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BackgroundLittle is known about the effect of fish consumption on gene expression of inflammation-related genes in immune cells in coronary heart disease (CHD).Aim of the studyWe sought to evaluate the effect of a fatty fish (FF) or a lean fish (LF) diet on the modulation of inflammatory and endothelial function-related genes in peripheral blood mononuclear cells (PBMCs) of subjects with CHD, and its association with serum fatty acid (FA) profile and lipid metabolic compounds.MethodsData from 27 patients randomized into an 8-week FF (n = 10; mean ± SD: 4.3 ± 0.4 portions of fish per week), LF (n = 11; 4.7 ± 1.1 portions of fish per week), or control diet (n = 6; 0.6 ± 0.4 portions of fish per week) were analyzed. The mRNA expression was measured using real-time PCR.ResultsThe effect of the intervention on the mRNA expression of the genes studied did not differ among groups. In the FF group, however, the decrease in arachidonic acid to eicosapentaenoic acid (AA:EPA) ratio in cholesterol ester and phospholipid fractions strongly correlated with the change in IL1B mRNA levels (r s = 0.60, P = 0.06 and r s = 0.86, P = 0.002, respectively). In the LF group, the decrease in palmitic acid and total saturated FAs in cholesterol esters correlated with the change in intercellular cell adhesion molecule-1 (ICAM1) expression (r s = 0.64, P = 0.04 for both). Circulating levels of soluble ICAM-1 decreased only in the LF group (P ConclusionsThe intake of FF or LF diet did not alter the expression of inflammatory and endothelial function-related genes in PBMCs of patients with CHD. However, the decrease in AA:EPA ratio in serum lipids in the FF group may induce an anti-inflammatory response at mRNA levels in PBMCs. A LF diet might benefit endothelial function, possibly mediated by the changes in serum FA composition.

Udgivelsesdato: 2009-Mar-19 Mitochondrial membranes are unique in many ways. Unlike other cellular membranes, they are comprised of two membranes instead of just one, and cardiolipins, one of the abundant lipid species in mitochondrial membranes, are not found in significant amounts elsewhere in the cell. Among other aspects, the exceptional nature of cardiolipins is characterized by their small charged head group connected to typically four hydrocarbon chains. In this work, we present atomic-scale molecular dynamics simulations of the inner mitochondrial membrane modeled as a mixture of cardiolipins (CLs), phosphatidylcholines (PCs), and phosphatidylethanolamines (PEs). For comparison, we also consider pure one-component bilayers and mixed PC-PE, PC-CL, and PE-CL membranes. We find that the influence of CLs on membrane properties depends strongly on membrane composition. This is highlighted by studies of the stability of CL-containing membranes, which indicate that the interactions of CL in ternary lipid bilayers cannot be deduced from the corresponding ones in binary membranes. Moreover, while the membrane properties in the hydrocarbon region are only weakly affected by CLs, the changes at the membrane-water interface turn out to be prominent. The effects at the interface are most evident in membrane properties related to hydrogen bonding and the binding phenomena associated with electrostatic interactions.

Lipidomics owes its emergence to technologies developed over the past decade that empowered us with the ability to detect and quantify hundreds of intact lipid molecular species in parallel. One of the biggest current challenges of lipidomics is the elucidation of important pathobiological phenomena from the integration of large amounts of new data becoming available. In this respect, development of lipidomics as a field bears many similarities to the emergence and progress of genomics. Initial excitement over the ability to measure the expression of large numbers of genes in parallel was soon overshadowed by concerns over the ability to reliably analyze and interpret the expression data. This led to active research in the area of bioinformatics, which led to improved statistical methods as well as to new approaches for the analysis of transcriptome data in the pathway context. This review covers recent bioinformatics developments of relevance to analysis and interpretation of analytical lipidomics data, with the focus primarily on practical aspects of lipid bioinformatics.

OBJECTIVE Apolipoprotein CIII (apoCIII) is an independent risk factor for cardiovascular disease, but the molecular mechanisms involved are poorly understood. We investigated potential proatherogenic properties of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes. RESEARCH DESIGN AND METHODS LDL was isolated from control subjects, subjects with type 2 diabetes, and apoB transgenic mice. LDL-biglycan binding was analyzed with a solid-phase assay using immunoplates coated with biglycan. Lipid composition was analyzed with mass spectrometry. Hydrolysis of LDL by sphingomyelinase was analyzed after labeling plasma LDL with [3H]sphingomyelin. ApoCIII isoforms were quantified after isoelectric focusing. Human aortic endothelial cells were incubated with desialylated apoCIII or with LDL enriched with specific apoCIII isoforms. RESULTS We showed that enriching LDL with apoCIII only induced a small increase in LDL-proteoglycan binding, and this effect was dependent on a functional site A in apoB100. Our findings indicated that intrinsic characteristics of the diabetic LDL other than apoCIII are responsible for further increased proteoglycan binding of diabetic LDL with high-endogenous apoCIII, and we showed alterations in the lipid composition of diabetic LDL with high apoCIII. We also demonstrated that high apoCIII increased susceptibility of LDL to hydrolysis and aggregation by sphingomyelinases. In addition, we demonstrated that sialylation of apoCIII increased with increasing apoCIII content and that sialylation of apoCIII was essential for its proinflammatory properties. CONCLUSIONS We have demonstrated a number of features of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes that could explain the proatherogenic role of apoCIII.

