A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples

The synthesis of this dataset was enabled by funding from the Canada Foundation for Innovation, from Genome Canada through Ontario Genomics, from NSERC, and from the Ontario Ministry of Research, Innovation and Science in support of the International Barcode of Life project. It was also enabled by philanthropic support from the Gordon and Betty Moore Foundation and from Ann McCain Evans and Chris Evans. The release of the data on GGBN was supported by a GGBN – Global Genome Initiative Award and we thank G. Droege, L. Loo, K. Barker, and J. Coddington for their support. Our work depended heavily on the analytical capabilities of the Barcode of Life Data Systems (BOLD, www.boldsystems.org). We also thank colleagues at the CBG for their support, including S. Adamowicz, S. Bateson, E. Berzitis, V. Breton, V. Campbell, A. Castillo, C. Christopoulos, J. Cossey, C. Gallant, J. Gleason, R. Gwiazdowski, M. Hajibabaei, R. Hanner, K. Hough, P. Janetta, A. Pawlowski, S. Pedersen, J. Robertson, D. Roes, K. Seidle, M. A. Smith, B. St. Jacques, A. Stoneham, J. Stahlhut, R. Tabone, J.Topan, S. Walker, and C. Wei. For bioblitz-related assistance, we are grateful to D. Ireland, D. Metsger, A. Guidotti, J. Quinn and other members of Bioblitz Canada and Ontario Bioblitz. For our work in Canada’s national parks, we thank S. Woodley and J. Waithaka for their lead role in organizing permits and for the many Parks Canada staff who facilitated specimen collections, including M. Allen, D. Amirault-Langlais, J. Bastick, C. Belanger, C. Bergman, J.-F. Bisaillon, S. Boyle, J. Bridgland, S. Butland, L. Cabrera, R. Chapman, J. Chisholm, B. Chruszcz, D. Crossland, H. Dempsey, N. Denommee, T. Dobbie, C. Drake, J. Feltham, A. Forshner, K. Forster, S. Frey, L. Gardiner, P. Giroux, T. Golumbia, D. Guedo, N. Guujaaw, S. Hairsine, E. Hansen, C. Harpur, S. Hayes, J. Hofman, S. Irwin, B. Johnston, V. Kafa, N. Kang, P. Langan, P. Lawn, M. Mahy, D. Masse, D. Mazerolle, C. McCarthy, I. McDonald, J. McIntosh, C. McKillop, V. Minelga, C. Ouimet, S. Parker, N. Perry, J. Piccin, A. Promaine, P. Roy, M. Savoie, D. Sigouin, P. Sinkins, R. Sissons, C. Smith, R. Smith, H. Stewart, G. Sundbo, D. Tate, R. Tompson, E. Tremblay, Y. Troutet, K. Tulk, J. Van Wieren, C. Vance, G. Walker, D. Whitaker, C. White, R. Wissink, C. Wong, and Y. Zharikov. For our work near Canada’s ports in Vancouver, Toronto, Montreal, and Halifax, we thank R. Worcester, A. Chreston, M. Larrivee, and T. Zemlak, respectively. Many other organizations improved coverage in the reference library by providing access to specimens – they included the Canadian National Collection of Insects, Arachnids and Nematodes, Smithsonian Institution’s National Museum of Natural History, the Canadian Museum of Nature, the University of Guelph Insect Collection, the Royal British Columbia Museum, the Royal Ontario Museum, the Pacifc Forestry Centre, the Northern Forestry Centre, the Lyman Entomological Museum, the Churchill Northern Studies Centre, and rare Charitable Research Reserve. We also thank the many taxonomic specialists who identifed specimens, including A. Borkent, B. Brown, M. Buck, C. Carr, T. Ekrem, J. Fernandez Triana, C. Guppy, K. Heller, J. Huber, L. Jacobus, J. Kjaerandsen, J. Klimaszewski, D. Lafontaine, J-F. Landry, G. Martin, A. Nicolai, D. Porco, H. Proctor, D. Quicke, J. Savage, B. C. Schmidt, M. Sharkey, A. Smith, E. Stur, A. Tomas, J. Webb, N. Woodley, and X. Zhou. We also thank K. Kerr and T. Mason for facilitating collections at Toronto Zoo and D. Iles for servicing the trap at Wapusk National Park. This paper contributes to the University of Guelph’s Food from Thought research program supported by the Canada First Research Excellence Fund. The Barcode of Life Data System (BOLD; www.boldsystems.org)8 was used as the primary workbench for creating, storing, analyzing, and validating the specimen and sequence records and the associated data resources48. The BOLD platform has a private, password-protected workbench for the steps from specimen data entry to data validation (see details in Data Records), and a public data portal for the release of data in various formats. The latter is accessible through an API (http://www.boldsystems.org/index.php/resources/api?type=webservices) that can also be controlled through R75 with the package ‘bold’76.Peer reviewedPublisher PD

