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Record W3091824907 · doi:10.1016/s2666-5247(20)30122-1

A multinational listeriosis outbreak and the importance of sharing genomic data

2020· article· en· W3091824907 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Lancet Microbe · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsCanadian Food Inspection Agency
FundersCenters for Disease Control and PreventionNational Institutes of Health
KeywordsOutbreakMultinational corporationSubtypingGenomeData sharingDisseminationWhole genome sequencingData scienceFood safetyFood microbiologyBiotechnologyBusinessBiologyGeneticsComputer scienceMedicineVirologyFood scienceGeneTelecommunications

Abstract

fetched live from OpenAlex

Our globalised food supply presents immense challenges to ensuring food safety, as shown by outbreaks of foodborne illnesses associated with imported foods.1Gould LH Kline J Monahan C Vierk K Outbreaks of disease associated with food imported into the United States, 1996–2014.Emerg Infect Dis. 2017; 23: 525-528Crossref PubMed Scopus (33) Google Scholar The speed with which such outbreaks are resolved often depends on how rapidly public health scientists communicate and disseminate actionable data. One such data source is whole-genome sequencing, which is the newest method of molecular subtyping and has superior discriminatory power compared with previous methods.2Allard MW Strain E Melka D et al.Practical value of food pathogen traceability through building a whole-genome sequencing network and database.J Clin Microbiol. 2016; 54: 1975-1983Crossref PubMed Scopus (179) Google Scholar Consequently, whole-genome sequencing has been and continues to be adopted by countries across the world as a tool to combat foodborne pathogens.3Thomas J Govender N McCarthy KM et al.Outbreak of listeriosis in South Africa associated with processed meat.N Engl J Med. 2020; 382: 632-643Crossref PubMed Scopus (53) Google Scholar Sequence data can be made publicly available through numerous databases (eg, the European Nucleotide Archive, the National Center for Biotechnology Information [NCBI] Sequence Read Archive, and the DNA Data Bank of Japan Sequence Read Archive). Laboratories are encouraged to share the genomes they have sequenced4Gardy JL Loman NJ Towards a genomics-informed, real-time, global pathogen surveillance system.Nat Rev Genet. 2018; 19: 9-20Crossref PubMed Scopus (214) Google Scholar and, as new genomes are made public, isolates can be clustered into genetically similar groups to facilitate the detection of potential outbreaks and sources of contamination. Here we discuss a recent case of how international collaboration and sharing of whole-genome sequencing data facilitated the identification of a novel food associated with outbreaks caused by Listeria monocytogenes in Australia, Canada, and the USA. We hope that the events we describe will encourage all public health, food safety, and other authorities to contribute their whole-genome sequencing data to public databases in as close to real-time as possible. By the end of 2019, cluster PDS000011550 in the NCBI L monocytogenes Pathogen Detection database contained 36 clinical isolates with a distinct genetic pattern. The associated illnesses, which included four deaths and six pregnancy-associated infections, began in 2016 and a suspected food vehicle could not be identified from available food exposure histories. In February, 2020, the Canadian Food Inspection Agency (CFIA) uploaded whole-genome sequencing data for a 2016 isolate from a food sample from its pathogen archives to the NCBI Pathogen Detection database. This Canadian domestic mushroom matched the outbreak cluster. Subsequently, another Canadian isolate from an imported enoki mushroom sample was uploaded to the NCBI Pathogen Detection database that matched the outbreak cluster. These isolates provided the first evidence of what contaminated food might be causing human illnesses and deaths. In late February to early March, 2020, US state and federal partners collected enoki mushroom product samples, which yielded 12 isolates whose sequences were uploaded into the NCBI Pathogen Detection database and were found to match the outbreak cluster.5US Food and Drug AdministrationOutbreak investigation of Listeria monocytogenes: enoki mushrooms (March 2020). US Food and Drug Administration, College Park, MDJune 9, 2020https://www.fda.gov/food/outbreaks-foodborne-illness/outbreak-investigation-listeria-monocytogenes-enoki-mushrooms-march-2020Date accessed: September 10, 2020Google Scholar In the middle of March, 2020, US public health officials learned via the EU Epidemic Intelligence Information System that French authorities had collected five L monocytogenes isolates from enoki mushrooms originating from South Korea in 2017. Genomic analyses showed these five food isolates were highly related to the outbreak, but those sequence data are not publicly available. In late March, 2020, further testing by the CFIA identified five samples of enoki mushroom product from South Korea that were positive for L monocytogenes; whole-genome sequencing data was a match to the outbreak cluster. Additionally, through the Food and Agriculture Organization and WHO International Food Safety Authorities Network, US federal officials were notified of six clinical isolates collected in Australia between October, 2017, and March, 2020. These isolates were uploaded to NCBI Pathogen Detection database and found to match the outbreak cluster. In April, 2020, as part of the US domestic outbreak investigation, additional imported enoki mushrooms from South Korea were sampled and L monocytogenes isolates were found that matched the outbreak. This outbreak investigation shows how international sharing of whole-genome sequencing data can decrease the time required to resolve foodborne outbreaks and reduce the burden to public health (appendix pp 1–2). None of the insights described here would have been possible without effective surveillance and international cooperation based on shared genomic data. A common cause of illness over several years and continents would not have been recognised, contaminated products would have remained in the food supply chain, and more people would have been at risk from contaminated food. Delays in aggregating and disseminating data internationally can result in the continued exposure of consumers to potentially contaminated products, whereas routine real-time data sharing can halt this exposure and save lives. We hope this case study provides incentive to all nations doing whole-genome sequencing of foodborne pathogens to upload and share their whole-genome sequencing data in real-time. By doing so, we can reduce the time a foodborne pathogen is present within the global food chain. This online publication has been corrected. The corrected version first appeared at thelancet.com/microbe on Oct 16, 2020 This online publication has been corrected. The corrected version first appeared at thelancet.com/microbe on Oct 16, 2020 We declare no competing interests. The findings and conclusions in this Comment are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention. Download .pdf (.25 MB) Help with pdf files Supplementary appendix Correction to Lancet Microbe 2020; 1: e233–34Pettengill JB, Markell A, Conrad A, et al. A multinational listeriosis outbreak and the importance of sharing genomic data. Lancet Microbe 2020; 1: e233–34—In this Comment, in paragraphs six, seven, and eight, mentions of “North Korea” should have been “South Korea”. These corrections have been made as of Oct 16, 2020. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaOpen science
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.127
GPT teacher head0.326
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it