An integrative approach to identify Bacillus weihenstephanensis resistance biomarkers using gene expression quantification throughout acid inactivation.

The aim of this study was to define an integrative approach to identify resistance biomarkers using gene expression quantification and mathematical modelling. Mid-exponentially growing cells were transferred into acid conditions (BHI, pH 4.6) to obtain inactivation kinetics, performed in triplicate. The inactivation curve was fitted with a mixed Weibull model. This model allowed to differentiate two subpopulations with various acid resistances among the initial population. In parallel, differential gene expression was quantified by RT-qPCR. While narL was down-regulated throughout acid inactivation, sigB and katA were up-regulated. sigB expression up-regulation peak was correlated to the less resistant subpopulation when katA up-regulation, was correlated to the more resistant subpopulation. Moreover, differences in population structure were highlighted between each replicate. The higher proportion of the more resistant subpopulation was linked to a higher katA gene expression. These results suggest that sigB and katA might be used as different types of biomarkers, for instance to track moderate and high acid-resistance, respectively. The use of this approach combining RT-qPCR and predictive modelling to track cellular biomarker variations appears as an interesting tool to take into account physiological cell responses into mathematical modelling, allowing an accurate prediction of microbial behaviour.