OBJECTIVE—To determine whether 1) hepatic ceramide and diacylglycerol concentrations, 2) SCD1 activity, and 3) hepatic lipogenic index are increased in the human nonalcoholic fatty liver.RESEARCH DESIGN AND METHODS—We studied 16 subjects with (n = 8) and without (n = 8) histologically determined nonalcoholic fatty liver (NAFL+ and NAFL−) matched for age, sex, and BMI. Hepatic concentrations of lipids and fatty acids were quantitated using ultra-performance liquid chromatography coupled to mass spectrometry and gas chromatography.RESULTS—The absolute (nmol/mg) hepatic concentrations of diacylglycerols but not ceramides were increased in the NAFL+ group compared with the NAFL− group. The livers of the NAFL+ group contained proportionally less long-chain polyunsaturated fatty acids as compared with the NAFL− group. Liver fat percent was positively related to hepatic stearoyl-CoA desaturase 1 (SCD1) activity index (r = 0.70, P = 0.003) and the hepatic lipogenic index (r = 0.54, P = 0.030). Hepatic SCD1 activity index was positively related to the concentrations of diacylglycerols (r = 0.71, P = 0.002) but not ceramides (r = 0.07, NS).CONCLUSIONS—We conclude that diacylglycerols but not ceramides are increased in NAFL. The human fatty liver is also characterized by depletion of long polyunsaturated fatty acids in the liver and increases in hepatic SCD1 and lipogenic activities.

Lipidome is loosely defined as the entire spectrum of lipids in a biological system. Given the modern lipidomics platforms for the first time empower us with the ability to obtain a snapshot of complete cellular/organismal lipidome, many surprises and discoveries are likely awaiting us in the area of lipids as related to cellular/organismal physiology. The lipidomics approaches can be applied both as the phenotyping platform and for the hypothesis-driven research aiming to elucidate, e.g., a specific pathway or gene function. Modern lipidomics methods combine the latest mass spectrometry technology and bioinformatics methods with traditional methods such as for sample preparation and lipid extraction.

The liver secretes triglyceride-rich VLDLs, and the triglycerides in these particles are taken up by peripheral tissues, mainly heart, skeletal muscle, and adipose tissue. Blocking hepatic VLDL secretion interferes with the delivery of liver-derived triglycerides to peripheral tissues and results in an accumulation of triglycerides in the liver. However, it is unclear how interfering with hepatic triglyceride secretion affects adiposity, muscle triglyceride stores, and insulin sensitivity. To explore these issues, we examined mice that cannot secrete VLDL [due to the absence of microsomal triglyceride transfer protein (Mttp) in the liver]. These mice exhibit markedly reduced levels of apolipoprotein B-100 in the plasma, along with reduced levels of triglycerides in the plasma. Despite the low plasma triglyceride levels, triglyceride levels in skeletal muscle were unaffected. Adiposity and adipose tissue triglyceride synthesis rates were also normal, and body weight curves were unaffected. Even though the blockade of VLDL secretion caused hepatic steatosis accompanied by increased ceramides and diacylglycerols in the liver, the mice exhibited normal glucose tolerance and were sensitive to insulin at the whole-body level, as judged by hyperinsulinemic euglycemic clamp studies. Normal hepatic glucose production and insulin signaling were also maintained in the fatty liver induced by Mttp deletion. Thus, blocking VLDL secretion causes hepatic steatosis without insulin resistance, and there is little effect on muscle triglyceride stores or adiposity.

Microbial metabolism of the stereoisomers (+)-catechin and (−)-epicatechin was compared by two analytical techniques, GC/MS for quantitative targeted analysis and GC×GC-TOF for global characterization of the metabolome, using human faecal microbiota as an inoculum of converting microbiota. The ring-fission site changed when the inocula originated from two different groups of donors, but dehydroxylation progressed similarly regardless of the inoculum. Whereas GC/MS proved to be an appropriate tool for the study of specific expected metabolites of catechin stereoisomers, GC×GC-TOF-based metabolomics analysis also revealed new metabolites not included in the targeted analyses. Quantitation and verification of identification can also be performed in a metabolomics platform, if authentic standards are available.

The emergence of systems biology necessitates development of platforms to organise and interpret plentitude of biological data. We present a system to integrate data across multiple bioinformatics databases and enable mining across various conceptual levels of biological information. The results are represented as complex networks. Context dependent mining of these networks is achieved by use of distances. Our approach is demonstrated with three applications: full metabolic network retrieval with network topology study, exploration of properties and relationships of a set of selected proteins, and combined visualisation and exploration of gene expression data with related pathways and ontologies.

The aim of this study was to develop a method for comprehensive profiling of metabolites involved in mammalian steroid metabolism. The study was performed using the partial filling micellar electrokinetic chromatography (PF-MEKC) technique for determination of endogenous low-hydrophilic steroids. The detection techniques in capillary electrophoresis were UV absorption and electrospray mass spectrometry (ESI-MS). Thirteen steroids were included in the method development, and the selected were metabolites involved in major pathways of steroid biosynthesis. Although only eight of them could be separated and detected with UV, they could be identified by ESI-MS using selected ion monitoring (SIM) technique. Tandem MS spectra were also collected. UV detection was more sensitive than MS due to better separation of compounds and the selective signal sensitivity. The lowest limits of detection were 10–100 ng/mL for cortisone, corticosterone, hydrocortisone and testosterone. The other steroids could be detected at 500–1000 ng/mL. The identification of cortisone, corticosterone, hydrocortisone, estrogen and testosterone were made in patient urine samples and their concentrations were 1–40 μg/L.

