Metabolic Choreography of Energy Substrates During DCD Heart Perfusion.

Background: The number of patients waiting for heart transplant far exceeds the number of hearts available. Donation after circulatory death (DCD) combined with machine perfusion can increase the number of transplantable hearts by as much as 48%. Emerging studies also suggest machine perfusion could enable allograft "reconditioning" to optimize outcomes. However, a detailed understanding of the energetic substrates and metabolic changes during perfusion is lacking. Methods: Metabolites were analyzed using 1-dimensional 1H and 2-dimensional 13C-1H heteronuclear spectrum quantum correlation nuclear magnetic resonance spectroscopy on serial perfusate samples (N = 98) from 32 DCD hearts that were successfully transplanted. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test for significant differences in metabolite resonances during perfusion and network analysis was used to uncover altered metabolic pathways. Results: Metabolite differences were observed comparing baseline perfusate to samples from hearts at time points 1-2, 3-4, and 5-6 h of perfusion and all pairwise combinations. Among the most significant changes observed were a steady decrease in fatty acids and succinate and an increase in amino acids, especially alanine, glutamine, and glycine. This core set of metabolites was also altered in a DCD porcine model perfused with a nonblood-based perfusate. Conclusions: Temporal metabolic changes were identified during ex vivo perfusion of DCD hearts. Fatty acids, which are normally the predominant myocardial energy source, are rapidly depleted, while amino acids such as alanine, glutamine, and glycine increase. We also noted depletion of ketone, ß-hydroxybutyric acid, which is known to have cardioprotective properties. Collectively, these results suggest a shift in energy substrates and provide a basis to design optimal preservation techniques during perfusion.

NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples.

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach.

Background and aim: Results from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND). Methods: Ninety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success. Results: There were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period. Conclusion: We identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention.

Scope: The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight. Methods: Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed. Results: The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate. Conclusion: The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.

Urinary and plasma metabolome of farm mink (Neovison vison) after an intervention with raw or cooked poultry offal: a 1H NMR investigation.

The introduction of high amounts of cooked poultry offal in mink feed has been associated with health problems in growing mink. Cooking mink feed is a convenient way of reducing microbiological activity, but it may have a negative effect on raw material quality and animal welfare. This study investigates growth and health of mink fed raw or cooked poultry offal and describes urinary and blood plasma metabolic changes related to the feeding. A total of 65 male mink were divided in three feeding groups, two fed cooked offal and one group fed raw offal, and the plasma and urine samples were collected at 3 time points during the growth. Both bio-fluids and feed samples were measured by 1H NMR spectroscopy and resulted metabolomics data were analysed using univariate and multivariate statistical methods that revealed dominating effect of the mink growth stages and to a less extent the feeding regime. Metabolome differences in relation to low body mass index (BMI) and kidney lesions were observed in plasma. Disease and decrease in BMI was associated with high creatinine and dimethylglycine content in plasma. These molecules were also particularly indicative of the cooked feeds. Moreover, low urinary taurine levels were also associated with disease and low BMI. Individual mink appeared to show negative effects of the cooked feed diet, including impaired growth and gross pathological lesions involving the kidneys. This may be related to the absorption of essential metabolites such as amino acids and fats, necessary for mink growth, that are negatively impacted by the cooking process.

Human Fecal Metabolome Reflects Differences in Body Mass Index, Physical Fitness, and Blood Lipoproteins in Healthy Older Adults.

This study investigated how body mass index (BMI), physical fitness, and blood plasma lipoprotein levels are related to the fecal metabolome in older adults. The fecal metabolome data were acquired using proton nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry on 163 healthy older adults (65-80 years old, 80 females and 83 males). Overweight and obese subjects (BMI ≥ 27) showed higher levels of fecal amino acids (AAs) (valine, alanine, and phenylalanine) compared to normal-weight subjects (BMI ≤ 23.5). Adults classified in the high-fitness group displayed slightly lower concentrations of fecal short-chain fatty acids, propionic acid, and AAs (methionine, leucine, glutamic acid, and threonine) compared to the low-fitness group. Subjects with lower levels of cholesterol in low-density lipoprotein particles (LDLchol, ≤2.6 mmol/L) displayed higher fecal levels of valine, glutamic acid, phenylalanine, and lactic acid, while subjects with a higher level of cholesterol in high-density lipoprotein particles (HDLchol, ≥2.1 mmol/L) showed lower fecal concentration of isovaleric acid. The results from this study suggest that the human fecal metabolome, which primarily represents undigested food waste and metabolites produced by the gut microbiome, carries important information about human health and should be closely integrated to other omics data for a better understanding of the role of the gut microbiome and diet on human health and metabolism.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy.

