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Scope: Milk fat globule membrane (MFGM) is an essential component of milk. Bovine MFGM (bMFGM) has been shown to support cognitive development and increase relative concentrations of serum phospholipids. This study investigated bioavailability of bMFGM components after oral administration in two preclinical models to explore whether dietary bMFGM induced parallel changes to plasma and brain lipidomes. Methods and results: Transgenic APOE*3.Leiden mice (n = 18/group) and Sprague-Dawley rats (n = 12/group) were fed bMFGM-enriched (MFGM+) or Control diet, after which phospholipid profiles were determined in peripheral plasma, hippocampus and prefrontal cortex tissue by targeted mass spectrometry. Multivariate analysis of lipidomic profiles demonstrated a clear separation between MFGM+ and Control plasma samples across rodents. In plasma, sphingomyelins contributed the most to the separation of lipid patterns among both models, where three sphingomyelins (d18:1/14:0, d18:1/23:0, d18:1/23:1[9Z]) were significantly and consistently higher in the circulation of MFGM+ versus Control groups. A similar trend was observed in rat prefrontal cortex, although no significant separation of the brain lipidome was demonstrated. Conclusion: bMFGM-enriched diet alters plasma phospholipid composition in rodents, predominantly increasing sphingomyelin levels in the systemic circulation with some similar, but non-significant, trends in central regions of the brain. These changes may contribute to the beneficial effects of dietary bMFGM on neurodevelopment during early life.

Unintentional intake of allergens through food products poses a daily risk for allergic patients. Models estimating the risk of reactions mostly use intake data from general population surveys. Our study evaluates the comparability of food intake levels in the general population to those in the food allergic population. Data were collected by a 24-h recall method on 2 non-consecutive days in 38 cow's milk and/or hen's egg and 35 peanut and/or tree nut allergic adult patients. All products were assigned to food groups previously developed for allergen risk assessment. Food intake distributions from the allergic populations and a matched sample from the Dutch National Food Consumption Survey were compared, and risk assessments were performed. Food intake data was obtained for 92% of the food groups. Comparison of the intake showed no statistically significant differences between either of the two allergic populations and the general population. Consequently, only small variations in estimated risks were found, that would not result in different risk management decisions. In conclusion, food intake data from the general population can be used for food allergen risk assessment and will not lead to a relevant under- or overestimation of the risk for the food allergic population.

Background: Overweight and obesity can lead to adipose tissue inflammation, which causes insulin resistance and on the long-term type 2 diabetes mellitus (T2D). The inflammatory changes of obese-adipose tissue are characterized by macrophage infiltration and activation, but validated circulating biomarkers for adipose tissue inflammation for clinical use are still lacking. One of the most secreted enzymes by activated macrophages is chitotriosidase (CHIT1). Objective: To test whether circulating CHIT1 enzymatic activity levels reflect adipose tissue inflammation. Methods: Plasma and adipose tissue samples of 105 subjects (35 lean, 37 overweight, and 33 T2D patients) were investigated. CHIT1 mRNA levels were determined in adipose tissue-resident innate immune cells. CHIT1 mRNA levels, protein abundance, and plasma enzymatic activity were subsequently measured in adipose tissue biopsies and plasma of control subjects with varying levels of obesity and adipose tissue inflammation as well as in T2D patients. Results: In adipose tissue, CHIT1 mRNA levels were higher in stromal vascular cells compared to adipocytes, and higher in adipose tissue-residing macrophages compared to circulating monocytes (p < 0.001). CHIT1 mRNA levels in adipose tissue were enhanced in overweightcompared to lean subjects and even more in T2D patients (p < 0.05). In contrast, plasma CHIT1 enzymatic activity did not differ between lean, overweight subjects and T2D patients. A mutation of the CHIT1 gene decreases plasma CHIT1 activity. Conclusions: CHIT1 is expressed by adipose tissue macrophages and expression is higher in overweight subjects and T2D patients, indicating its potential as tissue biomarker for adipose tissue inflammation. However, these differences do not translate into different plasma CHIT1 activity levels. Moreover, a common CHIT1 gene mutation causing loss of plasma CHIT1 activity interferes with its use as a biomarker of adipose tissue inflammation. These results indicate that plasma CHIT1 activity is of limited value as a circulating biomarker for adipose tissue inflammation in human subjects.

Background. N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. Objective. To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. Method. We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. Results. The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. Conclusion. For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference.