OBJECTIVE— We sought to determine whether adipose tissue is inflamed in individuals with increased liver fat (LFAT) independently of obesity. RESEARCH DESIGN AND METHODS— A total of 20 nondiabetic, healthy, obese women were divided into normal and high LFAT groups based on their median LFAT level (2.3 ± 0.3 vs. 14.4 ± 2.9%). Surgical subcutaneous adipose tissue biopsies were studied using quantitative PCR, immunohistochemistry, and a lipidomics approach to search for putative mediators of insulin resistance and inflammation. The groups were matched for age and BMI. The high LFAT group had increased insulin (P = 0.0025) and lower HDL cholesterol (P = 0.02) concentrations. RESULTS— Expression levels of the macrophage marker CD68, the chemokines monocyte chemoattractant protein-1 and macrophage inflammatory protein-1α, and plasminogen activator inhibitor-1 were significantly increased, and those of peroxisome proliferator–activated receptor-γ and adiponectin decreased in the high LFAT group. CD68 expression correlated with the number of macrophages and crown-like structures (multiple macrophages fused around dead adipocytes). Concentrations of 154 lipid species in adipose tissue revealed several differences between the groups, with the most striking being increased concentrations of triacylglycerols, particularly long chain, and ceramides, specifically Cer(d18:1/24:1) (P = 0.01), in the high LFAT group. Expression of sphingomyelinases SMPD1 and SMPD3 were also significantly increased in the high compared with normal LFAT group. CONCLUSIONS— Adipose tissue is infiltrated with macrophages, and its content of long-chain triacylglycerols and ceramides is increased in subjects with increased LFAT compared with equally obese subjects with normal LFAT content. Ceramides or their metabolites could contribute to adverse effects of long-chain fatty acids on insulin resistance and inflammation.

Motivation: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today. Results: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level. Contact: marko.sysi-aho@vtt.fi

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1-25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies. © Springer Science+Business Media, LLC 2007.

The gut microbiota influences many aspects of host metabolism. We have previously shown that the presence of a gut microbiota remodels lipid composition. Here we investigated how interaction between gut microbiota and dietary lipids regulates lipid composition in the liver and plasma, and gene expression in the liver. Germ-free and conventionally raised mice were fed a lard or fi sh oil diet for 11 weeks. We performed lipidomics analysis of the liver and serum and microarray analysis of the liver. As expected, most of the variation in the lipidomics dataset was induced by the diet, and abundance of most lipid classes differed between mice fed lard and fi sh oil. However, the gut microbiota also affected lipid composition. The gut microbiota increased hepatic levels of cholesterol and cholesteryl esters in mice fed lard, but not in mice fed fi sh oil. Serum levels of cholesterol and cholesteryl esters were not affected by the gut microbiota. Genes encoding enzymes involved in cholesterol biosynthesis were downregulated by the gut microbiota in mice fed lard and were expressed at a low level in mice fed fi sh oil independent of microbial status. In summary, we show that gut microbiota-induced regulation of hepatic cholesterol metabolism is dependent on dietary lipid composition.

The aim of this study was to investigate the longitudinal plasma metabolic profiles in healthy infants and the potential association with breastfeeding duration and islet autoantibodies predictive of type 1 diabetes.Up to four longitudinal plasma samples from age 3 months from case children who developed islet autoimmunity (n = 29) and autoantibody-negative control children (n = 29) with the HLA DR4-DQ8/DR3-DQ2 genotype were analyzed using two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer for detection of small polar metabolites.Plasma metabolite levels were found to depend strongly on age, with fold changes varying up to 50% from age 3 to 24 months (p 0.001 after correction for multiple testing). Tyrosine levels tended to be lower in case children, but this was not significant after correction for multiple testing. Ornithine levels were lower in case children compared with the controls at the time of seroconversion, but the difference was not statistically significant after correcting for multiple testing. Breastfeeding for at least 3 months as compared with shorter duration was associated with higher plasma levels of isoleucine, and lower levels of methionine and 3,4-dihydroxybutyric acid at 3 months of age.Plasma levels of several small, polar metabolites changed with age during early childhood, independent of later islet autoimmunity status and sex. Breastfeeding was associated with higher levels of branched-chain amino acids, and lower levels of methionine and 3,4-dihydroxybutyric acid.

Lipidomic analysis, which enables comprehensive characterization of molecular lipids in biological systems, is rapidly becoming an essential tool in biomedical research. While lipidomics already have contributed to several conceptual advances in metabolic research and led to new, validated disease biomarkers, its translation into the clinic remains a challenge.

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

Adipose tissue dysfunction is an important determinant of obesity-associated, lipid-induced metabolic complications. Ceramides are well-known mediators of lipid-induced insulin resistance in peripheral organs such as muscle. DEGS1 is the desaturase catalyzing the last step in the main ceramide biosynthetic pathway. Functional suppression of DEGS1 activity results in substantial changes in ceramide species likely to affect fundamental biological functions such as oxidative stress, cell survival, and proliferation. Here, we show that degs1 expression is specifically decreased in the adipose tissue of obese patients and murine models of genetic and nutritional obesity. Moreover, loss-of-function experiments using pharmacological or genetic ablation of DEGS1 in preadipocytes prevented adipogenesis and decreased lipid accumulation. This was associated with elevated oxidative stress, cellular death, and blockage of the cell cycle. These effects were coupled with increased dihydroceramide content. Finally, we validated in vivo that pharmacological inhibition of DEGS1 impairs adipocyte differentiation. These data identify DEGS1 as a new potential target to restore adipose tissue function and prevent obesity-associated metabolic disturbances., Funding agencies:Medical Research Council (MRC)Metabolic Diseases Unit of the MRCBritish Heart Foundation

Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites., Cited By :18; Export Date: 12 March 2018; Article