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using 1H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.

An NMR Metabolomics Approach to Investigate Factors Affecting the Yoghurt Fermentation Process and Quality.

A great number of factors can influence milk fermentation for yoghurt production such as fermentation conditions, starter cultures and milk characteristics. It is important for dairy companies to know the best combinations of these parameters for a controlled fermentation and for the desired qualities of yoghurt. This study investigates the use of a 1H-NMR metabolomics approach to monitor the changes in milk during fermentation from time 0 to 24 h, taking samples every hour in the first 8 h and then at the end-point at 24 h. Three different starter cultures (L. delbrueckii ssp. bulgaricus, S. thermophilus and their combination) were used and two different heat treatments (99 or 105 °C) were applied to milk. The results clearly show the breakdown of proteins and lactose as well as the concomitant increase in acetate, lactate and citrate during fermentation. Formate is found at different initial concentrations depending on the heat treatment of the milk and its different time trajectory depends on the starter cultures: Lactobacillus cannot produce formate, but needs it for growth, whilst Streptococcus is able to produce formate from pyruvate, therefore promoting the symbiotic relationship between the two strains. On the other hand, Lactobacillus can hydrolyze milk proteins into amino acids, enriching the quality of the final product. In this way, better insight into the protocooperation of lactic acid bacteria strains and information on the impact of a greater heat treatment in the initial matrix were obtained. The global chemical view on the fermentations provided using NMR is key information for yoghurt producers and companies producing starter cultures.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet.

OBJECTIVES: The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss. METHODS: The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight. RESULTS: Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research. CONCLUSIONS: Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Human Faecal 1H NMR Metabolomics: Evaluation of Solvent and Sample Processing on Coverage and Reproducibility of Signature Metabolites.

The human faecal metabolome is complex, but rich in information and allows investigation of the host metabolism as a function of diet and health. The faecal metabolome is still much less explored than the plasma and urine metabolome, and in order to generate comparable data across laboratories and cohorts, standard operating procedures are required. This study evaluates 10 protocols, using different extraction solvents and sample processing methods for measuring the human faecal metabolome using proton nuclear magnetic resonance (1H NMR) spectroscopy. Three solvents: water, methanol, and dimethyl sulfoxide (DMSO) were investigated at varying concentrations for their ability to extract metabolites directly from faecal slurry or after freeze-drying. The protocols were evaluated on four different pools of human feces. The study also demonstrates a novel signature mapping (SigMa) method for rapid and unbiased processing of complex NMR spectra applied for the first time to human faecal metabolomics. The method is provided with a library containing the chemical shift ranges of 81 common faecal metabolites for future unambiguous and rapid faecal metabolite annotations. The result from the 10 faecal extraction protocols were investigated in terms of reproducibility, coverage, and ability to extract low concentration metabolites. The solvent type was shown to induce the highest variation in the data (45.7%) and the water based extractions allowed detection of the greatest number of metabolites and resulted in the highest reproducibility. Direct extraction of faecal slurry was proved to be more reproducible than freeze-drying. In addition, freeze-drying caused a relative loss of short chain fatty acids (SCFA). DMSO was used for the first time to extract faecal metabolites and enabled the detection of certain bile acids. Some derivatives of SCFA were only detected using methanol as solvent.

Signature Mapping (SigMa): An efficient approach for processing complex human urine 1H NMR metabolomics data.

Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.

Monitoring molecular composition and digestibility of ripened bresaola through a combined foodomics approach.