OBJECTIVES: A key to a successful personalized nutrition (PN) strategy is tailoring the behavior intervention to achieve sustained behavior change consistent with desired health outcomes. The objective of this study was to evaluate the impact of a digitally-delivered, systems biology-based PN program (Habit, LLC, Oakland, CA), that included nutrition and behavior advice, and meals. METHODS: In this single-arm, multi-phase, open-label study (clinicaltrials.gov: NCT03424395), participants (n = 107; 67% female), 30–59 years of age, were provided a Fitbit® Activity Tracker and instructions to complete an at-home kit that included collection of DNA and capillary blood before and after (30 and 120 min) consuming a mixed macronutrient challenge beverage. Lifestyle behavior data and activity levels were collected at baseline. Participants completed a 10-week control phase and a 10-week PN program intervention phase that included tailored nutrition and behavior advice, and meals (2 meals/day, 5 days/week). Validated quality of life, perceived stress, diet self-efficacy, and eating behavior questionnaires were administered to assess behavior before and after the intervention. Data from 73 compliant participants (66% female) were included in the analysis. Anthropometric and biological results were used to categorize participants into subgroups including Protein Seekers (PS) and Range Seekers (RS). RESULTS: Participants reported feeling fuller faster after the intervention, relative to before (P = 0.002), which could be linked to the dietary recommendations and behavior advice. Analysis by gender showed men reported more control over their eating patterns during negative events at baseline, compared to women (P = 0.005). This difference was not evident at the end of the intervention with women reporting slightly more, and men slightly less control. PS entered the study with a higher weight and BMI (P

Personalized nutrition may be more effective in changing lifestyle behaviors compared to population-based guidelines. This single-arm exploratory study evaluated the impact of a 10-week personalized systems nutrition (PSN) program on lifestyle behavior and health outcomes. Healthy men and women (n = 82) completed the trial. Individuals were grouped into seven diet types, for which phenotypic, genotypic and behavioral data were used to generate personalized recommendations. Behavior change guidance was also provided. The intervention reduced the intake of calories (−256.2 kcal, p &lt, 0.0001), carbohydrates (−22.1 g, 0.0039), sugar (−13.0 g, 0.0001), total fat (−17.3 g, 0.0001), saturated fat (−5.9 g, p = 0.0003) and PUFA (−2.5 g, p = 0.0065). Additionally, BMI (−0.6 kg/m2, 0.0001), body fat (−1.2%, p = 0.0192) and hip circumference (−5.8 cm, 0.0001) were decreased after the intervention. In the subgroup with the lowest phenotypic flexibility, a measure of the body’s ability to adapt to environmental stressors, LDL (−0.44 mmol/L, p = 0.002) and total cholesterol (−0.49 mmol/L, 0.0001) were reduced after the intervention. This study shows that a PSN program in a workforce improves lifestyle habits and reduces body weight, BMI and other health-related outcomes. Health improvement was most pronounced in the compromised phenotypic flexibility subgroup, which indicates that a PSN program may be effective in targeting behavior change in health-compromised target groups.

Background: The consumption of products rich in cereal fiber and with a low glycemic index is implicated in a lower risk of metabolic diseases. Previously, we showed that the consumption of fiber-rich pasta compared with bread resulted in a lower rate of appearance of exogenous glucose and a lower glucose clearance rate quantified with a dual-isotope technique, which was in accordance with a lower insulin and glucose-dependent insulinotropic polypeptide response.Objective: To gain more insight into the acute metabolic consequences of the consumption of products resulting in differential glucose kinetics, postprandial metabolic profiles were determined.Methods: In a crossover study, 9 healthy men (mean +/- SEM age: 21 +/- 0.5 y; mean SEM body mass index (kg/m(2)): 22 +/- 0.5] consumed wheat bread (132 g) and fresh pasta (119 g uncooked) enriched with wheat bran (10%) meals. A total of 134 different metabolites in postprandial plasma samples (at -5, 30, 60, 90, 120, and 180 min) were quantified by using a gas chromatography mass spectrometry based metabolomics approach (secondary outcomes). Two-factor ANOVA and advanced multivariate statistical analysis (partial least squares) were applied to detect differences between both food products.Results: Forty-two different postprandial metabolite profiles were identified, primarily representing pathways related to protein and energy metabolism, which were on average 8% and 7% lower after the men consumed pasta rather than bread, whereas concentrations of arabinose and xylose were 58% and 53% higher, respectively. Arabinose and xylose are derived from arabinoxylans, which are important components of wheat bran. The higher bioavailability of arabinose and xylose after pasta intake coincided with a lower rate of appearance of glucose and amino acids. We speculate that this higher bioavailability is due to higher degradation of arabinoxylans by small intestinal microbiota, facilitated by the higher viscosity of arabinoxylans after pasta intake than after bread intake.Conclusion: This study suggests that wheat bran, depending on the method of processing, can increase the viscosity of the meal bolus in the small intestine and interfere with macronutrient absorption in healthy men, thereby influencing postprandial glucose and insulin responses. This trial was registered at www.controlled-trials.com as ISRCTN42106325.

