Bacterial microcompartments (BMCs) are intracellular proteinaceous organelles devoid of a lipid membrane that encapsulates enzymes of metabolic pathways. Salmonella enterica synthesizes propanediol‐utilization BMCs containing enzymes involved in the degradation of 1,2‐propanediol. BMCs can be designed to enclose heterologous proteins, paving the way to engineered catalytic microreactors. Here, we investigate broader applicability of this design principle by directing three different enzymes to the BMC. We demonstrate that β‐galactosidase, esterase Est5, and cofactor‐dependent glycerol dehydrogenase can be directed to the BMC and copurified with the microcompartment shell in a catalytically active form. We show that the BMC shell protects enzymes from pH‐dependent but not from temperature stress. Moreover, we provide evidence that the heterologously expressed BMCs act as a moderately selective diffusion barrier for lipophilic small molecules.

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Traditional kefir, which is claimed for health-promoting properties, is made from natural grain-based kefir, while commercial kefirs are made of defined mixtures of microorganisms. Here, approaches are described how to discriminate commercial and traditional kefirs. These two groups of kefirs were characterized by in-depth analysis on the taxonomic and functional level. Cultivation-independent targeted qPCR as well as next-generation sequencing (NGS) proved a completely different microbial composition in traditional and commercial kefirs. While in the traditional kefirs, Lactobacillus kefiranofaciens was the dominant bacterial species, commercial kefirs were dominated by Lactococcus lactis. Volatile organic compounds (VOCs) analysis using headspace-gas chromatography-ion mobility spectrometry also revealed drastic differences between commercial and traditional kefirs; the former built a separate cluster together with yogurt samples. Lactose and galactose concentrations in commercial kefirs were considerably higher than in traditional kefirs, which is important regarding their health properties for people who have specific intolerances. In summary, the analyzed commercial kefirs do not resemble the microbial community and metabolite characteristics of traditional grain-based kefir. Thus, they may deliver different functional effects to the consumers, which remain to be examined in future studies.

Fermented foods, such as yogurt and kefir, contain a versatile spectrum of volatile organic compounds (VOCs), including ethanol, acetic acid, ethyl acetate, and diacetyl. To overcome the challenge of overlapping peaks regarding these key compounds, the drift tube temperature was raised in a prototypic high-temperature ion mobility spectrometer (HTIMS). This HS-GC-HTIMS was used for the volatilomic profiling of 33 traditional kefir, 13 commercial kefir, and 15 commercial yogurt samples. Pattern recognition techniques, including principal component analysis (PCA) and NNMF, in combination with non-targeted screening, revealed distinct differences between traditional and commercial kefir while showing strong similarities between commercial kefir and yogurt. Classification of fermented dairy samples into commercial yogurt, commercial kefir, traditional mild kefir, and traditional tangy kefir was also possible for both PCA- and NNMF-based models, obtaining cross-validation (CV) error rates of 0% for PCA-LDA, PCA-kNN (k = 5), and NNMF-kNN (k = 5) and 3.3% for PCA-SVM and NNMF-LDA. Through back projection of NNMF loadings, characteristic substances were identified, indicating a mild flavor composition of commercial samples, with high concentrations of buttery-flavored diacetyl. In contrast, traditional kefir showed a diverse VOC profile with high amounts of flavorful alcohols (including ethanol and methyl-1-butanol), esters (including ethyl acetate and 3-methylbutyl acetate), and aldehydes. For validation of the results and deeper understanding, qPCR sequencing was used to evaluate the microbial consortia, confirming the microbial associations between commercial kefir and commercial yogurt and reinforcing the differences between traditional and commercial kefir. The diverse flavor profile of traditional kefir primarily results from the yeast consortium, while commercial kefir and yogurt is primarily, but not exclusively, produced through bacterial fermentation. The flavor profile of fermented dairy products may be used to directly evaluate the microbial consortium using HS-GC-HTIMS analysis.