Microbial variability in growth and heat resistance of a pathogen and a spoiler : All variabilities are equal but some are more equal than others

Quantitative microbiology is used in risk assessment studies, microbial shelf life studies, product development, and experimental design. Realistic prediction is, however, complicated by different sources of variability. The final concentration of microorganisms at the moment of consumption is affected by different sources of variability: variability in the storage times and temperatures, variability in product characteristics, variability in process characteristics, variability in the initial contamination of the raw materials, and last but not least, microbiological variability.This article compares different sources of microbiological variability in growth and inactivation kinetics of a pathogen and a spoiler, namely experimental variability, reproduction variability (within strain variability), strain variability (between strain variability) and variability between individual cells within a population (population heterogeneity). Comparison of the different sources of microbiological variability also allows to prioritize their importance. In addition, the microbiological variability is compared to other variability factors encountered in a model food chain to evaluate the impact of different variability factors on the variability in microbial levels encountered in the final product.</p

Gene profiling-based phenotyping for identification of cellular parameters that contribute to fitness, stress-tolerance and virulence of Listeria monocytogenes variants.

Microbial population heterogeneity allows for a differential microbial response to environmental stresses and can lead to the selection of stress resistant variants. In this study, we have used two different stress resistant variants of Listeria monocytogenes LO28 with mutations in the rpsU gene encoding ribosomal protein S21, to elucidate features that can contribute to fitness, stress-tolerance and host interaction using a comparative gene profiling and phenotyping approach. Transcriptome analysis showed that 116 genes were upregulated and 114 genes were downregulated in both rpsU variants. Upregulated genes included a major contribution of SigB-controlled genes such as intracellular acid resistance-associated glutamate decarboxylase (GAD) (gad3), genes involved in compatible solute uptake (opuC), glycerol metabolism (glpF, glpK, glpD), and virulence (inlA, inlB). Downregulated genes in the two variants involved mainly genes involved in flagella synthesis and motility. Phenotyping results of the two rpsU variants matched the gene profiling data including enhanced freezing resistance conceivably linked to compatible solute accumulation, higher glycerol utilisation rates, and better adhesion to Caco 2 cells presumably linked to higher expression of internalins. Also, bright field and electron microscopy analysis confirmed reduced flagellation of the variants. The activation of SigB-mediated stress defence offers an explanation for the multiple-stress resistant phenotype in rpsU variants