Background: Self-monitoring of glucose is important for managing diabetes. Noninvasive glucose monitors are not yet available, but patients would benefit highly from such a device. Methods: We present results that may lead to a novel, point-of-care noninvasive system to measure blood glucose based on Raman spectroscopy. A hospitalized cohort of 111 subjects was measured using a custom-made Raman spectrometer system. Blood glucose reference samples were used to correlate Raman data to glucose levels, using advanced preprocessing and analysis algorithms. Results: A correlation coefficient ( R2) of .83 was found correlating independent Raman-based predictions on reference blood glucose for the full cohort. Stratification of the cohort in gender-specific groups raised correlation levels to .88 (females) and .94 (males). Glucose could be measured noninvasively with average errors as low as 0.9 mM. Conclusion: We conclude that this novel system shows promising results for the advance of noninvasive, point-of-care glucose monitoring.

OBJECTIVES: This study evaluates the impact of a digitally-delivered systems biology-based personalized nutrition (PN) program (Habit LLC, Oakland, CA), that included nutrition and behavior advice, and meals. METHODS: In this single-arm, multi-phase, open-label study (clinicaltrials.gov: NCT03424395), participants (n = 107; 67% female) were provided a Fitbit® Activity Tracker and completed an at-home kit that included collection of DNA and capillary blood before and after (30 and 120 min) consuming a mixed macronutrient challenge beverage. Participants completed a 10-week run-in and 10-week PN program intervention phase. The intervention included personalized nutrition and behavior advice and meals (2 meals/day; 5 days/week) tailored to dietary subgroups. Here we report on changes in diet, anthropometrics, and biomarkers of two subgroups (Range Seekers [RS] and Protein Seekers [PS]) that differed in anthropometrics and biological measures at baseline and were provided different dietary recommendations. RESULTS: At baseline, there were no differences between groups in nutrients assessed, however, compared to RS (n = 48), PS (n = 22) had higher BMI, weight, body fat %, waist and hip circumference (all P

We aimed to explore whether vegetable consumption according to guidelines has beneficial health effects determined with classical biomarkers and nutrigenomics technologies. Fifteen lean (age 36 ± 7 years; BMI 23.4 ± 1.7 kg m−2) and 17 obese (age 40 ± 6 years; BMI 30.3 ± 2.4 kg m−2) men consumed 50- or 200-g vegetables for 4 weeks in a randomized, crossover trial. Afterward, all subjects underwent 4 weeks of energy restriction (60 % of normal energy intake). Despite the limited weight loss of 1.7 ± 2.4 kg for the lean and 2.1 ± 1.9 kg for the obese due to energy restriction, beneficial health effects were found, including lower total cholesterol, LDL cholesterol and HbA1c concentrations. The high vegetable intake resulted in increased levels of plasma amino acid metabolites, decreased levels of 9-HODE and prostaglandin D3 and decreased levels of ASAT and ALP compared to low vegetable intake. Adipose tissue gene expression changes in response to vegetable intake were identified, and sets of selected genes were submitted to network analysis. The network of inflammation genes illustrated a central role for NFkB in (adipose tissue) modulation of inflammation by increased vegetable intake, in lean as well as obese subjects. In obese subjects, high vegetable intake also resulted in changes related to energy metabolism, adhesion and inflammation. By inclusion of sensitive omics technologies and comparing the changes induced by high vegetable intake with changes induced by energy restriction, it has been shown that part of vegetables’ health benefits are mediated by changes in energy metabolism, inflammatory processes and oxidative stress. Electronic supplementary material The online version of this article (doi:10.1007/s12263-013-0343-9) contains supplementary material, which is available to authorized users.

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples. © The Author(s) 2009.

A longitudinal experimental design in combination with metabolomics and multiway data analysis is a powerful approach in the identification of metabolites whose correlation with bioproduct formation shows a shift in time. In this paper, a strategy is presented for the analysis of longitudinal microbial metabolomics data, which was performed in order to identify metabolites that are likely inducers of phenylalanine production by Escherichia coli. The variation in phenylalanine production as a function of differences in metabolism induced by the different environmental conditions in time was described by a validated multiway statistical model. Notably, most of the metabolites showing the strongest relations with phenylalanine production seemed to hardly change in time. Apparently, potential bottlenecks in phenylalanine seem to hardly change in the course of a batch fermentation. The approach described in this study is not limited to longitudinal microbial studies but can also be applied to other (biological) studies in which similar longitudinal data need to be analyzed.

