Assessing phenotypic virulence of Salmonella enterica across serovars and sources
IntroductionWhole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, inc...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1184387/full |
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| author | Sara Petrin Sara Petrin Lucas Wijnands Elisa Benincà Lapo Mughini-Gras Lapo Mughini-Gras Ellen H. M. Delfgou-van Asch Laura Villa Massimiliano Orsini Carmen Losasso John E. Olsen Lisa Barco |
| author_facet | Sara Petrin Sara Petrin Lucas Wijnands Elisa Benincà Lapo Mughini-Gras Lapo Mughini-Gras Ellen H. M. Delfgou-van Asch Laura Villa Massimiliano Orsini Carmen Losasso John E. Olsen Lisa Barco |
| author_sort | Sara Petrin |
| collection | DOAJ |
| description | IntroductionWhole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica.MethodsTo test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed.Results and DiscussionP(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)]. |
| first_indexed | 2024-03-13T07:08:14Z |
| format | Article |
| id | doaj.art-0d52689f8dbc4b0cb33917378f1f9231 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-03-13T07:08:14Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Microbiology |
| spelling | doaj.art-0d52689f8dbc4b0cb33917378f1f92312023-06-06T04:50:53ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-06-011410.3389/fmicb.2023.11843871184387Assessing phenotypic virulence of Salmonella enterica across serovars and sourcesSara Petrin0Sara Petrin1Lucas Wijnands2Elisa Benincà3Lapo Mughini-Gras4Lapo Mughini-Gras5Ellen H. M. Delfgou-van Asch6Laura Villa7Massimiliano Orsini8Carmen Losasso9John E. Olsen10Lisa Barco11Microbial Ecology and Microrganisms Genomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, ItalyDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, DenmarkCentre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsCentre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsCentre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, NetherlandsCentre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsDepartment of Infectious Diseases, Istituto Superiore di Sanità, Rome, ItalyMicrobial Ecology and Microrganisms Genomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, ItalyMicrobial Ecology and Microrganisms Genomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, ItalyDepartment of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, DenmarkWHOA and National Reference Laboratory for Salmonellosis, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, ItalyIntroductionWhole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica.MethodsTo test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed.Results and DiscussionP(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].https://www.frontiersin.org/articles/10.3389/fmicb.2023.1184387/fullSalmonella entericawhole genome sequencingphenotypic virulenceBayesian approachgastrointestinal tract model systemprobability of infection |
| spellingShingle | Sara Petrin Sara Petrin Lucas Wijnands Elisa Benincà Lapo Mughini-Gras Lapo Mughini-Gras Ellen H. M. Delfgou-van Asch Laura Villa Massimiliano Orsini Carmen Losasso John E. Olsen Lisa Barco Assessing phenotypic virulence of Salmonella enterica across serovars and sources Frontiers in Microbiology Salmonella enterica whole genome sequencing phenotypic virulence Bayesian approach gastrointestinal tract model system probability of infection |
| title | Assessing phenotypic virulence of Salmonella enterica across serovars and sources |
| title_full | Assessing phenotypic virulence of Salmonella enterica across serovars and sources |
| title_fullStr | Assessing phenotypic virulence of Salmonella enterica across serovars and sources |
| title_full_unstemmed | Assessing phenotypic virulence of Salmonella enterica across serovars and sources |
| title_short | Assessing phenotypic virulence of Salmonella enterica across serovars and sources |
| title_sort | assessing phenotypic virulence of salmonella enterica across serovars and sources |
| topic | Salmonella enterica whole genome sequencing phenotypic virulence Bayesian approach gastrointestinal tract model system probability of infection |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1184387/full |
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