Fungi under Modified Atmosphere—The Effects of CO2 Stress on Cell Membranes and Description of New Yeast Stenotrophomyces fumitolerans gen. nov., sp. nov.

High levels of carbon dioxide are known to inhibit the growth of microorganisms. A total of twenty strains of filamentous fungi and yeasts were isolated from habitats with enriched carbon dioxide concentration. Most strains were derived from modified atmosphere packed (MAP) food products or mofettes and were cultivated under an atmosphere of 20% CO₂ and 80% O₂. The influence of CO₂ on fungal cell membrane fatty acid profiles was examined in this study. Major changes were the increase in linolenic acid (C18:3 cis 9, 12, 15) and, additionally in most strains, linoleic acid (C18:2 cis 9, 12) with a maximum of 24.8%, at the expense of oleic (C18:1 cis 9), palmitic (C16:0), palmitoleic (C16:1 cis 9) and stearic acid (C18:0). The degree of fatty acid unsaturation increased for all of the strains in the study, which consequently led to lower melting temperatures of the cell membranes after incubation with elevated levels of CO₂, indicating fluidization of the membrane and a potential membrane malfunction. Growth was reduced in 18 out of 20 strains in laboratory experiments and a change in pigmentation was observed in several strains. Two of the isolated strains, strain WT5 and strain WR1, were found to represent a hitherto undescribed yeast for which the new genus and species Stenotrophomyces fumitolerans (MB# 849906) is proposed

Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%

Investigation and Rapid Discrimination of Food-Related Bacteria under Stress Treatments Using IR Microspectroscopy

Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference. Spectral changes compared to normal growth conditions without stressors were found in the spectral regions of 900–1500 cm−1 and 1500–1700 cm−1. These differences might stem from changes in the protein secondary structure, exopolymer production, and concentration of nucleic acids, lipids, and polysaccharides. As a result, a model for the discrimination of the studied microorganisms at the genus, species and strain level was established, with an accuracy of 96.6%. This was achieved despite the inclusion of various stress conditions and times after incubation of the bacteria. In addition, a model was developed for each individual microorganism, to separate each stress condition or regular treatment with 100% accuracy