Motivation: Modern functional genomics generates high-dimensional datasets. It is often convenient to have a single simple number characterizing the relationship between pairs of such high-dimensional datasets in a comprehensive way. Matrix correlations are such numbers and are appealing since they can be interpreted in the same way as Pearson's correlations familiar to biologists. The high-dimensionality of functional genomics data is, however, problematic for existing matrix correlations. The motivation of this article is 2-fold: (i) we introduce the idea of matrix correlations to the bioinformatics community and (ii) we give an improvement of the most promising matrix correlation coefficient (the RV-coefficient) circumventing the problems of high-dimensional data. Results: The modified RV-coefficient can be used in high-dimensional data analysis studies as an easy measure of common information of two datasets. This is shown by theoretical arguments, simulations and applications to two real-life examples from functional genomics, i.e. a transcriptomics and metabolomics example. Availability: The Matlab m-files of the methods presented can be downloaded from http://www.bdagroup.nl. Contact: a.k.smilde@uva.nl

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process. © 2005 American Chemical Society.

In the present study, the effects of a large series of flavonoids on multidrug resistance proteins (MRPs) were studied in MRP1 and MRP2 transfected MDCKII cells. The results were used to define the structural requirements of flavonoids necessary for potent inhibition of MRP1- and MRP2-mediated calcein transport in a cellular model. Several of the methoxylated flavonoids are among the best MRP1 inhibitors (IC50 values, ranging between 2.7 and 14.3 μM) followed by robinetin, myricetin and quercetin (IC50 values ranging between 13.6 and 21.8 μM). Regarding inhibition of MRP2 activity especially robinetin and myricetin appeared to be good inhibitors (IC 50 values of 15.0 and 22.2 μM, respectively). Kinetic characterization revealed that the two transporters differ marginally in the apparent Km for the substrate calcein. For one flavonoid, robinetin, the kinetics of inhibition were studied in more detail and revealed competitive inhibition with respect to calcein, with apparent inhibition constants of 5.0 μM for MRP1 and 8.5 μM for MRP2. For inhibition of MRP1, a quantitative structure activity relationship (QSAR) was obtained that indicates three structural characteristics to be of major importance for MRP1 inhibition by flavonoids: the total number of methoxylated moieties, the total number of hydroxyl groups and the dihedral angle between the B- and C-ring. Regarding MRP2 mediated calcein efflux inhibition, only the presence of a flavonol B-ring pyrogallol group seems to be an important structural characteristic. Overall, this study provides insight in the structural characteristics involved in MRP inhibition and explores the differences between inhibitors of these two transporters, MRP1 and MRP2. Ultimately, MRP2 displays higher selectivity for flavonoid type inhibition than MRP1. © 2004 Elsevier Inc. All rights reserved.

Contains fulltext : 57316.pdf (Publisher’s version ) (Closed access) The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. We have used two-dimensional gel electrophoresis (2-DE), oligonucleotide microarrays and principal component analysis (PCA) to characterize the U937 cell line as a model system for the differentiation of monocytes into macrophages. A total of 226 differentially expressed proteins were found, of which 41 were selected by PCA for identification using matrix-assisted laser desorption/ionization tandem mass spectrometry. Based on the PCA results, three marker proteins were selected for confirmation of differential expression using Western blot and quantitative real time-PCR. The selected marker proteins were: gamma interferon inducible lysosomal thiol reductase, cathepsin D and adipocyte-fatty acid binding protein. All three proved to be good differentiation markers for macrophage maturation of U937 cells as well as peripheral blood-derived macrophages. The transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. These include osteospontin, matrix metalloproteinase 9, and HC-gp39. Our results show that the characteristics of U937 macrophages resemble those of inflammatory (exudate) macrophages, exemplified by the down-regulation of 5' nucleotidase and the up-regulation of leucine aminopeptidase mRNAs. In conclusion, using the powerful combination of transcriptomics, 2-DE and PCA, our results show that U937 cells differentiated by PMA treatment are an excellent model system for monocyte derived macrophage generation from blood.

