298/Z/17/Z – ARTIC Network; TCW Wellcome Trust Award 204802/Z/16/ZResearch in contextEvidence before this studyCoronavirus disease-2019 (COVID-19) was first diagnosed in Scotland on the 1st of March 2020 following the emergence of the causative severe acute respiratory system coronavirus 2 (SARS-CoV-2) virus in China in December 2019. During the first month of the outbreak in Scotland, 2310 positive cases of COVID-19 were detected, associated with 1832 hospital admissions, 207 intensive care admissions and 126 deaths. The number of introductions into Scotland and the source of those introductions was not known prior to this study.Added value of this studyUsing a combined phylogenetic and epidemiological approach following real-time next generation sequencing of 452 SARS-CoV-2 samples, it was estimated that the virus was introduced to Scotland on at least 113 occasions, mostly from other European countries, including Italy, Austria and Spain. Localised outbreaks occurred in the community across multiple Scottish health boards, within healthcare facilities and an international conference and community transmission was established rapidly, before local and international lockdown measures were introduced.

The Coronavirus Disease 2019 (COVID-19) Genomics UK Consortium (COG-UK) was launched in March, 2020, with £20 million support from UK Research and Innovation, the UK Department of Health and Social Care, and Wellcome Trust. The goal of this consortium is to sequence severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for up to 230 000 patients, health-care workers, and other essential workers in the UK with COVID-19, which will help to enable the tracking of SARS-CoV-2 transmission, identify viral mutations, and integrate with health data to assess how the viral genome interacts with cofactors and consequences of COVID-19. Results from this initiative are guiding decision makers, including the weekly reports to the UK Scientific Advisory Group for Emergencies (SAGE). This initiative is the first time that large-scale genomic epidemiology has been used to guide and inform the public health response to a pandemic in the UK, setting the stage for genomics to serve as a core tool for outbreak tracking in future pandemics. COG-UK builds on the UK's strengths in pathogen genomics, population health sciences, and health informatics. It benefits from a large and well equipped network of specialist academic and research facilities working in close collaboration with the UK's public health agencies and the National Health Service (NHS). Partners in COG-UK at the time of writing are shown in the appendix (p 4). As COG-UK's example could help to inform other countries seeking to rapidly develop and scale-up national sequencing capacity and a joined-up health information system, we describe six key features of our experience. COG-UK is co-ordinated from an integrated hub (based between the University of Cambridge [Cambridge, UK] and Wellcome Sanger Institute [Cambridge, UK]), with sample collection and sequencing taking place at multiple organisations across the country. This decentralised model enables rapid sequencing and prioritisation at the point of need, while supporting equitable national access. The core of COG-UK is formed around a network of regional sequencing centres in UK academic institutions and UK public health agencies. Sequencing in regional sequencing centres is close to real time, with a 24–48 h turnaround time, with data interpreted and used locally. In addition, the Wellcome Sanger Institute provides a high volume national sequencing capacity using high throughput, cost efficient viral sequencing for UK hospitals that do not have sequencing capabilities (ie, contributing partners), the national testing centres for key workers, and overspill from the regional sequencing centres. To help prioritise finite sequencing resources, COG-UK has developed a sampling strategy to concurrently enable broad population-level analyses, targeted analyses of specific populations, and freedom to tackle local priorities. This strategy aims to maximise insight while minimising demands on stretched local facilities. To maximise speed and inclusiveness, COG-UK adopts a range of technical approaches (ie, amplicons, baits, and metagenomics) and technologies (ie, single molecule vs next generation sequencing approaches), enabling centres to build on existing pipelines and expertise. Several centres have adopted a tiling amplicon sequencing approach developed by the ARTIC network, which is run on multiple platforms to help achieve the very high sample throughput required. Tight linkage to diagnostic and public health laboratories minimises transport and analysis delays. The system needs to be continuous and rapid, with a target of 48 h from sample collection to analysis. Sequence data are uploaded to the Cloud Infrastructure for Microbial Bioinformatics (MRC-CLIMB) server;1Connor TR Loman NJ Thompson S et al.CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community.Microb Genom. 