Frans van den Berg

Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults

Background Recent evidence suggests that there is a link between metabolic diseases and bacterial populations in the gut. The aim of this study was to assess the differences between the composition of the intestinal microbiota in humans with type 2 diabetes and non-diabetic persons as control. Methods and Findings The study included 36 male adults with a broad range of age and body-mass indices (BMIs), among which 18 subjects were diagnosed with diabetes type 2. The fecal bacterial composition was investigated by real-time quantitative PCR (qPCR) and in a subgroup of subjects (N = 20) by tag-encoded amplicon pyrosequencing of the V4 region of the 16S rRNA gene. The proportions of phylum Firmicutes and class Clostridia were significantly reduced in the diabetic group compared to the control group (P = 0.03). Furthermore, the ratios of Bacteroidetes to Firmicutes as well as the ratios of Bacteroides-Prevotella group to C. coccoides-E. rectale group correlated positively and significantly with plasma glucose concentration (P = 0.04) but not with BMIs. Similarly, class Betaproteobacteria was highly enriched in diabetic compared to non-diabetic persons (P = 0.02) and positively correlated with plasma glucose (P = 0.04). Conclusions The results of this study indicate that type 2 diabetes in humans is associated with compositional changes in intestinal microbiota. The level of glucose tolerance should be considered when linking microbiota with metabolic diseases such as obesity and developing strategies to control metabolic diseases by modifying the gut microbiota.

Metabolomic studies on geographical grapes and their wines using 1H NMR analysis coupled with multivariate statistics.

Environmental vineyard conditions can affect the chemical composition or metabolites of grapes and their wines. Grapes grown in three different regions of South Korea were collected and separated into pulp, skin, and seed. The grapes were also vinified after crushing. (1)H NMR spectroscopy with pattern recognition (PR) methods was used to investigate the metabolic differences in pulp, skin, seed, and wines from the different regions. Discriminatory compounds among the grapes were Na, Ca, K, malate, citrate, threonine, alanine, proline, and trigonelline according to PR methods of principal component analysis (PCA) or partial least-squares discriminant analysis (PLS-DA). Grapes grown in regions with high sun exposure and low rainfall showed higher levels of sugar, proline, Na, and Ca together with lower levels of malate, citrate, alanine, threonine, and trigonelline than those grown in regions with relatively low sun exposure and high rainfall. Environmental effects were also observed in the complementary wines. This study demonstrates that (1)H NMR-based metabolomics coupled with multivariate statistical data sets can be useful for determining grape and wine quality.

1H Nuclear Magnetic Resonance-Based Metabolomic Characterization of Wines by Grape Varieties and Production Areas

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

Quantitatively different, yet qualitatively alike: a meta-analysis of the mouse core gut microbiome with a view towards the human gut microbiome.

Background A number of human diseases such as obesity and diabetes are associated with changes or imbalances in the gut microbiota (GM). Laboratory mice are commonly used as experimental models for such disorders. The introduction and dynamic development of next generation sequencing techniques have enabled detailed mapping of the GM of both humans and animal models. Nevertheless there is still a significant knowledge gap regarding the human and mouse common GM core and thus the applicability of the latter as an animal model. The aim of the present study was to identify inter- and intra-individual differences and similarities between the GM composition of particular mouse strains and humans. Methodology/Principal Findings A total of 1509428 high quality tag-encoded partial 16S rRNA gene sequences determined using 454/FLX Titanium (Roche) pyro-sequencing reflecting the GM composition of 32 human samples from 16 individuals and 88 mouse samples from three laboratory mouse strains commonly used in diabetes research were analyzed using Principal Coordinate Analysis (PCoA), nonparametric multivariate analysis of similarity (ANOSIM) and alpha diversity measures. A reliable cutoff threshold for low abundant taxa estimated on the basis of the present study is recommended for similar trials. Conclusions/Significance Distinctive quantitative differences in the relative abundance of most taxonomic groups between the examined categories were found. All investigated mouse strains clustered separately, but with a range of shared features when compared to the human GM. However, both mouse fecal, caecal and human fecal samples shared to a large extent not only representatives of the same phyla, but also a substantial fraction of common genera, where the number of shared genera increased with sequencing depth. In conclusion, the GM of mice and humans is quantitatively different (in terms of abundance of specific phyla and species) but share a large qualitatively similar core.