We investigated the toxicity of aggregated nanoparticles of cerium oxide (CeO2) using an in vitro 3D human bronchial epithelial model that included a mucociliary apparatus (MucilAir™). CeO2 was dispersed in saline and applied to the apical surface of the model. CeO2 did not induce distinct effects in the model, whereas it did in BEAS-2B and A549 cell cultures. The absence of effects of CeO2 was not because of the model’s insensitivity. Nanoparticles of zinc oxide (ZnO) elicited positive responses in the toxicological assays. Respiratory mucus (0.1% and 1%) added to dispersions increased aggregation/agglomeration to such an extent that most CeO2 sedimented within a few minutes. Also, the mucociliary apparatus of the model removed CeO2 from the central part of the apical surface to the borders. This ‘clearance’ may have prevented the majority of CeO2 from reaching the epithelial cells. Chemical analysis of cerium in the basal tissue culture medium showed only minimal translocation of cerium across the 3D barrier. In conclusion, mucociliary defence appeared to prevent CeO2 reaching the respiratory epithelial cells in this 3D in vitro model. This model and approach can be used to study compounds of specific toxicological concern in airway defence mechanisms in vitro.

A general method for estimating reaction rate constants of chemical reactions using ultraviolet-visible (UV-vis) spectroscopy is presented. The only requirement is that some of the chemical components involved be spectroscopically active. The method uses the combination of spectroscopic measurements and techniques from numerical mathematics and chemometrics. Therefore, the method can be used in cases where a large spectral overlap of the individual reacting absorbing species is present. No knowledge about molar absorbances of individual reacting absorbing species is required for quantification. The reaction rate constants and the individual spectra of the reacting absorbing species of the two-step consecutive reaction of 3-chlorophenylhydrazonopropane dinitrile with 2-mercaptoethanol were estimated simultaneously from UV-vis recorded spectra in time. The results obtained were excellent.

In this paper, two different spectral datasets are used in order to estimate reaction rate constants using different algorithms. Dataset 1 consists of short-wavelength near-infrared (SW NIR) spectra taken in time of the two-step epoxidation of 2,5-di-tert-butyl-1,4-benzoquinone using tert-butyl hydroperoxide and Triton B catalyst. This dataset showed moderate reproducibility. Dataset 2 consists of W-VIS recorded spectra of the consecutive reaction of 3-chlorophenylhydrazonopropane dinitrile with 2-mercaptoethanol. This dataset showed good reproducibility, Two-way and three-way methods were used in order to estimate the reaction rate constants for both datasets. For the SW-NIR dataset the lowest standard deviations for the reaction rate constants were obtained with a two-way method. The lowest standard deviations for the reaction rate constant estimates for the UV-VIS dataset were obtained with a two-way method which uses spectral information that is known in advance. In this case the purl spectrum of two reacting absorbing species is known in advance and this information was used by the two-way method. For one two-way method and a few three-way methods which do not use spectral information that is known in advance, pure spectra of the reacting absorbing species of the UV-VIS dataset were estimated which showed excellent agreement with the recorded pure spectra. The pure spectra of the reacting absorbing species for the SW-NIR dataset were not estimated, because it was not possible to record the real pure spectra of these species. For both spectral datasets, quality assessment has been performed using a jackknife method. Copyright (C) 2000 John Wiley & Sons, Ltd

A traditional curve fitting (TCF) algorithm is compared with a classical curve resolution (CCR) approach for estimating reaction rate constants from spectral data obtained in time of a chemical reaction. In the TCF algorithm, reaction rate constants an estimated from the absorbance versus time data obtained from selective wavelengths. In this case, wavelengths are selected at which mainly one species is absorbing in time. In CCR, pure spectra of reacting absorbing species and the reaction rate constants are estimated simultaneously. Both TCF and CCR have been applied to experimental data. The reaction rate constants and the individual pure spectra of the reacting absorbing species were estimated simultaneously from the UV-VIS spectra taken in time of the two-step biochemical consecutive reaction of 3-chlorophenylhydrazonopropane dinitrile with 2-mercaptoethanol. This reaction was performed under second order and pseudo-first order conditions. For both conditions, the signal-to-noise ratio was approximately the same. However, if second order conditions are chosen, the first reaction step is very slow, which results in small absorbance differences in time. For the pseudo-first order dataset, the best precision of the reaction rate constant estimates has been obtained with TCF For the second order dataset, CCR performed the best with respect to the precision of the reaction rate constant estimates. (C) 2000 Elsevier Science B.V. All rights reserved

Two methods for estimating reaction rate constants from spectral data are compared. The first method is a weighted curve resolution method. The second method is an improved curve resolution method. If some of the pure spectra of species in a reacting system are known, this can be incorporated into die second algorithm. Hence, spectral information that is known a priori is used. Both methods are applied to UV-VIS recorded spectra of the two-step consecutive reaction of 3-chlorophenylhydrazonopropane dinitrile, which is an uncoupler of oxidative phosphorylation in cells, with 2-mercaptoethanol in order to estimate the reaction rate constants involved. In order to perform quality assessment, upper and lower error bounds of estimates of reaction rate constants were estimated using a jackknife procedure. Application of the second method led to lower standard deviations of the estimated reaction rate constants, compared to the first method, due to extra spectral information that is used in this second method. (C)1999 Elsevier Science B.V. All rights reserved