In this work, the effects of maturation time and simulated gastrointestinal digestion on the molecular and peptide profiles of "Bresaola Valtellina" were assessed through the foodomics approach, in this case food proteomics and peptidomics combined to other analytical and biological assays, aiming at depicting a holistic food quality. Human digestion of this Italian cured meat product was simulated using an in vitro static protocol and the degree of proteolysis and the in vitro bioactivity of the soluble free compounds in the digestates were evaluated by biochemical assays, e.g. SDS-PAGE, size exclusion HPLC, HPLC/MS, 1H NMR, enzymatic and antioxidant activities. The obtained results demonstrated that in vitro gastrointestinal digestion contributed to a considerable release of myofibrillar proteins by the muscle tissue. Data from SDS-PAGE, peptidomic and size exclusion HPLC assays showed that the in vitro digestion largely degraded proteins of muscle tissue to peptides smaller than 250 Da. The released peptides were likely responsible for the inhibitory activity on amylolytic enzymes and for the antioxidant properties elicited by the gastric digestates of Bresaola. Overall, the results demonstrated the negligible role of ripening in making meat proteins more bioaccessible, whereas they confirmed the highly in vitro digestibility of meat proteins from Bresaola. This study represents a new approach merging proteomics and foodomics to evaluate the effect of ripening and in vitro digestion on the bioactivity and bioaccessibility of proteins and peptides of meat products.

Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk-of-Poverty.

SCOPE: According to Eurostat 2016, approximately 119 million European citizens live at-risk-of-poverty (ROP). This subpopulation is highly diverse by ethnicity, age, and culture in the different EU states, but they all have in common a low income that could represent an increased risk of nutrient deficiencies due to poor nutritional habits. This study aims to investigate the human urine metabolome in the search of common biomarkers representing dietary deficiencies amongst European populations at ROP. METHODS AND RESULTS: 2732 urine samples were collected from 1391 subjects across five different European countries, including the United Kingdom, Finland, Italy, Lithuania, and Serbia, and analyzed using 1 H-NMR spectroscopy. The resulting urine metabolome data were explored according to study design factors including economic status, country, and gender. CONCLUSION: Partitioning of the effects derived from the study design factors using ANOVA-simultaneous component analysis (ASCA) revealed that country and gender effects were responsible for most of the systematic variation. The effect of economic status was, as expected, much weaker than country and gender, but more pronounced in Lithuania than in other countries. Citrate and hippurate were among the most powerful ROP biomarkers. The possible relationship between these markers and nutritional deficiencies amongst the ROP population is discussed.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies.

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Combined magnetic resonance imaging and high resolution spectroscopy approaches to study the fertilization effects on metabolome, morphology and yeast community of wine grape berries, cultivar Nero di Troia.

Effects of fertilization practices, mineral (M) and organo-mineral (OM), on molecular composition of Nero di Troia cultivar grape berries was studied using conventional chemical analysis, Magnetic Resonance Imaging (MRI) and 1H NMR spectroscopy on intact berries and extracts, respectively, and through analysis of yeast species developed on grape skins. Plants vegetative status did not differ between the two fertilization practices, whereas some grape juice chemical characteristics differed in fertilized grapes. MRI provided information on grape berries morphology through weighted images depending on spin-spin (T2) and spin-lattice (T1) relaxation times. T1 values were the highest in OM grape berries. 1H NMR metabolic profile, combined with chemometric analysis, evidenced significant differences for some metabolites (valine, leucine, isoleucine, proline, and malic acid). Furthermore, higher frequency of yeasts genus Starmella sp., isolated from OM grape berries contributed to reinforcing the found results on the physiological response of wine grape Nero di Troia to fertilization.

ONS: an ontology for a standardized description of interventions and observational studies in nutrition.

BACKGROUND: The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. RESULTS: We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. CONCLUSIONS: The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts.

GC-MS Based Metabolomics and NMR Spectroscopy Investigation of Food Intake Biomarkers for Milk and Cheese in Serum of Healthy Humans.

The identification and validation of food intake biomarkers (FIBs) in human biofluids is a key objective for the evaluation of dietary intake. We report here the analysis of the GC-MS and 1H-NMR metabolomes of serum samples from a randomized cross-over study in 11 healthy volunteers having consumed isocaloric amounts of milk, cheese, and a soy drink as non-dairy alternative. Serum was collected at baseline, postprandially up to 6 h, and 24 h after consumption. A multivariate analysis of the untargeted serum metabolomes, combined with a targeted analysis of candidate FIBs previously reported in urine samples from the same study, identified galactitol, galactonate, and galactono-1,5-lactone (milk), 3-phenyllactic acid (cheese), and pinitol (soy drink) as candidate FIBs for these products. Serum metabolites not previously identified in the urine samples, e.g., 3-hydroxyisobutyrate after cheese intake, were detected. Finally, an analysis of the postprandial behavior of candidate FIBs, in particular the dairy fatty acids pentadecanoic acid and heptadecanoic acid, revealed specific kinetic patterns of relevance to their detection in future validation studies. Taken together, promising candidate FIBs for dairy intake appear to be lactose and metabolites thereof, for lactose-containing products, and microbial metabolites derived from amino acids, for fermented dairy products such as cheese.