NMR relaxometry and differential scanning calorimetry during meat cooking

By combining simultaneous nuclear magnetic resonance (NMR) T(2) relaxometry and differential scanning calorimetry (DSC) on pork samples heated to nine temperature levels between 25 and 75°C, the present study investigates the relationship between thermal denaturation of meat proteins and heat-induced changes in water characteristics. Principal component analysis (PCA) on the distributed (1)H NMR T(2) relaxation data revealed that the major changes in water characteristics during heating occur between 40 and 50°C. This is probably initiated by denaturation of myosin heads, which however, could not be detected in the DSC thermograms obtained directly on the meat. In contrast, the DSC thermograms revealed endothermic transitions at 54, 65 and 77°C, probably reflecting the denaturation of myosin (rods and light chain), sarcoplasmic proteins together with collagen and actin, respectively. Simultaneous modelling of DSC and NMR data by partial least squares regression (PLSR) revealed a correlation between denaturation of myosin rods and light chains at ∼53-58°C and heat-induced changes in myofibrillar water (T(2) relaxation time ∼10-60ms) as well as between actin denaturation at ∼80-82°C and expulsion of water from the meat. Accordingly, the present study demonstrates a direct relationship between thermal denaturation of specific proteins/protein structures and heat-induced changes in water mobility during heating of pork.

1H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains.

(1)H NMR spectroscopy coupled with multivariate statistical analysis was used for the first time to investigate metabolic changes in musts during alcoholic fermentation and wines during aging. Three Saccharomyces cerevisiae yeast strains (RC-212, KIV-1116, and KUBY-501) were also evaluated for their impacts on the metabolic changes in must and wine. Pattern recognition (PR) methods, including PCA, PLS-DA, and OPLS-DA scores plots, showed clear differences for metabolites among musts or wines for each fermentation stage up to 6 months. Metabolites responsible for the differentiation were identified as valine, 2,3-butanediol (2,3-BD), pyruvate, succinate, proline, citrate, glycerol, malate, tartarate, glucose, N-methylnicotinic acid (NMNA), and polyphenol compounds. PCA scores plots showed continuous movements away from days 1 to 8 in all musts for all yeast strains, indicating continuous and active fermentation. During alcoholic fermentation, the highest levels of 2,3-BD, succinate, and glycerol were found in musts with the KIV-1116 strain, which showed the fastest fermentation or highest fermentative activity of the three strains, whereas the KUBY-501 strain showed the slowest fermentative activity. This study highlights the applicability of NMR-based metabolomics for monitoring wine fermentation and evaluating the fermentative characteristics of yeast strains.

Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

Abstract This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics – multivariate data analysis – it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems that could not otherwise be handled before. The different mathematical models are all exemplified by food related subjects to underline the generic use of the models within the food chain. Applications will be given from meat storage, vegetable characterization, fish quality monitoring and industrial food processing, and will cover areas such as analysis of variance, monitoring and handling of sampling variation, calibration, exploration/data mining and hard modeling.

Evidence of vintage effects on grape wines using 1H NMR-based metabolomic study.

The chemical composition of grape wines varies with grape variety, environmental factors of climate and soil, and bacterial strains, which can each affect the wine quality. Using (1)H NMR analysis coupled with multivariate statistical data sets, we investigated the effects of grape vintage on metabolic profiles of wine and the relationship between wine metabolites and meteorological data. Principal component analysis (PCA) showed a clear differentiation between Meoru wines that were vinified with the same yeast strain and Meoru grapes harvested from the same vineyard but with a different vintage. The metabolites contributing to the differentiation were identified as 2,3-butandiol, lactic acid, alanine, proline, gamma-aminobutyric acid (GABA), choline, and polyphenols, by complementary PCA loading plot. Markedly higher levels of proline, lactic acid and polyphenols were observed in the 2006 vintage wines compared to those of 2007 vintage, showing excellent agreement with the meteorological data that the sun-exposed time and rainfall in 2006 were approximately two times more and four times less, respectively, than those in 2007. These results revealed the important role of climate during ripening period in the chemical compositions of the grape. This study highlights the reliability of NMR-based metabolomic data by integration with meteorological data in characterizing wine or grape.