In this paper, two algorithms are presented to estimate reaction rate constants from on-line short-wavelength near-infrared (SW-NIR) measurements. These can be applied in cases where the contribution of the different species in the mixture spectra is of exponentially decaying character. From a single two-dimensional dataset two two-way datasets are formed by splitting the original dataset such that there is a constant time lag between the two two-way datasets. Next, a trilinear structure is formed by stacking these two two-way datasets into a three-way array. In the first algorithm, based on the generalized rank annihilation method (GRAM), the trilinear structure is decomposed by solving a generalized eigenvalue problem (GEP). Because GRAM is sensitive to noise it leads to rough estimations of reaction rate constants. The second algorithm (LM-PAR) is an iterative algorithm, which consists of a combination of the Levenberg-Marquardt algorithm and alternating least squares steps of the parallel factor analysis (PARAFAC) model using the CRAM results as initial values. Simulations and an application to a real dataset showed that both algorithms can be applied to estimate reaction rate constants in case of extreme spectral overlap of different species involved in the reacting system. (C) 1998 Elsevier Science B.V. All rights reserved

Background: Recent evidence suggests that the gut microbiota plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host–microbiome interactions aids the design of nutritional strategies that act via modulation of the microbiota. Nevertheless, relating gut microbiota composition to host health states remains challenging because of the sheer complexity of these ecosystems and the large degrees of interindividual variation in human microbiota composition. Methods: We assessed fecal microbiota composition and host response patterns of metabolic and inflammatory markers in 10 apparently healthy men subjected to a high-fat high-caloric diet (HFHC, 1300 kcal/day extra) for 4 weeks. DNA was isolated from stool and barcoded 16S rRNA gene amplicons were sequenced. Metabolic health parameters, including anthropomorphic and blood parameters, where determined at t=0 and t=4 weeks. Results: A correlation network approach revealed diet-induced changes in Bacteroides levels related to changes in carbohydrate oxidation rates, whereas the change in Firmicutes correlates with changes in fat oxidation. These results were confirmed by multivariate models. We identified correlations between microbial diversity indices and several inflammation-related host parameters that suggest a relation between diet-induced changes in gut microbiota diversity and inflammatory processes. Conclusions: This approach allowed us to identify significant correlations between abundances of microbial taxa and diet-induced shifts in several metabolic health parameters. Constructed correlation networks provide an overview of these relations, revealing groups of correlations that are of particular interest for explaining host health aspects through changes in the gut microbiota.

In the present controlled, randomised, multiple cross-over dietary intervention study, we aimed to identify potential biomarkers for dietary protein from dairy products, meat and grain, which could be useful to estimate intake of these protein types in epidemiological studies. After 9 d run-in, thirty men and seventeen women (22 (sd4) years) received three high-protein diets (aimed at approximately 18 % of energy (en%)) in random order for 1 week each, with approximately 14 en% originating from either meat, dairy products or grain. We used a two-step approach to identify biomarkers in urine and plasma. With principal component discriminant analysis, we identified amino acids (AA) from the plasma or urinary AA profile that were distinctive between diets. Subsequently, after pooling total study data, we applied mixed models to estimate the predictive value of those AA for intake of protein types. A very good prediction could be made for the intake of meat protein by a regression model that included urinary carnosine, 1-methylhistidine and 3-methylhistidine (98 % of variation in intake explained). Furthermore, for dietary grain protein, a model that included seven AA (plasma lysine, valine, threonine, α-aminobutyric acid, proline, ornithine and arginine) made a good prediction (75 % of variation explained). We could not identify biomarkers for dairy protein intake. In conclusion, specific combinations of urinary and plasma AA may be potentially useful biomarkers for meat and grain protein intake, respectively. These findings need to be cross-validated in other dietary intervention studies.

Analysis of metabolites in biofluids by gas chromatography-mass spectrometry (GC-MS) after oximation and silylation is a key method in metabolomics. The GC-MS method was modified by a modified vial design and sample work-up procedure in order to make the method applicable to small volumes of cerebrospinal fluid (CSF), i. e. 10 μL, with similar coverage compared to the standard procedure using ≥100 μL of CSF. The data quality of the modified GC-MS method was assessed by analyzing a study sample set in an animal model for multiple sclerosis, including repetitively analysed quality control rat CSF samples. Automated normalization and intra- and inter-batch correction significantly improved the data quality with the majority of metabolites showing a relative standard deviation

