4.6 Article

Assessing phenotypic virulence of Salmonella enterica across serovars and sources

期刊

FRONTIERS IN MICROBIOLOGY
卷 14, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2023.1184387

关键词

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

向作者/读者索取更多资源

Whole genome sequencing (WGS) is a standard typing technique used for characterizing foodborne pathogens. This study aimed to determine if different genes can predict the probability of infection [P(inf)]. Results showed variability in P(inf) values among and within serovars, with no clear pattern observed. Additionally, significant associations between P(inf) and biofilm formation were found.
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)].

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据