Roux-en-Y gastric bypass (RYGB) is an effective method to attain sustained weight loss and diabetes remission. We aimed to elucidate early changes in the plasma metabolome and lipidome after RYGB. Plasma samples from 16 insulin-resistant morbidly obese subjects, of whom 14 had diabetes, were subjected to global metabolomics and lipidomics analysis at pre-surgery and 4 and 42 days after RYGB. Metabolites and lipid species were compared between time points and between subjects who were in remission and not in remission from diabetes 2 years after surgery. We found that the variables that were most discriminatory between time points were decanoic acid and octanoic acid, which were elevated 42 days after surgery, and sphingomyelins (18:1/21:0 and 18:1/23:3), which were at their lowest level 42 days after surgery. Insulin levels were lower at 4 and 42 days after surgery compared with pre-surgery levels. At 4 days after surgery, insulin levels correlated positively with metabolites of branched chain and aromatic amino acid metabolism and negatively with triglycerides with long-chain fatty acids. Of the 14 subjects with diabetes prior to surgery, 7 were in remission 2 years after surgery. The subjects in remission displayed higher pre-surgery levels of tricarboxylic acid cycle intermediates and triglycerides with long-chain fatty acids compared with subjects not in remission. Thus, metabolic alterations are induced soon after surgery and subjects with diabetes remission differ in the metabolic profiles at pre- and early post-surgery time points compared to patients not in remission., Funding agencies:Human Frontier of Science Program RGY64/2008

Together with the widely used Affymetrix microarrays, the recently introduced Illumina platform has become a cost-effective alternative for genome-wide studies. To efficiently use data from both array platforms, there is a pressing need for methods that allow systematic integration of multiple datasets, especially when the number of samples is small. To address these needs, we introduce a meta-analytic procedure for combining Affymetrix and Illumina data in the context of detecting differentially expressed genes between the platforms. We first investigate the effect of different expression change estimation procedures within the platforms on the agreement of the most differentially expressed genes. Using the best estimation methods, we then show the benefits of the integrative analysis in producing reproducible results across bootstrap samples. In particular, we demonstrate its biological relevance in identifying small but consistent changes during T helper 2 cell differentiation.

The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.

The nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARgamma) is critically required for adipogenesis. PPARgamma exists as two isoforms, gamma1 and gamma2. PPARgamma2 is the more potent adipogenic isoform in vitro and is normally restricted to adipose tissues, where it is regulated more by nutritional state than PPARgamma1. To elucidate the relevance of the PPARgamma2 in vivo, we generated a mouse model in which the PPARgamma2 isoform was specifically disrupted. Despite similar weight, body composition, food intake, energy expenditure, and adipose tissue morphology, male mice lacking the gamma2 isoform were more insulin resistant than wild-type animals when fed a regular diet. These results indicate that insulin resistance associated with ablation of PPARgamma2 is not the result of lipodystrophy and suggests a specific role for PPARgamma2 in maintaining insulin sensitivity independently of its effects on adipogenesis. Furthermore, PPARgamma2 knockout mice fed a high-fat diet did not become more insulin resistant than those on a normal diet, despite a marked increase in their mean adipocyte cell size. These findings suggest that PPARgamma2 is required for the maintenance of normal insulin sensitivity in mice but also raises the intriguing notion that PPARgamma2 may be necessary for the adverse effects of a high-fat diet on carbohydrate metabolism.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels.This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes.These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying — well in advance of pathogenic manifestations of disease — the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Metabolomics is a systems approach to the biology of health and disease and an ‘-omics’ discipline that measures small metabolites representing end products of a variety of metabolic and cellular processes as reflected in available biological specimens (e.g. blood, urine, saliva, feces and tissue). As an increasingly more common approach used for epidemiologic and clinical studies, metabolomics has the potential to improve disease risk assessment, screening, diagnosis, prognosis and predictive response to therapy, as well as provide disease mechanistic insight and help to establish criteria for causation. It is timely to establish mechanisms for leveraging existing resources and data for novel biomarker discovery using metabolomics approaches. To this end, the National Institutes of Health COnsortium of METabolomics Studies (COMETS) was established in 2014 (http://epi.grants.cancer.gov/comets/), and currently includes 34 prospective cohorts and 2 consortia from the United States, Europe, Asia and South America. The COMETS mission is to promote collaborations among prospective cohort studies that follow participants for a range of outcomes and perform metabolomic profiling of individuals. COMETS aims to facilitate an open exchange of ideas, knowledge, and results to accelerate a shared goal of identifying metabolomic profiles associated with chronic disease phenotypes (e.g. heart disease, diabetes, cancer). The structure of COMETS includes a steering committee with one representative from each participating cohort/consortium and a number of working groups, including age analysis, data harmonization, statistics, and trainee. In November 2016, COMETS held their inaugural scientific meeting. As a result of this meeting, more working groups are currently being established to support additional interests/projects among the consortium members. Here we further describe the COMETS structure, including preliminary descriptive data, which aims to advance the use and impact of metabolite profiling in population-based research. Citation Format: Rachael Z. Stolzenberg-Solomon, Steven Moore, Crnelia Ulrich, Elizabeth Poole, Marinella Temprosa, Mukesh Verma, Demetrius Albanes, Clara Barrios Barrera, Eric Boerwinkle, Juan P. Casas, Clary Clish, Robert Gerszten, Christian Gieger, Marc Gunter, Sei Harada, Tamara Harris, David Herrington, Ann Hsing, Mattias Johannson, Claudia Langenberg, Jessica Lasky-Su, Loic Le Marchand, Charles Matthews, Cristina Menni, Matej Oresic, Eric Orwoll, Alexandre Pereira, Kathyrn Rexrode, Svati Shah, Xiao-ou Shu, Victoria Stevens, Bing Yu, Hua Zhao, Krista Zanetti. COnsortium for METabolomics Studies (COMETS): leveraging resources to accelerate scientific discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4238. doi:10.1158/1538-7445.AM2017-4238