2016; 2e000086PubMed Google Scholar a centralised, replicated environment for data storage and analysis. MRC-CLIMB provides a ready-made starting point for the computational analysis, with the option to integrate with other resources within the UK or scale into the commercial cloud. A standardised lineage assignment to enable national and international comparison has been developed,2Rambaut A Holmes EC Hill V et al.A dynamic nomenclature proposal for SARS-CoV-2 to assist genomic epidemiology.bioRxiv. 2020; (published online April 19.)DOI: 10.1101/2020.04.17.046086Google Scholar which are linked to data and released interactively via Microreact.3Argimón S Abudahab K Goater RJE et al.Microreact: visualizing and sharing data for genomic epidemiology and phylogeography.Microb Genom. 2016; 2e000093Crossref PubMed Scopus (363) Google Scholar Sequence data are made open access through release into the European Nucleotide Archive through the European Bioinformatics Institute and the Global Initiative on Sharing All Influenza Data. To enhance the value of sequence data, we are creating an integrated dataset connecting viral genome data with multidimensional patient data from clinical, epidemiological, and other sources. By accessing existing NHS e-health records and related sources, the goal is to avoid adding to the burden on busy NHS staff. Each week a sequence data cutoff is applied across the consortium to give a defined dataset for weekly analysis to report to SAGE, initially focusing on: (1) local transmission versus imported cases,4Baillie GJ Galiano M Agapow P-M et al.Evolutionary dynamics of local pandemic H1N1/2009 influenza virus lineages revealed by whole-genome analysis.J Virol. 2012; 86: 11-18Crossref PubMed Scopus (89) Google Scholar (2) rates of epidemic growth,5Fraser C Donnelly CA Cauchemez S et al.Pandemic potential of a strain of influenza a (h1n1): early findings.Science. 2009; 324: 1557-1561Crossref PubMed Scopus (1561) Google Scholar, 6Volz EM Kosakovsky Pond SL Ward MJ Leigh Brown AJ Frost SD Phylodynamics of infectious disease epidemics.Genetics. 2009; 183: 1421-1430Crossref PubMed Scopus (154) Google Scholar (3) reconstructing spatial movement, (4) chains of transmission,7Houlihan CF Frampton D Ferns RB et al.Use of whole-genome sequencing in the investigation of a nosocomial influenza virus outbreak.J Infect Dis. 2018; 218: 1485-1489Crossref PubMed Scopus (41) Google Scholar (5) observed genetic changes, and (6) identification of genomic changes potentially affecting common diagnostic tests or direct (eg, chemotherapeutics) or indirect therapies. Our website will provide a publication strategy, including open access publications, bespoke local and regional analyses, and data summaries suitable for the general public. In only 4 weeks, over 7000 genomes have been sequenced by COG-UK, the largest number of any country to date. We are committed to open and global collaboration. Reciprocity is crucial to genomic epidemiological approaches; the use of our data will be maximised if other countries adopt similar approaches. We declare no competing interests. COG-UK is supported by the funding from the UK Research and Innovation (UKRI), Medical Research Council, and the National Institute of Health Research and Wellcome, administered by UKRI. Download .pdf (.18 MB) Help with pdf files Supplementary appendix

Sequence-based surveillance of antimicrobial resistance (AMR) is becoming increasingly prevalent – with the emergence of low-capital investment long-read technology in the form of Oxford Nanopore Technologies (ONT) MinION enabling such surveillance in infrastructure poor low-middle income countries. In such settings, the surveillance of mobile genetic elements, which can facilitate the rapid spread of beneficial AMR genotypes horizontally throughout bacterial populations, and across species boundaries may provide a relatively low-cost target for surveillance. In this study we used an ONT MinION to whole genome sequence E. coli isolates from Nairobi, Kenya for which Illumina data had previously been generated alongside producing whole genome sequences for novel isolates collected for a model surveillance project in Busia, Kenya. The Illumina sequenced isolates had been found to carry a consistently co-occurring set of antibiotic resistance genes (tetA, strAB, sul1, bla-TEM, and dfrA7), conferring resistance to five antibiotic classes. The exact genomic context of which could not be determined with Illumina sequence data alone. Whilst those isolates collected in Busia demonstrated phenotypic resistance to those classes. Initially suspected to be borne on regionally disseminated plasmids; the long-reads generated by the MinION allowed the full resolution of these genes across several closely associated transposable elements, which were further found to be integrated chromosomally, across these geographically independent isolates. This study demonstrates that value of long-read sequencing in the resolution of regionally important mobile genetic elements, simultaneously allowing further value to be added to previously produced long-read data.