Impact of the Resistance Responses to Stress Conditions Encountered in Food and Food Processing Environments on the Virulence and Growth Fitness of Non-Typhoidal Salmonellae

Abstract

The success of Salmonella as a foodborne pathogen can probably be attributed to two major features: its remarkable genetic diversity and its extraordinary ability to adapt. Salmonella cells can survive in harsh environments, successfully compete for nutrients, and cause disease once inside the host. Furthermore, they are capable of rapidly reprogramming their metabolism, evolving in a short time from a stress-resistance mode to a growth or virulent mode, or even to express stress resistance and virulence factors at the same time if needed, thanks to a complex and fine-tuned regulatory network. It is nevertheless generally acknowledged that the development of stress resistance usually has a fitness cost for bacterial cells and that induction of stress resistance responses to certain agents can trigger changes in Salmonella virulence. In this review, we summarize and discuss current knowledge concerning the effects that the development of resistance responses to stress conditions encountered in food and food processing environments (including acid, osmotic and oxidative stress, starvation, modified atmospheres, detergents and disinfectants, chilling, heat, and non-thermal technologies) exerts on different aspects of the physiology of non-typhoidal Salmonellae, with special emphasis on virulence and growth fitness.

The effect of emulsifier type and oil inclusion on stress‐related gene expression of Salmonella typhimurium in oil‐in‐water emulsion

“…This result implies that the interaction between the positively charged emulsifier and cell membrane may be stronger, potentially providing greater protection against thermal treatment (Tsai & Tikekar, 2023). Another possible explanation is the stress caused by the high temperature itself, which can cause changes in bacterial membrane permeability and fluidity, and disrupt cellular functions, ultimately leading to the effect on bacterial inactivation (Cebrián et al., 2019; Guillén et al., 2021). Thus, the protective effect in this study could be attributed to various stress response mechanisms, including emulsifier stress and high temperature stress.…”

“…The expression levels of rpoE , rpoH , and ibpA are involved in the activation of specific heat shock proteins after thermal treatment. In various stress environments, including heat, oxidation, UV‐light, and desiccation, the alternative σ factor, σE, is regulated by the transcriptional regulator rpoE , which controls its expression levels (Amar et al., 2018; Guillén et al., 2021). Activated rpoE under stress conditions leads to the upregulation of outer membrane proteins and the β‐barrel assembly machinery complex, which works to alleviate stress on the outer membrane (Palmer & Slauch, 2020).…”

Coordinated interaction between Lon protease and catalase-peroxidase regulates virulence and oxidative stress management during Salmonellosis

“…The inactivation of cytosolic Lon protease appears to impair the stress response mechanism and causes the accumulation of partially folded proteins related to Salmonella virulence. 31 , 32 To address this possibility with respect to lon gene deletion, we used the combination of 2-D gel analysis of proteins to evaluate the global expression pattern. We compared the proteome of wild-type (WT) Salmonella to that of lon deletion mutant using 2-D gel electrophoresis.…”

Training in modern statistical methodologies and software tools for the definition and analysis of (stochastic) quantitative microbial risk assessment models with a comparison between the Hungarian and Spanish food supply chains

“…A QMRA needs mathematical models that provide quantitative estimates of the microbial response within the food chain (Allende et al., 2022 ). In this sense, the field of predictive microbiology is a well‐established methodology that defines the experimental and numerical protocols to define such models (Perez‐Rodriguez & Valero, 2013 ), that has served to define growth and inactivation models for most food pathogens (Guillén et al., 2021a , b ; Alvarenga et al., 2022 ; Georgalis et al., 2022 ). One of the main limitations of this approach is the complexity in the implementation of these mathematical models.…”