Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence
ABSTRACT Salmonella comprises more than 2,600 serovars. Very few environmental and uncommon serovars have been characterized for their potential role in virulence and human infections. A complementary in vitro and in vivo systematic high-throughput analysis of virulence was used to elucidate the ass...
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American Society for Microbiology
2020
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oai:doaj.org-article:1cbd8b03b80d40479408aade43ac7b4a2021-11-15T15:30:15ZCombining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence10.1128/mSphere.00293-202379-5042https://doaj.org/article/1cbd8b03b80d40479408aade43ac7b4a2020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00293-20https://doaj.org/toc/2379-5042ABSTRACT Salmonella comprises more than 2,600 serovars. Very few environmental and uncommon serovars have been characterized for their potential role in virulence and human infections. A complementary in vitro and in vivo systematic high-throughput analysis of virulence was used to elucidate the association between genetic and phenotypic variations across Salmonella isolates. The goal was to develop a strategy for the classification of isolates as a benchmark and predict virulence levels of isolates. Thirty-five phylogenetically distant strains of unknown virulence were selected from the Salmonella Foodborne Syst-OMICS (SalFoS) collection, representing 34 different serovars isolated from various sources. Isolates were evaluated for virulence in 4 complementary models of infection to compare virulence traits with the genomics data, including interactions with human intestinal epithelial cells, human macrophages, and amoeba. In vivo testing was conducted using the mouse model of Salmonella systemic infection. Significant correlations were identified between the different models. We identified a collection of novel hypothetical and conserved proteins associated with isolates that generate a high burden. We also showed that blind prediction of virulence of 33 additional strains based on the pan-genome was high in the mouse model of systemic infection (82% agreement) and in the human epithelial cell model (74% agreement). These complementary approaches enabled us to define virulence potential in different isolates and present a novel strategy for risk assessment of specific strains and for better monitoring and source tracking during outbreaks. IMPORTANCE Salmonella species are bacteria that are a major source of foodborne disease through contamination of a diversity of foods, including meat, eggs, fruits, nuts, and vegetables. More than 2,600 different Salmonella enterica serovars have been identified, and only a few of them are associated with illness in humans. Despite the fact that they are genetically closely related, there is enormous variation in the virulence of different isolates of Salmonella enterica. Identification of foodborne pathogens is a lengthy process based on microbiological, biochemical, and immunological methods. Here, we worked toward new ways of integrating whole-genome sequencing (WGS) approaches into food safety practices. We used WGS to build associations between virulence and genetic diversity within 83 Salmonella isolates representing 77 different Salmonella serovars. Our work demonstrates the potential of combining a genomics approach and virulence tests to improve the diagnostics and assess risk of human illness associated with specific Salmonella isolates.Alanna CrouseCatherine SchrammJean-Guillaume Emond-RheaultAdrian HerodMaud KerhoasJohn RohdeSamantha GruenheidIrena Kukavica-IbruljBrian BoyleCelia M. T. GreenwoodLawrence D. GoodridgeRafael GardunoRoger C. LevesqueDanielle MaloFrance DaigleAmerican Society for MicrobiologyarticleSalmonellaamoebaepithelial cellsfood safetyhost cell interactionmacrophagesMicrobiologyQR1-502ENmSphere, Vol 5, Iss 3 (2020) |
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Salmonella amoeba epithelial cells food safety host cell interaction macrophages Microbiology QR1-502 |
| spellingShingle |
Salmonella amoeba epithelial cells food safety host cell interaction macrophages Microbiology QR1-502 Alanna Crouse Catherine Schramm Jean-Guillaume Emond-Rheault Adrian Herod Maud Kerhoas John Rohde Samantha Gruenheid Irena Kukavica-Ibrulj Brian Boyle Celia M. T. Greenwood Lawrence D. Goodridge Rafael Garduno Roger C. Levesque Danielle Malo France Daigle Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| description |
ABSTRACT Salmonella comprises more than 2,600 serovars. Very few environmental and uncommon serovars have been characterized for their potential role in virulence and human infections. A complementary in vitro and in vivo systematic high-throughput analysis of virulence was used to elucidate the association between genetic and phenotypic variations across Salmonella isolates. The goal was to develop a strategy for the classification of isolates as a benchmark and predict virulence levels of isolates. Thirty-five phylogenetically distant strains of unknown virulence were selected from the Salmonella Foodborne Syst-OMICS (SalFoS) collection, representing 34 different serovars isolated from various sources. Isolates were evaluated for virulence in 4 complementary models of infection to compare virulence traits with the genomics data, including interactions with human intestinal epithelial cells, human macrophages, and amoeba. In vivo testing was conducted using the mouse model of Salmonella systemic infection. Significant correlations were identified between the different models. We identified a collection of novel hypothetical and conserved proteins associated with isolates that generate a high burden. We also showed that blind prediction of virulence of 33 additional strains based on the pan-genome was high in the mouse model of systemic infection (82% agreement) and in the human epithelial cell model (74% agreement). These complementary approaches enabled us to define virulence potential in different isolates and present a novel strategy for risk assessment of specific strains and for better monitoring and source tracking during outbreaks. IMPORTANCE Salmonella species are bacteria that are a major source of foodborne disease through contamination of a diversity of foods, including meat, eggs, fruits, nuts, and vegetables. More than 2,600 different Salmonella enterica serovars have been identified, and only a few of them are associated with illness in humans. Despite the fact that they are genetically closely related, there is enormous variation in the virulence of different isolates of Salmonella enterica. Identification of foodborne pathogens is a lengthy process based on microbiological, biochemical, and immunological methods. Here, we worked toward new ways of integrating whole-genome sequencing (WGS) approaches into food safety practices. We used WGS to build associations between virulence and genetic diversity within 83 Salmonella isolates representing 77 different Salmonella serovars. Our work demonstrates the potential of combining a genomics approach and virulence tests to improve the diagnostics and assess risk of human illness associated with specific Salmonella isolates. |
| format |
article |
| author |
Alanna Crouse Catherine Schramm Jean-Guillaume Emond-Rheault Adrian Herod Maud Kerhoas John Rohde Samantha Gruenheid Irena Kukavica-Ibrulj Brian Boyle Celia M. T. Greenwood Lawrence D. Goodridge Rafael Garduno Roger C. Levesque Danielle Malo France Daigle |
| author_facet |
Alanna Crouse Catherine Schramm Jean-Guillaume Emond-Rheault Adrian Herod Maud Kerhoas John Rohde Samantha Gruenheid Irena Kukavica-Ibrulj Brian Boyle Celia M. T. Greenwood Lawrence D. Goodridge Rafael Garduno Roger C. Levesque Danielle Malo France Daigle |
| author_sort |
Alanna Crouse |
| title |
Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| title_short |
Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| title_full |
Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| title_fullStr |
Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| title_full_unstemmed |
Combining Whole-Genome Sequencing and Multimodel Phenotyping To Identify Genetic Predictors of <italic toggle="yes">Salmonella</italic> Virulence |
| title_sort |
combining whole-genome sequencing and multimodel phenotyping to identify genetic predictors of <italic toggle="yes">salmonella</italic> virulence |
| publisher |
American Society for Microbiology |
| publishDate |
2020 |
| url |
https://doaj.org/article/1cbd8b03b80d40479408aade43ac7b4a |
| work_keys_str_mv |
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| _version_ |
1718427871902433280 |