Metabolomics and microbiological profile of Italian mozzarella cheese produced with buffalo and cow milk

Effect of ZnO Nanoparticles on Human Bone Marrow Mesenchymal Stem Cells: Viability, Morphology, Particles Uptake, Cell Cycle and Metabolites

Despite the growing interest in nanoparticles (NPs), the evaluation of their safety use has to be deeply considered, but standardized procedures for the evaluation of their toxicity have not been defined. In vitro methods are ideal in toxicology research because they can rapidly provide reproducible results while preventing the use of animals. Primary cells are considered a better option as model systems for predicting toxicological behavior, although several cell types do not survive enough in culture and isolated cells can have substantial variability when obtained from different donors. Recently, a new test for acute toxicity based on the use of human bone marrow mesenchymal stem cells (hBMMSCs) has been developed and successfully tested in our laboratory following the ICCVAM (Interagency Coordinating Committee on the Validation of Alternative Methods) guidelines [1]. Along these lines, the aim of this study is to evaluate the acute cytotoxicity of ZnO nanoparticles using the new toxicity test based on hBMMSCs, while comparing their behavior with respect to the toxicity of ZnO micrometer ones. For this reason, we assessed the citotoxicity by performing Neutral Red assay, the cellular uptake by transmission electron microscopy and the effects on hBMMSCs cycle by FACS analysis. Furthermore, we also analyzed by means of GC-MS the polar metabolite profile of hBMMSCs samples treated with ZnO micro- and nanoparticles. Our results show that despite the slight differences in terms of cytotoxicity, nano and microparticles show a very different behavior with respect to their effects on hBMMSCs cycle, metabolite profile and cellular uptake.

Intrinsic Parameters and Bacterial Growth Prediction in a Brazilian Minimally Ripened Cheese (Coalho) during Refrigerated Storage

This study evaluated the microbiological and physicochemical characteristics in different commercial brands of a Brazilian minimally ripened (coalho) cheese during 60 days of storage under refrigeration. Combinations of maximum and minimum values of water activity and pH determined in cheese samples at refrigeration temperature (7°C) were used in a bacterial growth prediction analysis. Maximum growth rate (Grmax) was estimated for different pathogenic and/or spoilage bacteria using the ComBase Predictor. Results of microbiological characterization analyses showed persistent high counts for all monitored microbial groups ( Lactobacillus spp., Lactococcus spp., Enterococcus spp., Staphylococcus spp., Enterobacteriaceae, proteolytic and lipolytic microorganisms, and fungi) in cheese samples; no dominant microbial group was observed over time. Values of pH (6.03 ± 0.16 to 7.28 ± 0.55), acidity (0.15% ± 0.09% to 0.66% ± 0.26%), sodium chloride (1.05% ± 0.19% to 1.97% ± 0.75%), and water activity (0.948 ± 0.020 to 0.974 ± 0.012) did not vary in cheese samples during storage. Estimated Grmax values for the tested bacteria were in the range of 0.004 to 0.044 log CFU/h. Highest Grmax values (0.005 to 0.044 log CFU/h) were predicted for the psychrotrophic Aeromonas hydrophila, Listeria monocytogenes, Pseudomonas spp., and Yersinia enterocolitica. Grmax values predicted for Escherichia coli, Salmonella spp., and Staphylococcus aureus were in the range of 0.004 to 0.016 log CFU/h. These results indicate unsatisfactory microbiological characteristics of commercially available coalho cheese. Physicochemical characteristics of commercial coalho cheese stored under refrigeration allow bacterial growth to occur, indicating higher risk for fast growth of contaminant bacteria in this product.

