Assessing phenotypic virulence of Salmonella enterica across serovars and sources

Frontiers in Microbiology

Microbiology

Elisa Beninc๠ Ellen H. M. Delfgou-van Asch¹  Lucas Wijnands¹  Lapo Mughini-Gras²  Laura Villa³  John E. Olsen⁴  Carmen Losasso⁵  Massimiliano Orsini⁵  Sara Petrin⁶  Lisa Barco⁷ 

[1] Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands;Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands;Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, Netherlands;Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy;Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark;Microbial Ecology and Microrganisms Genomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy;Microbial Ecology and Microrganisms Genomics Laboratory, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy;Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark;WHOA and National Reference Laboratory for Salmonellosis, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy;

关键词: Salmonella enterica;    whole genome sequencing;    phenotypic virulence;    Bayesian approach;    gastrointestinal tract model system;    probability of infection;    virulence genes;   

DOI  :  10.3389/fmicb.2023.1184387

 received in 2023-03-11, accepted in 2023-05-15,  发布年份 2023

来源: Frontiers

【 摘 要 】

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)].

【 授权许可】

Unknown   
Copyright © 2023 Petrin, Wijnands, Benincà, Mughini-Gras, Delfgou-van Asch, Villa, Orsini, Losasso, Olsen and Barco.