Eicosanoids and endocannabinoids/N-acylethanolamines (NAEs) are fatty acid derived compounds with a regulatory role in inflammation. Considering their complex metabolism, it is likely that inflammation affects multiple compounds at the same time, but how lipid profiles change in plasma and other tissues after an inflammatory stimulus has not been described in detail. In addition, dietary fish oil increases levels of several n-3 fatty acid derived eicosanoids and endocannabinoids, and this may lead to a broader change in the profiles of bioactive lipids. In the present study mice were fed a diet containing 3% w/w fish oil for 6 weeks before receiving i.p. saline or 3 mg/kg lipopolysaccharide (LPS) to induce an inflammatory response. Eicosanoid and endocannabinoid/NAE levels (in total 61 metabolites) in plasma, liver, ileum, and adipose tissue were quantified using targeted lipidomics after 2, 4, 8, and 24 h, respectively. Tissue- and time-dependent effects of LPS on bioactive lipid profiles were observed. For example, levels of CYP derived eicosanoids in the ileum were markedly affected by LPS, whereas this was less pronounced in the plasma and adipose tissue. For some compounds, such as 9,10-DiHOME, opposing effects of LPS were seen in the plasma compared to the other tissues, suggesting differential regulation of bioactive l ipid levels after an inflammatory stimulus. Taken together, our results show that plasma levels do not always correlate with the effects found in the tissues, which underlines the need to measure profiles and pathways of mediators involved in inflammation, including endocannabinoid-like structures, in both plasma and tissues.

Introduction: The aim of this study was to develop and demonstrate a framework assuring efficient process development using fewer experiments than standard experimental designs. Methods: A novel optimality criterion for experimental designs (Iw criterion) is defined that leads to more efficient process development because: (a) prior knowledge is used in the experimental design to focus on optimal processing conditions and (b) a lean design is used which can dramatically reduce the number of experiments compared to standard designs. In this way, the criterion serves as a framework to connect a series of screening and optimization designs. Results: The philosophy behind the Iw criterion is explained including a detailed step-wise discussion how to apply it in practice. Moreover, its advantages were shown in an industrial process development case using a screening and an optimization design that were not explicitly connected. In this paper, a reduction of 21% of experiments could be obtained compared to the traditional approach using standard experimental designs and no framework. Conclusions: The Iw criterion is a valuable tool to increase accuracy and to speed up research that contain sets of experiments and where prior knowledge is already available or will be derived using screening designs. © 2011 Springer Science+Business Media, LLC.

For the optimization of microbial production processes, the choice of the quantitative phenotype to be optimized is crucial. For instance, for the optimization of product formation, either product concentration or productivity can be pursued, potentially resulting in different targets for strain improvement. The choice of a quantitative phenotype is highly relevant for classical improvement approaches, and even more so for modern systems biology approaches. In this study, the information content of a metabolomics dataset was determined with respect to different quantitative phenotypes related to the formation of specific products. To this end, the production of two industrially relevant products by Aspergillus niger was evaluated: (i) the enzyme glucoamylase, and (ii) the more complex product group of secreted proteases, consisting of multiple enzymes. For both products, six quantitative phenotypes associated with activity and productivity were defined, also taking into account different time points of sampling during the fermentation. Both linear and nonlinear relationships between the metabolome data and the different quantitative phenotypes were considered. The multivariate data analysis tool partial least-squares (PLS) was used to evaluate the information content of the datasets for all the different quantitative phenotypes defined. Depending on the product studied, different quantitative phenotypes were found to have the highest information content in specific metabolomics datasets. A detailed analysis of the metabolites that showed strong correlation with these quantitative phenotypes revealed that various sugar derivatives correlated with glucoamylase activity. For the reduction of protease activity, mainly as-yet-unidentified compounds correlated.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies. © 2006 American Chemical Society.

Contains fulltext : 58859.pdf (Publisher’s version ) (Closed access) The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.

Model errors, experimental errors and instrumental noise influence the accuracy of reaction rate constant estimates obtained from spectral data recorded in time during a chemical reaction. In order to improve the accuracy, which can be divided into the precision and bias of reaction rate constant estimates, constraints can be used within the estimation procedure. The impact of different constraints on the accuracy of reaction rate constant estimates has been investigated using classical curve resolution (CCR). Different types of constraints can be used in CCR. For example, if pure spectra of reacting absorbing species are known in advance, this knowledge can be used explicitly. Also, the fact that pure spectra of reacting absorbing species are non-negative is a constraint that can be used in CCR. Experimental data have been obtained from UV-vis spectra taken in time of a biochemical reaction. From the experimental data, reaction rate constants and pure spectra were estimated with and without implementation of constraints in CCR. Because only the precision of reaction rate constant estimates could be investigated using the experimental data, simulations were set up that were similar to the experimental data in order to additionally investigate the bias of reaction rate constant estimates. From the results of the simulated data it is concluded that the use of constraints does not result self-evidently in an improvement in the accuracy of rate constant estimates. Guidelines for using constraints are given. Copyright (C) 2002 John Wiley Sons, Ltd