Metabolomics characterization of colostrum in three sow breeds and its influences on piglets' survival and litter growth rates.

BACKGROUND: Colostrum is the first secretion produced by mammary glands during the hours immediately preceding and succeeding parturition. This secretion differs from milk and represents an essential vehicle of passive immunity, prebiotic compounds and growth factors involved in intestinal development. Most of the literature concerning colostrum composition refers mainly to human and cow; and little is known about pig colostrum metabolome and how it varies between pig breeds and different farrowing parity. Thus, the aim of the present research is to provide new information about pig colostrum composition and the associations between metabolites, the sows' breed and the survival and growth rates of their litters. RESULTS: Colostrum samples were gathered from 58 parturitions of sows belonging to three different breeds chosen for their importance in Italian heavy pig production: 31 Large White, 15 Landrace and 12 Duroc respectively. The defatted and ultrafiltered colostrum samples were analysed using 1H-NMR spectroscopy. Principal Components Analysis (PCA) was assessed on the obtained spectra. In addition, using a Stepwise Regression and a Linear Regression analyses the metabolites named after the signals assignment were tested for their associations with piglets' performances. Twenty-five metabolites were identified, comprehending monosaccharides, disaccharides (such as lactose), organic acids (lactate, citrate, acetate and formate), nitrogenous organic acids (such as creatine) and other compounds, including nucleotides. PCA results evidence a clustering due to breed and season effects. Lactose was the main compound determining the assignment of the samples into different clusters according to the sow breed. Furthermore, some metabolites showed to be associated with piglets' performance and survival traits: acetate and taurine were positively related to litter weight gain and piglets' survival rate, respectively, while dimethylamine and cis-aconitate were linked to new-borns' impaired ability to survive. CONCLUSIONS: The results obtained suggest that colostrum composition is affected by breed, which, together with environmental conditions, may cause changes in colostrum metabolites content with possible consequences on piglets' performances. Among the identified metabolites, acetate, taurine, dimethylamine and cis-aconitate showed consistent associations with piglets' survival rate and litter weight gain, implying that these compounds may affect new-borns' ability to survive.

Identification of weak and gender specific effects in a short 3 weeks intervention study using barley and oat mixed linkage ß-glucan dietary supplements: a human fecal metabolome study by GC-MS.

INTRODUCTION: Mixed-linkage (1→3),(1→4)-ß-d-glucans (BG) reduce cholesterol level and insulin response in humans. Despite this, their role in human metabolism and a mode of action remains largely unknown. OBJECTIVES: To investigate the effects of three structurally different BG on human fecal metabolome in a full cross-over intervention using GC-MS metabolomics. METHODS: Over three weeks of intervention, young healthy adults received food supplemented with BG from oat, two different BG from barley or a non-fiber control in a full cross-over design. Untargeted metabolomics and short chain fatty acid analysis was performed on day three fecal samples. ANOVA-simultaneous component analysis was applied to partition the data variation according to the study design, and PLS-DA was used to select most discriminative metabolite markers. RESULTS: Univariate and multivariate data analysis revealed a dominating effect of inter-individual variances followed by a gender effect. Weak effects of BG intake were identified including an increased level of gamma-amino-butyrate and palmitoleic acid in males and a decreased level of enterolactone in females. Barley and oat derived BG were found to influence the human fecal metabolome differently. Barley BG increased the relative level of formate in males and isobutyrate, isovalerate, 2-methylbutyrate in females. In total 15, 3 and 11 human fecal metabolites were significantly different between control vs. BG, control vs. oat BG, and barley BG vs. oat BG, respectively. CONCLUSIONS: The study show that human fecal metabolome largely reflects individual (∼28% variation) and gender (∼15% variation) differences, whereas the treatment effect of the BG (∼8% variation) only manifests in a few key metabolites (primarily by the metabolites: d-2-aminobutyric acid, palmitoleic acid, linoleic acid and 11-eicosenoic acid).