Fermented Foods as a Dietary Source of Live Organisms

The popularity of fermented foods and beverages is due to their enhanced shelf-life, safety, functionality, sensory, and nutritional properties. The latter includes the presence of bioactive molecules, vitamins, and other constituents with increased availability due to the process of fermentation. Many fermented foods also contain live microorganisms that may improve gastrointestinal health and provide other health benefits, including lowering the risk of type two diabetes and cardiovascular diseases. The number of organisms in fermented foods can vary significantly, depending on how products were manufactured and processed, as well as conditions and duration of storage. In this review, we surveyed published studies in which lactic acid and other relevant bacteria were enumerated from the most commonly consumed fermented foods, including cultured dairy products, cheese, fermented sausage, fermented vegetables, soy-fermented foods, and fermented cereal products. Most of the reported data were based on retail food samples, rather than experimentally produced products made on a laboratory scale. Results indicated that many of these fermented foods contained 105-7 lactic acid bacteria per mL or gram, although there was considerable variation based on geographical region and sampling time. In general, cultured dairy products consistently contained higher levels, up to 109/mL or g. Although few specific recommendations and claim legislations for what constitutes a relevant dose exist, the findings from this survey revealed that many fermented foods are a good source of live lactic acid bacteria, including species that reportedly provide human health benefits.

A Metabolomics Approach Uncovers Differences between Traditional and Commercial Dairy Products in Buryatia (Russian Federation)

Commercially available and traditional dairy products differ in terms of their manufacturing processes. In this study, commercially available and traditionally fermented cheese, yogurt, and milk beverages were analyzed and compared. The metabolomic technique of ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF) in the MS^E mode was used in combination with statistical methods, including univariate analysis and chemometric analysis, to determine the differences in metabolite profiles between commercially and traditionally fermented dairy products. The experimental results were analyzed statistically and showed that traditional and commercial dairy products were well differentiated in both positive and negative ion modes, with significant differences observed between the samples. After screening for metabolite differences, we detected differences between traditional milk beverages and yogurt and their commercial counterparts in terms of the levels of compounds such as l-lysine, l-methionine, l-citrulline, l-proline, l-serine, l-valine and l-homocysteine, and of short peptides such as Asp-Arg, Gly-Arg, His-Pro, Pro-Asn. The greatest difference between commercially available and traditional cheese was in the short peptide composition, as commercially available and traditional cheese is rich in short peptides.

Potential characterization of yeasts isolated from Kazak artisanal cheese to produce flavoring compounds

Cheese is a typical handcrafted fermented food in Kazak minority from the Uighur Autonomy Region in China and Central Asia. Among the microbial community that is responsible for Kazak cheese fermentation, yeasts play important role in flavor formation during ripening. To develop ripening cultures, we isolated 123 yeasts from 25 cheese products in Kazak, and identified 87 isolates by the D1/D2 domain of the large subunit rRNA gene sequence. Pichia kudriavzevii was the dominant yeast in Kazak cheese, followed by Kluyveromyces marxianus and Kluyveromyces lactis. Of these, the ability to exhibit enzyme of dominant isolates and contribution to the typical flavor of cheeses was assessed. Enzyme producing yeast strains were inoculated in Hazak cheese‐like medium and volatile compounds were identified by head space solid phase micro extraction coupled to gas chromatography and mass spectroscopy. Pichia kudriavzevii N‐X displayed the strongest extracellular proteolytic and activity on skim milk agar and produced a range of aroma compounds (ethanol, ethyl acetate, 3‐methylbutanol, and acetic acid) for Kazak cheese flavor, could be explored as ripening cultures in commercial production of Kazak cheeses.

Lactation-related metabolic mechanism investigated based on mammary gland metabolomics and 4 biofluids' metabolomics relationships in dairy cows

Background

Lactation is extremely important for dairy cows; however, the understanding of the underlying metabolic mechanisms is very limited. This study was conducted to investigate the inherent metabolic patterns during lactation using the overall biofluid metabolomics and the metabolic differences from non-lactation periods, as determined using partial tissue-metabolomics. We analyzed the metabolomic profiles of four biofluids (rumen fluid, serum, milk and urine) and their relationships in six mid-lactation Holstein cows and compared their mammary gland (MG) metabolomic profiles with those of six non-lactating cows by using gas chromatography-time of flight/mass spectrometry.

