Assessing phenotypic virulence of Salmonella enterica across serovars and sources
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Assessing phenotypic virulence of Salmonella enterica across serovars and sources. / Petrin, Sara; Wijnands, Lucas; Benincà, Elisa; Mughini-Gras, Lapo; Delfgou-van Asch, Ellen H.M.; Villa, Laura; Orsini, Massimiliano; Losasso, Carmen; Olsen, John E.; Barco, Lisa.
In: Frontiers in Microbiology, Vol. 14, 1184387, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Assessing phenotypic virulence of Salmonella enterica across serovars and sources
AU - Petrin, Sara
AU - Wijnands, Lucas
AU - Benincà, Elisa
AU - Mughini-Gras, Lapo
AU - Delfgou-van Asch, Ellen H.M.
AU - Villa, Laura
AU - Orsini, Massimiliano
AU - Losasso, Carmen
AU - Olsen, John E.
AU - Barco, Lisa
N1 - Publisher Copyright: Copyright © 2023 Petrin, Wijnands, Benincà, Mughini-Gras, Delfgou-van Asch, Villa, Orsini, Losasso, Olsen and Barco.
PY - 2023
Y1 - 2023
N2 - Introduction: Whole 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. Methods: To 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 Discussion: P(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)].
AB - Introduction: Whole 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. Methods: To 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 Discussion: P(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)].
KW - Bayesian approach
KW - gastrointestinal tract model system
KW - phenotypic virulence
KW - probability of infection
KW - Salmonella enterica
KW - virulence genes
KW - whole genome sequencing
U2 - 10.3389/fmicb.2023.1184387
DO - 10.3389/fmicb.2023.1184387
M3 - Journal article
C2 - 37346753
AN - SCOPUS:85162266430
VL - 14
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
SN - 1664-302X
M1 - 1184387
ER -
ID: 358560836