Curve resolution is a class of techniques concerned with estimating profiles underlying a set of measurements of time-evolving chemical systems. In general, the estimated profiles are not unique. Both intensity and rotational ambiguities exist in the solutions of these problems. Constraints can be imposed on the solution to decrease the ambiguity. Some chemical systems show closure. It is proven that imposing a closure constraint is sufficient to solve the intensity ambiguity but not the rotational ambiguity. Some curve resolution techniques are concerned with estimating reaction rate or equilibrium constants from measurements of evolving systems. Then a kinetic model is imposed on the measured data. It is proven that imposing a certain class of kinetic models is sufficient to solve both the rotational and the intensity ambiguity. Copyright (C) 2001 John Wiley gr Sons, Ltd.

The holistic study of the keystone biomolecules by applied genomics is one of the intellectual products of the current molecular biology revolution. These applied genomics technologies (transcriptomics, proteomics, and metabolomics) aim at analyzing the components of a cell in its entirety and have, together with the developments in computing, ushered in a new era in scientific methodology for the life sciences. Genomics technologies have mainly been set up and further developed by the pharmaceutical industry, but will also be invaluable to the food industry. Only recently, the first papers about nutrigenomics and the application of genomics in nutrition science and food technology have appeared. In this review, we briefly explain the essence of the various applied genomics technologies, and focus on the impact that these technologies will have in the food industry.

In both analytical and process chemistry, one common aim is to build models describing measured data. In cases where additional information about the chemical system is available, this can be incorporated into the model with the aim of improving model fit and interpretability. A model which consists of a 'hard' or 'white' part describing known sources of variation and a 'soft' or' black' part describing unknown sources of variation is called a 'grey' model. In this paper the use of a grey model is demonstrated using data from a first-order chemical batch reaction monitored by UV-vis spectroscopy. The resultant three-way data matrix is modelled using a Tucker3 structure, and external information about the spectroscopically active compounds is incorporated in the form of constraints on the model parameters with additional restrictions on the Tucker3 core matrix. The grey model is then used to analyse new batches. Different approaches to building grey models are described and some of their properties discussed. Copyright (C) 2000 John Wiley & Sons, Ltd

This paper describes the estimation of reaction rate constants and pure species UV-vis spectra of the consecutive reaction of 3-chlorophenylhydrazonopropane dinitrile with 2-mercaptoethanol. The reaction rate constants were estimated from the UV-vis measurements of the reacting system using the generalized rank annihilation method (GRAM) and the Levenberg-Marquardt/PARAFAC (LM-PAR) algorithm. Both algorithms can be applied in cases where the contribution of different species in the mixture spectra is of exponentially decaying character. From a single two-way array, two two-way datasets are formed by means of splitting such that there is a constant time lag between the two two-way datasets. By stacking these two two-way datasets, the reaction rate constants can be estimated very easily from the third dimension. GRAM, which is fast and non-iterative, decomposes the trilinear structure using a generalized eigenvalue problem (GEP). The iterative algorithm LM-PAR consists of a combination of the Levenberg-Marquardt algorithm and alternating least squares steps of the PARAFAC model using GRAM results as a set of initial starting values. Pure spectra of the absorbing species were estimated and compared with their measured pure spectra. LM-PAR performed the best, giving the lowest relative fit error. However, the relative fit error obtained with GRAM was acceptable. Since a lot of measurements are based on exponentially decaying functions, GRAM and LM-PAR can have many applications in chemistry. Copyright (C) 1999 John Wiley & Sons, Ltd

A method is presented for estimating reaction rate constants very rapidly from on-line short-wavelength near-infrared measurements. It can deal with a strong spectral overlap between the individual spectra of the species in the reacting system. For this an iterative curve-resolution method is combined with a nonlinear fitting procedure. This procedure also makes it possible to assess the quality of the estimated reaction rate constants. Simulations show that the method can be applied in cases with extreme spectral overlap of the individual spectra of the species. The procedure is applied in practice to an example with a strong spectral overlap. Reaction rate constants of the two-step epoxidation of 2,5-di-tert-butyl-1,4-benzoquinone were estimated. This method can lead to the application to on-line monitoring reaction rate constants.

Background Being able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret. Methods A statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process. Results The health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment. Conclusions The 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.