Near-Infrared Spectroscopy for Cocrystal Screening. A Comparative Study with Raman Spectroscopy

Near-infrared (NIR) spectroscopy is a well-established technique for solid-state analysis, providing fast, noninvasive measurements. The use of NIR spectroscopy for polymorph screening and the associated advantages have recently been demonstrated. The objective of this work was to evaluate the analytical potential of NIR spectroscopy for cocrystal screening using Raman spectroscopy as a comparative method. Indomethacin was used as the parent molecule, while saccharin and l-aspartic acid were chosen as guest molecules. Molar ratios of 1:1 for each system were subjected to two types of preparative methods. In the case of saccharin, liquid-assisted cogrinding as well as cocrystallization from solution resulted in a stable 1:1 cocrystalline phase termed IND-SAC cocrystal. For l-aspartic acid, the solution-based method resulted in a polymorphic transition of indomethacin into the metastable alpha form retained in a physical mixture with the guest molecule, while liquid-assisted cogrinding did not induce any changes in the crystal lattice. The good chemical peak selectivity of Raman spectroscopy allowed a straightforward interpretation of sample data by analyzing peak positions and comparing to those of pure references. In addition, Raman spectroscopy provided additional information on the crystal structure of the IND-SAC cocrystal. The broad spectral line shapes of NIR spectra make visual interpretation of the spectra difficult, and consequently, multivariate modeling by principal component analysis (PCA) was applied. Successful use of NIR/PCA was possible only through the inclusion of a set of reference mixtures of parent and guest molecules representing possible solid-state outcomes from the cocrystal screening. The practical hurdle related to the need for reference mixtures seems to restrict the applicability of NIR spectroscopy in cocrystal screening.

Influence of solvent evaporation rate and formulation factors on solid dispersion physical stability.

Lorazepam is a strong sedative for intensive care patients and a commonly used method of administering it to the patient is by infusion of a freshly prepared lorazepam solution. During lorazepam infusion often unwanted lorazepam crystallization occurs, resulting in line obstruction and reduced lorazepam concentrations. With the aid of solubility measurements a solid-liquid phase diagram for lorazepam in mixtures of a commercially available lorazepam solution and an aqueous glucose solution was determined. This confirmed that the glucose solution acts as an anti-solvent, greatly reducing the lorazepam solubility in the infusion solution. Three approaches are proposed to obtain stable lorazepam solutions upon mixing both solutions and thus to prevent crystallization during infusion: (1) using a high lorazepam concentration, and thus a lower glucose solution volume fraction, in the mixed solution; (2) using an elevated temperature during solution preparation and administration; (3) reducing the lorazepam concentration in the commercial lorazepam solution.New chemical entities (NCEs) often show poor water solubility necessitating solid dispersion formulation. The aim of the current study is to employ design of experiments in investigating the influence of one critical process factor (solvent evaporation rate) and two formulation factors (PVP:piroxicam ratio (PVP:PRX) and PVP molecular weight (P(MW))) on the physical stability of PRX solid dispersion prepared by the solvent evaporation method. The results showed the rank order of an increase in factors contributing to a decrease in the extent of PRX nucleation being evaporation rate>PVP:PRX>P(MW). The same rank order was found for the decrease in the extent of PRX crystal growth in PVP matrices from day 0 up to day 12. However, after 12days the rank became PVP:PRX>evaporation rate>P(MW). The effects of an increase in evaporation rate and PVP:PRX ratio in stabilizing PRX were of the same order of magnitude, while the effect from P(MW) was much smaller. The findings were confirmed by XRPD. FT-IR showed that PRX recrystallization in the PVP matrix followed Ostwalds step rule, and an increase in the three factors all led to increased hydrogen bonding interaction between PRX and PVP. The present study showed the applicability of the Quality by Design approach in solid dispersion research, and highlights the need for multifactorial analysis.