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Background: Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.Results: The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.Conclusions: The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

Plants are of pivotal importance to sustain life on Earth because they supply oxygen, food, energy, and many valuable metabolites. All plant constituents, including secondary metabolites, some of which are used as flavors, fragrances, colorants, or pharmaceuticals, are ultimately derived from primary products of photosynthesis through multiple enzymatic steps encoded by the genome of each plant. However, our knowledge of how both primary and secondary metabolites are synthesized and which genes are involved is far from complete. A better understanding of metabolite synthesis and the regulation thereof will be increasingly important for improving the sustainability and efficiency of useful plant production. Recently, the availability of entire genome sequences of Arabidopsis thaliana and rice and the development of functional genomics tools have allowed the elucidation of metabolite syntheses by a systems biology approach (1–3). The mining and exploitation of the data obtained from genomics and the related research areas of genomewide transcriptomics, proteomics, and metabolomics will bring us into a new era of understanding of biological systems (Fig. 1). Fig. 1. A schematic representation of a plant metabolomics platform using a systems biology approach. The platform is divided into multiple analytical approaches aimed at increased coverage of the metabolome. Data processing methods enable us to track both known and unknown metabolites, which can then be integrated from across multiple platforms. Multivariate analyses are used to find trends in data and select most relevant unknown compounds for further identification. Sulfur is one of the essential nutrients for all plants, required to synthesize the key amino acids cysteine and methionine, which in turn are needed to produce functional proteins and many secondary metabolites (4). Approximately 90% of the reduced S is bound through these amino …

Mice lacking Peroxisome Proliferator-Activated Receptor γ2 (PPARγ2) have unexpectedly normal glucose tolerance and mild insulin resistance. Mice lacking PPARγ2 were found to have elevated levels of Lipocalin prostaglandin D synthase (L-PGDS) expression in BAT and subcutaneous white adipose tissue (WAT). To determine if induction of L-PGDS was compensating for a lack of PPARγ2, we crossed L-PGDS KO mice to PPARγ2 KO mice to generate Double Knock Out mice (DKO). Using DKO mice we demonstrated a requirement of L-PGDS for maintenance of subcutaneous WAT (scWAT) function. In scWAT, DKO mice had reduced expression of thermogenic genes, the de novo lipogenic program and the lipases ATGL and HSL. Despite the reduction in markers of lipolysis in scWAT, DKO mice had a normal metabolic rate and elevated serum FFA levels compared to L-PGDS KO alone. Analysis of intra-abdominal white adipose tissue (epididymal WAT) showed elevated expression of mRNA and protein markers of lipolysis in DKO mice, suggesting that DKO mice may become more reliant on intra-abdominal WAT to supply lipid for oxidation. This switch in depot utilisation from subcutaneous to epididymal white adipose tissue was associated with a worsening of whole organism metabolic function, with DKO mice being glucose intolerant, and having elevated serum triglyceride levels compared to any other genotype. Overall, L-PGDS and PPARγ2 coordinate to regulate carbohydrate and lipid metabolism.

Metabolic changes are the final answer of the cell to environmental or genetic changes. While gene expression in breast cancer has extensively been studied, studies on the breast cancer metabolome and its correlation to transcriptional changes are rare. Therefore, a combined metabolomics and transcriptomics study was carried out by the METAcancer consortium. In this project, 275 breast cancer tissues collected as fresh-frozen samples in the METAcancer tumor bank were analyzed using gas chromatography combined with time of flight mass spectrometry (GC-TOF-MS). Out of these, 156 tumor samples were transcriptionally profiled using whole genome DASL. Before, we identified 10 increased and 9 decreased metabolites in the more aggressive estrogen receptor negative (ER-) compared to the estrogen receptor positive (ER+) breast cancer (poster #5573, AACR Annual Meeting 2010). Among these changes, the increase of beta-alanine (fold change = 2.4, p = 1.7E-20) was the strongest and most significant alteration. Comparing breast cancer and normal tissues, beta-alanine turned out to be strongly increased in the cancer tissues (fold change = 2.6, p = 1.1E-34). Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) we identified 135 enzymes that catalyze reactions of the 19 regulated metabolites. These enzymes were subjected to correlation analysis with their substrates and products. As most significant result, a negative relationship between 4-aminobutyrate aminotransferase (ABAT) expression and beta-alanine concentration was detected (Pearson correlation = –0.62, p = 1.1E-17). ABAT was significantly down-regulated in the more aggressive ER- subtype (fold change = –1.9, p = 1.6E-08). The differential expression of ABAT was confirmed in three publicly available breast cancer microarray data sets. The effect of ABAT inhibition was investigated in estrogen receptor positive MCF-7 and triple-negative MDA-MB-231 breast cancer cells. In both cell lines, treatment with vigabatrin, a small molecule that binds irreversibly to ABAT and inhibits the enzymatic activity, increased proliferation. In summary, we detected an accumulation of beta-alanine in breast cancer and an increase of beta-alanine and a down-regulation of ABAT in the more aggressive ER- subtype. The regulation of beta-alanine metabolism that underlies these changes is currently investigated in cell culture models. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4806. doi:1538-7445.AM2012-4806