Objective to investigate plasma S100b concentration following competitive play and concussive episodes; to compare plasma S100b concentration in professional rugby players with age matched non-contact athletic controls; to develop an objective blood biomarker to aid in the diagnosis of concussion. Design Prospective case-control study Setting Professional rugby club, Elite rowing clubs. Participants Professional rugby players (n=44); elite rowers (age/activity matched athletic controls) (n=16). Interventions Blood samples were collected at baseline (14 days prior to training/competitive play) in both the rugby and rowing cohort. Rugby players were again assessed one hour following competitive games in the early, middle and late season (3 times). This was repeated again the following season (6 data sets). Concussed players were re-evaluated following concussion. Outcomes Plasma was prepared and assessed for S100b concentration by ELISA (Abnova). Metabolomic profiling of samples was carried out by two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). Main results There was significant increase in plasma S100b following competitive play, compared to baseline (p Conclusions There is a significant effect of competitive play on circulating S100b. Data on metabolomic profiles are being collected continually throughout the season and being clustered according to concussive and non-concussive episodes. It is hoped that this analysis may reveal concussion-specific changes in the metabolomic profile of players. Competing interests None.

International audience; Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

The gut microbiota is an environmental factor which affects host metabolism and correspondingly contributes to obesity and its cardiometabolic comorbidities. However, the mechanisms behind the regulation of host lipid metabolism by gut microbiota are poorly understood. The mechanistic in vivo studies over the past decade combining gnotobiotic animal models, metagenomics, and metabolomics have begun to decipher the role of the gut microbiota in the regulation of host physiology. However, since the animal studies cannot be easily extrapolated to humans, it still remains unclear if an altered microbiota associated with a specific disease in humans is a causative factor or merely a consequence of the disease state or both. Cheaper and more comprehensive sequencing tools for the analysis of gut microbiota, together with the recent advances in mass spectrometry-based analysis of molecular lipids, are expected to contribute to our understanding of the mechanisms linking gut microbiota, host lipid metabolism, and how they together contribute to metabolic comorbidities of obesity.

An ultra high performance liquid chromatography tandem mass spectrometry method (UHPLC-MS/MS) was developed for the determination of 33 target and 28 unknown bile acids (BAs) in biological samples. Sixty-one BAs could be measured in 20 min using only a small amount of sample and with a simple sample preparation. The method proved to be very sensitive (limit of detection 5-350 pg/mL, lower limit of quantitation 0.1-2.6 ng/mL), linear (R(2) > 0.99) and reproducible (typically CV, Funding agencies:Academy of Finland Centre of Excellence in Molecular Systems in Immunology and Physiology research SyMMys 250114

Background & Aims\ud \ud The Glu167Lys (E167K) variant in the transmembrane 6 superfamily member 2 protein (TM6SF2) was recently shown to influence liver fat (LFAT) content. We aimed at studying how this variant influences circulating triacylglycerol (TAG) signatures and whether it influences hepatic or adipose tissue insulin sensitivity.\ud Methods\ud \ud We genotyped 300 Finnish subjects for the E167K (rs58542926) variant in TM6SF2 and for the I148M (rs738409) variant in the patatin-like phospholipase domain-containing protein 3 (PNPLA3) in whom LFAT was measured using 1H-MRS and circulating lipids by UPLC-MS. We compared the plasma lipidome between E167K carriers (TM6SF2EK/KK) and non-carriers (TM6SF2EE), and between three groups of NAFLD: (i) carriers of the E167K but not of the I148M variant in PNPLA3 (‘TM6SF2 NAFLD’), (ii) carriers of the I148M but not of the E167K variant (‘PNPLA3 NAFLD’), and (iii) non-carriers of either risk allele (‘Non-risk NAFLD’). Hepatic and adipose tissue insulin sensitivities were measured using the euglycemic hyperinsulinemic clamp technique combined with infusion of [3-3H]glucose in 111 subjects.\ud Results\ud \ud The LFAT content was 34% higher in the TM6SF2EK/KK (13.07 ± 1.57%) than in the TM6SF2EE group (9.77 ± 0.58%, p = 0.013). The effect of insulin on glucose production and lipolysis were significantly higher in the TM6SF2EK/KK than in the TM6SF2EE group. Comparison of the three NAFLD groups with similar LFATs showed that both the ‘TM6SF2 NAFLD’ and ‘PNPLA3 NAFLD’ had significantly lower triglyceride levels and were characterized by lower levels of most common TAGs compared to the ‘Non-risk NAFLD’ group.\ud Conclusions\ud \ud We conclude that the E167K variant in TM6SF2 is associated with a distinct subtype of NAFLD, characterized by preserved insulin sensitivity with regard to lipolysis, hepatic glucose production and lack of hypertriglyceridemia despite a clearly increased LFAT content.

Islet autoimmunity in children who later progress to type 1 diabetes is preceded by dysregulated serum metabolite profiles, but the origin of these metabolic changes is unknown. The gut microbiota affects host metabolism and changes in its composition contribute to several immune-mediated diseases; however, it is not known whether the gut microbiota is involved in the early metabolic disturbances in progression to type 1 diabetes. We rederived non-obese diabetic (NOD) mice as germ free to explore the potential role of the gut microbiota in the development of diabetic autoimmunity and to directly investigate whether the metabolic profiles associated with the development of type 1 diabetes can be modulated by the gut microbiota. The absence of a gut microbiota in NOD mice did not affect the overall diabetes incidence but resulted in increased insulitis and levels of interferon gamma and interleukin 12; these changes were counterbalanced by improved peripheral glucose metabolism. Furthermore, we observed a markedly increased variation in blood glucose levels in the absence of a microbiota in NOD mice that did not progress to diabetes. Additionally, germ-free NOD mice had a metabolite profile similar to that of pre-diabetic children. Our data suggest that germ-free NOD mice have reduced glycaemic control and dysregulated immunologic and metabolic responses.

