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%.

Sphingomonas aliaeris sp. nov., a new species isolated from pork steak packed under modified atmosphere.

Species belonging to the genus Sphingomonas have been isolated from environments such as soil, water and plant tissues. Many strains are known for their capability of degrading aromatic molecules and producing extracellular polymers. A Gram-stain-negative, strictly aerobic, motile, red-pigmented, oxidase-negative, catalase-positive, rod-shaped strain, designated DH-S5T, has been isolated from pork steak packed under CO2-enriched modified atmosphere. Cell diameters were 1.5×0.9 µm. Growth optima were at 30 °C and at pH 6.0. Phylogenetic analyses based on both complete 16S rRNA gene sequence and whole-genome sequence data revealed that strain DH-S5T belongs to the genus Sphingomonas, being closely related to Sphingomonas alpina DSM 22537T (97.4 % gene sequence similarity), followed by Sphingomonas qilianensis X1T (97.4 %) and Sphingomonas hylomeconis GZJT-2T (97.3 %). The DNA G+C content was 64.4 mol%. The digital DNA-DNA hybridization value between the isolate strain and S. alpina DSM 22537T was 21.0 % with an average nucleotide identity value of 77.03 %. Strain DH-S5T contained Q-10 as the ubiquinone and major fatty acids were C18 : 1 cis 11 (39.3 %) and C16 : 1 cis 9 (12.5 %), as well as C16 : 0 (12.1 %) and C14 : 0 2-OH (11.4 %). As for polar lipids, phosphatidylcholine, phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, dimethylphosphatidylethanolamine and sphingoglycolipid could be detected, alongside traces of monomethylphosphatidylethanolamine. Based on its phenotypic, chemotaxonomic and phylogenetic characteristics, strain DH-S5T (=DSM 110829T=LMG 31606T) is classified as a representative of the genus Sphingomonas, for which the name Sphingomonas aliaeris sp. nov. is proposed.

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.

Rapid detection and discrimination of food-related bacteria using IR-microspectroscopy in combination with multivariate statistical analysis.

Spoilage microorganisms are of great concern for the food industry. While traditional culturing methods for spoilage microorganism detection are laborious and time-consuming, the development of early detection methods has gained a lot of interest in the last decades. In this work a rapid and non-destructive detection and discrimination method of eight important food-related microorganisms (Bacillus subtilis DSM 10, Bacillus coagulans DSM 1, Escherichia coli K12 DSM 498, Escherichia coli TOP10, Micrococcus luteus DSM 20030, Pseudomonas fluorescens DSM 4358, Pseudomonas fluorescens DSM 50090 and Bacillus thuringiensis israelensis DSM 5724) based on IR-microspectroscopy and chemometric evaluation was developed. Sampling was carried out directly from the surface to be tested, without the need for sample preparation such as purification, singulation, centrifugation and washing steps, as an efficient and inexpensive blotting technique using the sample carrier. IR spectra were recorded directly after the blotting from the surface of the sample carrier without any further pretreatments. A combination of data preprocessing, principal component analysis and canonical discriminant analysis was found to be suitable. The spectral range from 400 to 1750 cm-1 of the IR-microspectrosopic data was determined to be highly sensitive to the time after incubation and sample thickness, resulting in a high standard deviation. Therefore, this area was excluded from the evaluation in favor of the meaningfulness of the chemometric model and, thus, only the spectral range of specific -CH/-NH/-OH excitations (2815-3680 cm-1) was used for model development. This study showed that the differentiation of food-related microorganisms on genera, species and strain level is feasible. A leave-one-out cross-validation of the training data set showed 100% accuracy. The classification of the ungrouped test data showed with an accuracy of 94.5% that, despite the large biological variance of the analytes such as different times after incubation and the presented sampling (including its variance), a robust and meaningful model for the differentiation of food-related bacteria could be developed by data preprocessing and subsequent chemometric evaluation.