Therapeutic options for prostate cancer are limited and treatment responses to currently existing therapies are often unsatisfactory. Thus, there is an urgent need for novel agents to target advanced and metastatic prostate cancer cells. We have recently carried out a chemical-biological high-throughput screening of 4,910 known drugs and drug-like molecules in four prostate cancer cell models and two non-tumorigenic prostate epithelial cell lines to identify prostate cancer cell growth selective inhibitors. Only four compounds, antibiotic ionophore monensin, aldehyde dehydrogenase (ALDH) inhibitor disulfiram, histone deacetylase inhibitor trichostatin A and fungicide thiram inhibited selectively cancer cell growth at nanomolar concentrations. The mechanistic studies indicated that monensin inhibited prostate cancer cell growth by inducing oxidative stress and apoptosis. In addition, monensin reduced androgen receptor signaling, showed a synergistic anti-proliferative effect with anti-androgens as well as reduced the levels of MYC and ERG oncogenes and reduced the activity of ALDH in prostate cancer cells. Moreover, antioxidant vitamin C rescued the monensin induced growth inhibition, indicating that oxidative stress plays a key role in the antineoplastic effect of monensin in cultured prostate cancer cells. Our previous Connectivity Map results indicated that monensin has agonistic effects to NF-κB inactivator and oxidative stress inducer niclosamide. Here, we show that monensin indeed reduced the activity of NF-κB pathway. NF-κB maintains cellular antioxidant defence capacity and its inhibition induces oxidative stress as well as reduces tumorigenesis, metastasis and cancer stem cell potential. Cancer stem cells have a controlled redox balance system including high ALDH and CD44 expression which protect cancer stem cells from oxidative stress. Our results confirmed that monensin reduced the cancer stem cell markers in prostate cancer cells. Moreover, monensin induced epithelial cell differentiation shown as well as reduced motility in cultured prostate cancer cells, suggesting that monensin inhibits prostate tumorigenesis by multiple ways. Furthermore, the steroid profiling indicated that monensin increases the levels of oxidative stress inducing steroids and reduces androgen precursors in cultured prostate cancer cells. In conclusion, our results suggest that impairing the redox control, which has a crucial role in cancer cells enabling survival under high intracellular ROS, is a potent way to target prostate cancer cells and potentially also prostate cancer stem cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A62.

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes., Author Summary We have recently found that distinct metabolic disturbances precede β-cell autoimmunity in children who later progress to type 1 diabetes (T1D). Here we performed a murine study using non-obese diabetic (NOD) mice that recapitulated the protocol used in human, followed up by independent studies where NOD mice were studied in relation to risk of diabetes progression. We found that young female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum subgroup. The metabolic phenotypes observed in our study could be relevant as end points for studies investigating T1D pathogenesis and/or responses to interventions. By proceeding from a clinical study via metabolomics and modeling to an experimental model using a similar study design, then evolving further to tissue-specific studies, we hereby also present a conceptually novel approach to reversed translation that may be useful in future therapeutic studies in the context of prevention and treatment of T1D as well as of other diseases characterized by long prodromal periods.

10514 Background: Methods of analytical chemistry allow a global view on metabolic changes occurring during the malignant transformation. Methods: Metabolite profiling of 369 fresh-frozen breast ti...