Primary obesity and psychotic disorders are similar with respect to the associated changes in energy balance and comorbidities, including the metabolic syndrome. The specific underlying mechanisms lin

The contribution of peroxisomal proliferator-activated receptor (PPAR)-γ agonism in pancreatic β-cells to the antidiabetic actions of thiazolidinediones has not been clearly elucidated. Genetic models of pancreatic β-cell PPARγ ablation have revealed a potential role for PPARγ in β-cell expansion in obesity but a limited role in normal β-cell physiology. Here we overexpressed PPARγ1 or PPARγ2 specifically in pancreatic β-cells of mice subjected to high-fat feeding using an associated adenovirus (β-PPARγ1-HFD and β-PPARγ2-HFD mice). We show β-cell-specific PPARγ1 or PPARγ2 overexpression in diet-induced obese mice exacerbated obesity-induced glucose intolerance with decreased β-cell mass, increased islet cell apoptosis, and decreased plasma insulin compared with obese control mice (β-eGFP-HFD mice). Analysis of islet lipid composition in β-PPARγ2-HFD mice revealed no significant changes in islet triglyceride content and an increase in only one of eight ceramide species measured. Interestingly β-PPARγ2-HFD islets had significantly lower levels of lysophosphatidylcholines, lipid species shown to enhance insulin secretion in β-cells. Gene expression profiling revealed increased expression of uncoupling protein 2 and genes involved in fatty acid transport and β-oxidation. In summary, transgenic overexpression of PPARγ in β-cells in diet-induced obesity negatively impacts whole-animal carbohydrate metabolism associated with altered islet lipid content, increased expression of β-oxidative genes, and reduced β-cell mass., Funding agencies:Canadian Diabetes Association University of Northern British Columbia Research Project Awards

Postprandial responses to food are complex, involving both genetic and environmental factors. We studied postprandial responses to a Big Mac meal challenge in monozygotic co-twins highly discordant for body weight. This unique design allows assessment of the contribution of obesity, independent of genetic liability. Comprehensive metabolic profiling using 3 analytical platforms was applied to fasting and postprandial serum samples from 16 healthy monozygotic twin pairs discordant for weight (body mass index difference >3 kg/m2). Nine concordant monozygotic pairs were examined as control pairs. Fecal samples were analyzed to assess diversity of the major bacterial groups by using 5 different validated bacterial group specific denaturing gradient gel electrophoresis methods. No differences in fecal bacterial diversity were detected when comparing co-twins discordant for weight (ANOVA, P

SCOPE: Dysregulation of lipid homeostasis is related to multiple major healthcare problems. The aim of this study was to investigate the effects of n-3 fatty acid (FA) and polyphenol rich diets on plasma and HDL fraction lipidomic profiles in subjects at high cardiovascular risk. METHODS AND RESULTS: Ultra performance LC coupled to quadrupole TOF/MS mass spectrometry global lipidomic profiling was applied to plasma and HDL fraction from an 8 wk randomized intervention with four isoenergetic diets, differing in their natural n-3 FA and polyphenols content, in 78 subjects with a high BMI, abdominal obesity, and at least one other feature of the metabolic syndrome. Dependency network analysis showed a different pattern of associations between lipidomics, dietary, and clinical variables after the dietary interventions. The most remarkable associations between variables were observed after the diet high in n-3 FA and polyphenols, as the inverse association between gallic acid intake and LDL cholesterol levels, which was indirectly associated with a HDL cluster exclusively comprised lysophospholipids. CONCLUSION: This is the first human randomized controlled trial showing direct and indirect associations with lipid molecular species and clinical variables of interest in the evaluation of the metabolic syndrome after diets naturally rich in polyphenols.

Lipids are a vital class of essential metabolites which have many key biological functions. Lipidomics is a subdiscipline of metabolomics, with the focus on the global study of molecular lipids, including pathways and networks of cellular lipids in biological systems. The recent advances in mass spectrometry have highly influenced the field of lipidomics. This chapter describes the main methods utilized in mass spectrometry-based lipidomics, including shotgun MS, surface MS, and MS combined with chromatographic techniques.

We found significant species-specific correlations between the use of two synonymous codons and protein secondary structure units by comparing the three-dimensional structures of human and Escherichia coli proteins with their mRNA sequences. The correlations are not explained by codon-context, expression level, GC/AU content, or positional effects. The E. coli correlation is between Asn AAC and the C-terminal regions of beta-sheet segments; it may result from selection for translational accuracy, suggesting the hypothesis that downstream Asn residues are important for beta-sheet formation. The correlation in human proteins is between Asp GAU and the N termini of alpha-helices; it may be important for eukaryote-specific sequential, cotranslational folding. The kingdom-specific correlations may reflect kingdom-specific differences in translational mechanisms. The correlations may help identify residues that are important for secondary structure formation, be useful in secondary structure prediction algorithms, and have implications for recombinant gene expression.

Aims/hypothesis We examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data. Methods We analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy (1H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively. Results Individuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids (‘lipid triplet’) was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study. Conclusions/interpretation The liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease. Electronic supplementary material The online version of this article (doi:10.1007/s00125-013-2981-2) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

If we are to study complex multi-factorial disorders such as Metabolic Syndrome (MetS) by applying the state-of-the-art ‘omics’ technologies, the reductionist approach commonly applied in life sciences is no longer suitable. In order to understand the adaptive changes in molecular networks in different stages of the disease pathogenesis, a comprehensive view of the system is needed because activation of different pathways may still lead to the same functionality, but with different metabolic costs. Systems biology emerged as an inter-disciplinary field of study that focuses on complex interactions within and between biological systems, using a more holistic perspective approach to biological and biomedical research. While the importance of the systems approach has already been recognized decades ago, the experimental and modeling techniques have matured to the level where comprehensive characterization of biological systems at the molecular level is now feasible.