A molecular-based identification resource for the arthropods of Finland

Publisher Copyright: © 2021 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.To associate specimens identified by molecular characters to other biological knowledge, we need reference sequences annotated by Linnaean taxonomy. In this study, we (1) report the creation of a comprehensive reference library of DNA barcodes for the arthropods of an entire country (Finland), (2) publish this library, and (3) deliver a new identification tool for insects and spiders, as based on this resource. The reference library contains mtDNA COI barcodes for 11,275 (43%) of 26,437 arthropod species known from Finland, including 10,811 (45%) of 23,956 insect species. To quantify the improvement in identification accuracy enabled by the current reference library, we ran 1000 Finnish insect and spider species through the Barcode of Life Data system (BOLD) identification engine. Of these, 91% were correctly assigned to a unique species when compared to the new reference library alone, 85% were correctly identified when compared to BOLD with the new material included, and 75% with the new material excluded. To capitalize on this resource, we used the new reference material to train a probabilistic taxonomic assignment tool, FinPROTAX, scoring high success. For the full-length barcode region, the accuracy of taxonomic assignments at the level of classes, orders, families, subfamilies, tribes, genera, and species reached 99.9%, 99.9%, 99.8%, 99.7%, 99.4%, 96.8%, and 88.5%, respectively. The FinBOL arthropod reference library and FinPROTAX are available through the Finnish Biodiversity Information Facility (www.laji.fi) at https://laji.fi/en/theme/protax. Overall, the FinBOL investment represents a massive capacity-transfer from the taxonomic community of Finland to all sectors of society.Peer reviewe

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England

The SARS-CoV-2 lineage B.1.1.7, designated variant of concern (VOC) 202012/01 by Public Health England1, was first identified in the UK in late summer to early autumn 20202. Whole-genome SARS-CoV-2 sequence data collected from community-based diagnostic testing for COVID-19 show an extremely rapid expansion of the B.1.1.7 lineage during autumn 2020, suggesting that it has a selective advantage. Here we show that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that B.1.1.7 has higher transmissibility than non-VOC lineages, even if it has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with cases of B.1.1.7 including a larger share of under 20-year-olds than non-VOC cases. We estimated time-varying reproduction numbers for B.1.1.7 and co-circulating lineages using SGTF and genomic data. The best-supported models did not indicate a substantial difference in VOC transmissibility among different age groups, but all analyses agreed that B.1.1.7 has a substantial transmission advantage over other lineages, with a 50% to 100% higher reproduction number

Publisher Correction: SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

In the version of this article initially published, the author affiliation information was incomplete, neglecting to note that Brian J. Willett, Joe Grove, Oscar A. MacLean, Craig Wilkie, Giuditta De Lorenzo, Wilhelm Furnon, Diego Cantoni, Sam Scott, Nicola Logan and Shirin Ashraf contributed equally and that John Haughney, David L. Robertson, Massimo Palmarini, Surajit Ray and Emma C. Thomson jointly supervised the work, as now indicated in the HTML and PDF versions of the article

Exponential growth, high prevalence of SARS-CoV-2, and vaccine effectiveness associated with the Delta variant

SARS-CoV-2 infections were rising during early summer 2021 in many countries associated with the Delta variant. We assessed RT-PCR swab-positivity in the REal-time Assessment of Community Transmission-1 (REACT-1) study in England. We observed sustained exponential growth with average doubling time (June-July 2021) of 25 days driven by complete replacement of Alpha variant by Delta, and by high prevalence at younger less-vaccinated ages. Unvaccinated people were three times more likely than double-vaccinated people to test positive. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020-December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020–December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population