A European-wide dataset to uncover adaptive traits of Listeria monocytogenes to diverse ecological niches
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Published:2022-04-28
Issue:1
Volume:9
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Félix BenjaminORCID, Sevellec Yann, Palma Federica, Douarre Pierre Emmanuel, Felten Arnaud, Radomski NicolasORCID, Mallet LudovicORCID, Blanchard Yannick, Leroux Aurélie, Soumet ChristopheORCID, Bridier ArnaudORCID, Piveteau Pascal, Ascensio ElietteORCID, Hébraud Michel, Karpíšková Renáta, Gelbíčová Tereza, Torresi MarinaORCID, Pomilio Francesco, Cammà Cesare, Di Pasquale Adriano, Skjerdal Taran, Pietzka Ariane, Ruppitsch Werner, Canelhas Monica Ricão, Papić Bojan, Hurtado AnaORCID, Wullings Bart, Bulawova Hana, Castro Hanna, Lindström Miia, Korkeala HannuORCID, Šteingolde Žanete, Kramarenko Toomas, Cabanova Lenka, Szymczak BarbaraORCID, Gareis Manfred, Oswaldi Verena, Marti Elisabet, Seyfarth Anne-Mette, Leblanc Jean-Charles, Guillier LaurentORCID, Roussel Sophie
Abstract
AbstractListeria monocytogenes (Lm) is a ubiquitous bacterium that causes listeriosis, a serious foodborne illness. In the nature-to-human transmission route, Lm can prosper in various ecological niches. Soil and decaying organic matter are its primary reservoirs. Certain clonal complexes (CCs) are over-represented in food production and represent a challenge to food safety. To gain new understanding of Lm adaptation mechanisms in food, the genetic background of strains found in animals and environment should be investigated in comparison to that of food strains. Twenty-one partners, including food, environment, veterinary and public health laboratories, constructed a dataset of 1484 genomes originating from Lm strains collected in 19 European countries. This dataset encompasses a large number of CCs occurring worldwide, covers many diverse habitats and is balanced between ecological compartments and geographic regions. The dataset presented here will contribute to improve our understanding of Lm ecology and should aid in the surveillance of Lm. This dataset provides a basis for the discovery of the genetic traits underlying Lm adaptation to different ecological niches.
Funder
EC | Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference52 articles.
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