We examined whether relative concentrations of circulating triacylglycerols (TAGs) between carriers compared with noncarriers of PNPLA3I148M gene variant display deficiency of TAGs, which accumulate in the liver because of defective lipase activity. We also analyzed the effects of obesity-associated nonalcoholic fatty liver disease (NAFLD) independent of genotype, and of NAFLD due to either PNPLA3I148M gene variant or obesity on circulating TAGs. A total of 372 subjects were divided into groups based on PNPLA3 genotype or obesity. Absolute and relative deficiency of distinct circulating TAGs was observed in the PNPLA3148MM/148MI compared with the PNPLA3148II group. Obese and ‘nonobese’ groups had similar PNPLA3 genotypes, but the obese subjects were insulin-resistant. Liver fat was similarly increased in obese and PNPLA3148MM/148MI groups. Relative concentrations of TAGs in the obese subjects versus nonobese displayed multiple changes. These closely resembled those between obese subjects with NAFLD but without PNPLA3I148M versus those with the I148M variant and NAFLD. The etiology of NAFLD influences circulating TAG profiles. ‘PNPLA3 NAFLD’ is associated with a relative deficiency of TAGs, supporting the idea that the I148M variant impedes intrahepatocellular lipolysis rather than stimulates TAG synthesis. ‘Obese NAFLD’ is associated with multiple changes in TAGs, which can be attributed to obesity/insulin resistance rather than increased liver fat content per se.

Lipids are a diverse group of metabolites that have many key biological functions, acting as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Due to their importance lipids are under tight homeostatic control and exhibit spatial and dynamic complexity at multiple levels. It is thus not surprising that altered lipid metabolism plays important roles in the pathogenesis of most of the common diseases. Lipidomics emerged as a discipline which is dedicated to global study of lipidomes, including pathways and networks of lipids in biological systems. When studying the lipidomes at a systems level, one of the key challenges is how to address the lipid functionality at many physiological levels, from metabolic and signaling pathways to spatial systems such as cellular membranes and lipoprotein particles. Besides the better analytical techniques to study lipids, computational techniques have started to emerge which enable modeling of lipidomes in their spatial and dynamic context. Together, the recent methodological advances in lipidomics have a potential to open novel avenues for predictive and preventive medicine. This review focuses on progress in systems approaches to study lipids in health and disease, with specific emphasis on clinical applications.

OBJECTIVE: Different healthy food patterns may modify cardiometabolic risk. We investigated the effects of an isocaloric healthy Nordic diet on insulin sensitivity, lipid profile, blood pressure and inflammatory markers in people with metabolic syndrome. DESIGN: We conducted a randomised dietary study lasting for 18 to 24 weeks in individuals with features of metabolic syndrome (mean age 55 years, BMI 31.6 kg/m(2) , 67% women). Altogether 309 individuals were screened, 200 started the intervention after 4-weeks run-in period, and 96 (proportion of dropouts 7.9%) and 70 individuals (dropouts 27%) completed the study, in the Healthy diet and Control diet groups, respectively. Healthy diet included whole grain products, berries, fruits and vegetables, rapeseed oil, three fish meals per week, and low fat dairy products. An average Nordic diet served as a Control diet. Compliance was monitored by repeated 4-d food diaries and fatty acid composition of serum phospholipids. RESULTS: Body weight remained stable, and no significant changes were observed in insulin sensitivity or blood pressure. Significant changes between the groups were found in non HDL cholesterol (-0.18, 95% CI -0.35;-0.01, p= 0.04), LDL- to HDL cholesterol (-0.15, -0.28;-0.00, p=0.046) and apolipoprotein B to apolipoprotein A1 ratios (-0.04, -0.07; -0.00, p=0.025) favouring the Healthy diet. IL-1Ra increased during the Control diet (difference -84,-133;-37 ng/l, p=0.00053). Intakes of saturated fats (beta estimate 4.48, 0.02; 8.53, p=0.049) and magnesium (-0.23, -0.41;-0.05, p=0.012) were associated with IL-1Ra. CONCLUSIONS: Healthy Nordic diet improved lipid profile and had a beneficial effect on low-grade inflammation. © 2013 The Association for the Publication of the Journal of Internal Medicine.

Molecular subtyping of breast cancer is necessary for therapy selection and mandatory for all breast cancer patients. Metabolic alterations are considered a hallmark of cancer and several metabolic drugs are currently being investigated in clinical trials. However, the dependence of metabolic alterations on breast cancer subtypes has not been investigated on -omics scale. Thus, 204 estrogen receptor positive (ER+) and 67 estrogen receptor negative (ER−) breast cancer tissues were investigated using GC–TOFMS based metabolomics. 19 metabolites were detected as altered in a predefined training set (2/3 of tumors) and could be validated in a predefined validation set (1/3 of tumors). The metabolite changes included increases in beta-alanine, 2-hydroyglutarate, glutamate, xanthine and decreases in glutamine in the ER− subtype. Beta-alanine demonstrated the strongest change between ER− and ER+ breast cancer (fold change = 2.4, p = 1.5E − 20). In a correlation analysis with genome-wide expression data in a subcohort of 154 tumors, we found a strong negative correlation (Spearman R = − 0.62) between beta-alanine and 4-aminobutyrate aminotransferase (ABAT). Immunohistological analysis confirmed down-regulation of the ABAT protein in ER− breast cancer. In a Kaplan–Meier analysis of a large external expression data set, the ABAT transcript was demonstrated to be a positive prognostic marker for breast cancer (HR = 0.6, p=3.2E-15).Biological significanceIt is well-known for more than a decade that breast cancer exhibits distinct gene expression patterns depending on the molecular subtype defined by estrogen receptor (ER) and HER2 status. Here, we show that breast cancer exhibits distinct metabolomics patterns depending on ER status. Our observation supports the current view of ER+ breast cancer and ER− breast as different diseases requiring different treatment strategies.Metabolic drugs for cancer including glutaminase inhibitors are currently under development and tested in clinical trials. We found glutamate enriched and glutamine reduced in ER− breast cancer compared to ER+ breast cancer and compared to normal breast tissues. Thus, metabolomics analysis highlights the ER− subtype as a preferential target for glutaminase inhibitors.For the first time, we report on a regulation of beta-alanine catabolism in cancer. In breast cancer, ABAT transcript expression was variable and correlated with ER status. Low ABAT transcript expression was associated with low ABAT protein expression and high beta-alanine concentration. In a large external microarray cohort, low ABAT expression shortened recurrence-free survival in breast cancer, ER+ breast cancer and ER− breast cancer.

Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

Activation of naive T helper cells by presentation of cognate antigen initiates a complex intracellular signaling process leading to development of functionally active effector cell population. The switch from quiescent naive state to activated state involves a profound change of cellular metabolism, required for completion of multiple rounds of proliferation. Using ultra performance liquid chromatography mass spectrometry, we analyzed how this change is reflected on the cellular lipid composition in human umbilical cord blood T-cells. We found that considerable concentration changes take place during the first 72 hours after T-cell receptor activation, correlating with first rounds of activation-induced cell division. Most importantly, composition of phosphatidylcholines and phosphatidylethanolamines exhibited consistent trend towards shorter and more saturated molecular species. Together with related transcriptomics data, the results clearly suggested induction of de novofatty acid synthesis and accumulation of endogenously synthesized fatty acids into the cellular membranes, leading to partial remodeling of the cellular lipidome in the newly developed effector cell population.

Purpose Syrah red grapes are used in the production of tannin-rich red wines. Tannins are high molecular weight molecules, proanthocyanidins (PAs), and poorly absorbed in the upper intestine. In this study, gut microbial metabolism of Syrah grape phenolic compounds was investigated. Methods Syrah grape pericarp was subjected to an enzymatic in vitro digestion model, and red wine and grape skin PA fraction were prepared. Microbial conversion was screened using an in vitro colon model with faecal microbiota, by measurement of short-chain fatty acids by gas chromatography (GC) and microbial phenolic metabolites using GC with mass detection (GC–MS). Red wine metabolites were further profiled using two-dimensional GC mass spectrometry (GCxGC-TOFMS). In addition, the effect of PA structure and dose on conversion efficiency was investigated by GC–MS. Results Red wine exhibited a higher degree of C1–C3 phenolic acid formation than PA fraction or grape pericarp powders. Hydroxyphenyl valeric acid (flavanols and PAs as precursors) and 3,5-dimethoxy-4-hydroxybenzoic acid (anthocyanin as a precursor) were identified from the red wine metabolite profile. In the absence of native grape pericarp or red wine matrix, the isolated PAs were found to be effective in the dose-dependent inhibition of microbial conversions and short-chain fatty acid formation. Conclusions Metabolite profiling was complementary to targeted analysis. The identified metabolites had biological relevance, because the structures of the metabolites resembled fragments of their grape phenolic precursors or were in agreement with literature data. Electronic supplementary material The online version of this article (doi:10.1007/s00394-012-0391-8) contains supplementary material, which is available to authorized users.

Comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) was applied in the quantification and identification of organic compounds in patient-matched human cerebrospinal fluid (CSF) and serum samples. Concentrations of 21 amino and hydroxyl acids varied from 0.04 to 77 ng/μl in CSF and from 0.1 to 84 ng/μl in serum. In total, 91 metabolites out of over 1200 detected were identified based on mass spectra and retention indices. The other metabolites were identified at the functional group level. The main metabolites detected in CSF were sugar and amino acid derivatives. The CSF and serum had clearly distinct metabolic profiles, with larger biological variation in the serum than in CSF. The GC × GC–TOFMS allowed detection and identification of several metabolites that have not been previously detected in CSF.

Accumulating evidence indicates that the intestinal microbiota regulates our physiology and metabolism. Bacteria marketed as probiotics confer health benefits that may arise from their ability to affect the microbiota. Here high-throughput screening of the intestinal microbiota was carried out and integrated with serum lipidomic profiling data to study the impact of probiotic intervention on the intestinal ecosystem, and to explore the associations between the intestinal bacteria and serum lipids. We performed a comprehensive intestinal microbiota analysis using a phylogenetic microarray before and after Lactobacillus rhamnosus GG intervention. While a specific increase in the L. rhamnosus-related bacteria was observed during the intervention, no other changes in the composition or stability of the microbiota were detected. After the intervention, lactobacilli returned to their initial levels. As previously reported, also the serum lipid profiles remained unaltered during the intervention. Based on a high-resolution microbiota analysis, intake of L. rhamnosus GG did not modify the composition of the intestinal ecosystem in healthy adults, indicating that probiotics confer their health effects by other mechanisms. The most prevailing association between the gut microbiota and lipid profiles was a strong positive correlation between uncultured phylotypes of Ruminococcus gnavus-group and polyunsaturated serum triglycerides of dietary origin. Moreover, a positive correlation was detected between serum cholesterol and Collinsella (Coriobacteriaceae). These associations identified with the spectrometric lipidome profiling were corroborated by enzymatically determined cholesterol and triglyceride levels. Actinomycetaceae correlated negatively with triglycerides of highly unsaturated fatty acids while a set of Proteobacteria showed negative correlation with ether phosphatidylcholines. Our results suggest that several members of the Firmicutes, Actinobacteria and Proteobacteria may be involved in the metabolism of dietary and endogenous lipids, and provide a scientific rationale for further human studies to explore the role of intestinal microbes in host lipid metabolism.