Results

In total, 33 metabolites were shared among the four biofluids, and 274 metabolites were identified in the MG tissues. The sub-clusters of the hierarchical clustering analysis revealed that the rumen fluid and serum metabolomics profiles were grouped together and highly correlated but were separate from those for milk. Urine had the most different profile compared to the other three biofluids. Creatine was identified as the most different metabolite among the four biofluids (VIP = 1.537). Five metabolic pathways, including gluconeogenesis, pyruvate metabolism, the tricarboxylic acid cycle (TCA cycle), glycerolipid metabolism, and aspartate metabolism, showed the most functional enrichment among the four biofluids (false discovery rate < 0.05, fold enrichment >2). Clear discriminations were observed in the MG metabolomics profiles between the lactating and non-lactating cows, with 54 metabolites having a significantly higher abundance (P < 0.05, VIP > 1) in the lactation group. Lactobionic acid, citric acid, orotic acid and oxamide were extracted by the S-plot as potential biomarkers of the metabolic difference between lactation and non-lactation. The TCA cycle, glyoxylate and dicarboxylate metabolism, glutamate metabolism and glycine metabolism were determined to be pathways that were significantly impacted (P < 0.01, impact value >0.1) in the lactation group. Among them, the TCA cycle was the most up-regulated pathway (P < 0.0001), with 7 of the 10 related metabolites increased in the MG tissues of the lactating cows.

Conclusions

The overall biofluid and MG tissue metabolic mechanisms in the lactating cows were interpreted in this study. Our findings are the first to provide an integrated insight and a better understanding of the metabolic mechanism of lactation, which is beneficial for developing regulated strategies to improve the metabolic status of lactating dairy cows.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4314-1) contains supplementary material, which is available to authorized users.

Functional properties of Mozzarella cheese for its end use application

Cheese is an extremely versatile food product that has a wide range of flavor, textures and end uses. The vast majority of cheese is eaten not by itself, but as part of another food. As an ingredient in foods, cheese is required to exhibit functional characteristics in the raw as well as cooked forms. Melting, stretching, free-oil formation, elasticity and browning are the functional properties considered to be significant for Mozzarella cheese. When a cheese is destined for its end use, some of its unique characteristics play a significant role in the products acceptability. For instance pH of cheese determines the cheese structure which in turn decides the cheese shredability and meltability properties. The residual galactose content in cheese mass determines the propensity of cheese to brown during baking. Development of 'tailor-made cheese' involves focusing on manipulation of such unique traits of cheese in order to obtain the desired characteristics for its end use application suiting the varied consumer's whims and wishes. This comprehensive review paper will provide an insight to the cheese maker regarding the factors determining the functional properties of cheese and also for the pizza manufacturers to decide which age of cheese to be used which will perform well in baking applications.

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.

Bacterial microbiota of Kazakhstan cheese revealed by single molecule real time (SMRT) sequencing and its comparison with Belgian, Kalmykian and Italian artisanal cheeses

BACKGROUND

In Kazakhstan, traditional artisanal cheeses have a long history and are widely consumed. The unique characteristics of local artisanal cheeses are almost completely preserved. However, their microbial communities have rarely been reported. The current study firstly generated the Single Molecule, Real-Time (SMRT) sequencing bacterial diversity profiles of 6 traditional artisanal cheese samples of Kazakhstan origin, followed by comparatively analyzed the microbiota composition between the current dataset and those from cheeses originated from Belgium, Russian Republic of Kalmykia (Kalmykia) and Italy.

RESULTS

Across the Kazakhstan cheese samples, a total of 238 bacterial species belonging to 14 phyla and 140 genera were identified. Lactococcus lactis (28.93%), Lactobacillus helveticus (26.43%), Streptococcus thermophilus (12.18%) and Lactobacillus delbrueckii (12.15%) were the dominant bacterial species for these samples. To further evaluate the cheese bacterial diversity of Kazakhstan cheeses in comparison with those from other geographic origins, 16S rRNA datasets of 36 artisanal cheeses from Belgium, Russian Republic of Kalmykia (Kalmykia) and Italy were retrieved from public databases. The cheese bacterial microbiota communities were largely different across sample origins. By principal coordinate analysis (PCoA) and multivariate analysis of variance (MANOVA), the structure of the Kazakhstan artisanal cheese samples was found to be different from those of the other geographic origins. Furthermore, the redundancy analysis (RDA) identified 16 bacterial OTUs as the key variables responsible for such microbiota structural difference.

CONCLUSION

Our results together suggest that the diversity of bacterial communities in different groups is stratified by geographic region. This study does not only provide novel information on the bacterial microbiota of traditional artisanal cheese of Kazakhstan at species level, but also interesting insights into the bacterial diversity of artisanal cheeses of various geographical origins.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.