Application of a Nonlinear Model to Transcript Levels of Upregulated Stress Response Gene ibpA in Stationary-Phase Salmonella enterica Subjected to Sublethal Heat Stress

The genetic response of Salmonella Typhimurium during trans-cinnamaldehyde assisted heat treatment and its correlation with bacterial resistance in different low moisture food components

Our previous study found that water activity (a_w)- and matrix-dependent bacterial resistance wasdeveloped in Salmonella Typhimurium during antimicrobial-assisted heat treatment in low moisture foods (LMFs) matrices. To better understand the molecular mechanism behind the observed bacterial resistance, gene expression analysis was conducted on S. Typhimurium adapted to different conditions with or without the trans-cinnamaldehyde (CA)-assisted heat treatment via quantitative polymerase chain reaction (qPCR). Expression profiles of nine stress-related genes were analyzed. The upregulation of rpoH and dnaK and downregulation of ompC were observed during bacterial adaptation in LMF matrices and the combined heat treatment, which likely contributed to the bacterial resistance during the combined treatment. Their expression profiles were partially consistent with the previously-observed effect of a_w or matrix on bacterial resistance. The upregulation of rpoE, otsB, proV, and fadA was also observed during adaptation in LMF matrices and might contribute to desiccation resistance, but likely did not contribute to bacterial resistance during the combined heat treatment. The observed upregulation of fabA and downregulation of ibpA could not be directly linked to bacterial resistance to either desiccation or the combined heat treatment. The results may assist the development of more efficient processing methods against S. Typhimurium in LMFs.

Increased heat tolerance and transcriptome analysis of Salmonella enterica Enteritidis PT 30 heat-shocked at 42 ℃

In this study, we compared the heat tolerance parameter (D_65℃) values of Salmonella enterica serovar Enteritidis PT 30 (S. Enteritidis ) heat adapted at different degrees (at 42 ℃ for 20-180 min) and cultivated using two methods. The treated group with the highest D_65℃ value (LP-42 ℃-60 min) and the untreated groups (Control-TSB and Control-TSA) were subjected to transcriptome analysis. Heat-adaptation increased the D_65℃ values of S. Enteritidis by 24.5-60.8%. The D_65℃ values of the LP-42 ℃-60 min group (1.85 ± 0.13 min, 7.7% higher) was comparable to that of the Control-TSA. A total of 483 up- and 443 downregulated genes of S. enteritidis were identified in the LP-42 ℃-60 min group (log₂fold change > 1, adjusted p-value < 0.05). Among these genes, 5 co-expressed and 15 differentially expressed genes in the LP-42 ℃-60 min and Control-TSA grops possibly contributed to the high D_65℃ values of S. Enteritidis . The Rpo regulon was involved in the heat adaptation of S. Enteritidis , as evidenced by the significant upregulation of rpoS, rpoN, and rpoE. KEGG enrichment pathways, such as biosynthesis of secondary metabolites, tricarboxylic acid, and ribosomes were identified and mapped to reveal the molecular mechanisms of S. enteritidis during heat adaptation. This study quantified the enhanced heat tolerance of S. Enteritidis heat adapted at different degrees of heat-adaptation. The results of this study may serve as a basis for elucidating the molecular mechanisms underlying the enhanced heat tolerance at the transcriptome level.

A Population Balance Model to Describe the Evolution of Sublethal Injury

The detection and quantification of sublethal injury (SI) of pathogenic microorganisms has become a common procedure when assessing the efficiency of microbial inactivation treatments. However, while a plethora of studies investigates SI in function of time, no suitable modelling procedure for SI data has been proposed thus far. In this study, a new SI model structure was developed that relies on existing microbial inactivation models. This model is based on the description of inactivation kinetics between the subpopulations of healthy, sublethally injured and dead cells. The model was validated by means of case studies on previously published results, modelled by different inactivation models, i.e., (i) log-linear inactivation; (ii) biphasic inactivation; and (iii) log-linear inactivation with tailing. Results were compared to those obtained by the traditional method that relies on calculating SI from independent inactivation models on non-selective and selective media. The log-linear inactivation case study demonstrated that the SI model is equivalent to the use of independent models when there can be no mistake in calculating SI. The biphasic inactivation case study illustrated how the SI model avoids unrealistic calculations of SI that would otherwise occur. The final case study on log-linear inactivation with tailing clarified that the SI model provides a more mechanistic description than the independent models, in this case allowing the reduction of the number of model parameters. As such, this paper provides a comprehensive overview of the potential and applications for the newly presented SI model.