Lactic acid bacteria (LAB) are widely used as starter cultures in the manufacture of foods. Upon preparation, these cultures undergo various stresses resulting in losses of survival and fitness. In order to find conditions for the subsequent identification of proteomic biomarkers and their exploitation for preconditioning of strains, we subjected Lactobacillus (Lb.) paracasei subsp. paracasei TMW 1.1434 (F19) to different stress qualities (osmotic stress, oxidative stress, temperature stress, pH stress and starvation stress). We analysed the dynamics of its stress responses based on the expression of stress proteins using MALDI-TOF mass spectrometry (MS), which has so far been used for species identification. Exploiting the methodology of accumulating protein expression profiles by MALDI-TOF MS followed by the statistical evaluation with cluster analysis and discriminant analysis of principle components (DAPC), it was possible to monitor the expression of low molecular weight stress proteins, identify a specific time point when the expression of stress proteins reached its maximum, and statistically differentiate types of adaptive responses into groups. Above the specific result for F19 and its stress response, these results demonstrate the discriminatory power of MALDI-TOF MS to characterize even dynamics of stress responses of bacteria and enable a knowledge-based focus on the laborious identification of biomarkers and stress proteins. To our knowledge, the implementation of MALDI-TOF MS protein profiling for the fast and comprehensive analysis of various stress responses is new to the field of bacterial stress responses. Consequently, we generally propose MALDI-TOF MS as an easy and quick method to characterize responses of microbes to different environmental conditions, to focus efforts of more elaborate approaches on time points and dynamics of stress responses.

Lactic acid bacteria are broadly employed as starter cultures in the manufacture of foods. Upon technological preparation, they are confronted with drying stress that amalgamates numerous stress conditions resulting in losses of fitness and survival. To better understand and differentiate physiological stress responses, discover general and specific markers for the investigated stress conditions, and predict optimal preconditioning for starter cultures, we performed a comprehensive genomic and quantitative proteomic analysis of a commonly used model system, Lactobacillus paracasei subsp. paracasei TMW 1.1434 (isogenic with F19) under 11 typical stress conditions, including among others oxidative, osmotic, pH, and pressure stress. We identified and quantified >1900 proteins in triplicate analyses, representing 65% of all genes encoded in the genome. The identified genes were thoroughly annotated in terms of subcellular localization prediction and biological functions, suggesting unbiased and comprehensive proteome coverage. In total, 427 proteins were significantly differentially expressed in at least one condition. Most notably, our analysis suggests that optimal preconditioning toward drying was predicted to be alkaline and high-pressure stress preconditioning. Taken together, we believe the presented strategy may serve as a prototypic example for the analysis and utility of employing quantitative-mass-spectrometry-based proteomics to study bacterial physiology.