There is an urgent need for more efficient and more targeted methods of prostate cancer treatment. We have previously shown PLA2G7 (also known as lipoprotein-associated phospholipase A2, Lp-PLA2) to be a possible biomarker and drug target especially in ERG positive prostate cancers by combining gene expression data from prostate cancer tissues in vivo and functional RNAi studies in vitro. To further study the potential of PLA2G7 in prostate cancer management immunohistochemical staining of 119 clinical prostate cancer samples and 112 adjacent normal prostate samples were performed. In addition, a global lipidomic, eicosanoid and gene expression analysis was utilized to study the effect of PLA2G7 silencing in ERG oncogene positive prostate cancer cells. The results emphasized high expression of PLA2G7 in 66 % of the cancer samples, whereas less than 4 % of the adjacent normal tissues showed positive staining. Furthermore, PLA2G7 expression significantly correlated with high Gleason score. PLA2G7 silencing in vitro induced a reduction in the amount of lysophosphatidyl choline and leukotriene E4, as well as in the expression of multiple genes and signaling pathways involved in cell adhesion and motility. In accordance, knock-down of PLA2G7 in 3D prostate cancer cell culture model lead to decreased invasion to extracellular matrix. Interestingly, PLA2G7 silencing also reduced the expression of ALDH1A1, a marker for malignant prostate stem cells and predictor of poor prostate cancer outcome. In conclusion, these novel findings suggest an oncogenic role for PLA2G7 in aggressive prostate cancers and support the rationale for PLA2G7 targeted therapy in these cancers. A novel PLA2G7 inhibitor is currently under clinical evaluation for cardiovascular diseases, presenting thus an interesting opportunity for drug repositioning to other indications, such as prostate cancer. In addition, lipid-lowering statins are known to lower PLA2G7 mass and activity, therefore PLA2G7 may also represent a target of lipid-lowering therapy in reducing the risk of aggressive prostate cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2597. doi:10.1158/1538-7445.AM2011-2597

Dilated cardiomyopathy (DCM), characterized by left ventricular dilatation and systolic dysfunction, constitutes a significant cause for heart failure, sudden cardiac death or need for heart transplantation. Lamin A/C gene (LMNA) on chromosome 1p12 is the most significant disease gene causing DCM and has been reported to cause 7-9% of DCM leading to cardiac transplantation. We have previously performed cardiac magnetic resonance imaging (MRI) to LMNA carriers to describe the early phenotype. Clinically, early recognition of subjects at risk of developing DCM would be important but is often difficult. Thus we have earlier used the MRI findings of these LMNA carriers for creating a model by which LMNA carriers could be identified from the controls at an asymptomatic stage. Some LMNA mutations may cause lipodystrophy. To characterize possible effects of LMNA mutations on lipid profile, we set out to apply global serum lipidomics using Ultra Performance Liquid Chromatography coupled to mass spectrometry in the same LMNA carriers, DCM patients without LMNA mutation and controls. All DCM patients, with or without LMNA mutation, differed from controls in regard to distinct serum lipidomic profile dominated by diminished odd-chain triglycerides and lipid ratios related to desaturation. Furthermore, we introduce a novel approach to identify associations between the molecular lipids from serum and the MR images from the LMNA carriers. The association analysis using dependency network and regression approaches also helped us to obtain novel insights into how the affected lipids might relate to cardiac shape and volume changes. Our study provides a framework for linking serum derived molecular markers not only with clinical endpoints, but also with the more subtle intermediate phenotypes, as derived from medical imaging, of potential pathophysiological relevance.

BACKGROUND: Persons with schizophrenia and other psychotic disorders have a high prevalence of obesity, impaired glucose tolerance, and lipid abnormalities, particularly hypertriglyceridemia and low high-density lipoprotein. More detailed molecular information on the metabolic abnormalities may reveal clues about the pathophysiology of these changes, as well as about disease specificity. METHODS: We applied comprehensive metabolomics in serum samples from a general population-based study in Finland. The study included all persons with DSM-IV primary psychotic disorder (schizophrenia, n = 45; other non-affective psychosis (ONAP), n = 57; affective psychosis, n = 37) and controls matched by age, sex, and region of residence. Two analytical platforms for metabolomics were applied to all serum samples: a global lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry, which covers molecular lipids such as phospholipids and neutral lipids; and a platform for small polar metabolites based on two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). RESULTS: Compared with their matched controls, persons with schizophrenia had significantly higher metabolite levels in six lipid clusters containing mainly saturated triglycerides, and in two small-molecule clusters containing, among other metabolites, (1) branched chain amino acids, phenylalanine and tyrosine, and (2) proline, glutamic, lactic and pyruvic acids. Among these, serum glutamic acid was elevated in all psychoses (P = 0.0020) compared to controls, while proline upregulation (P = 0.000023) was specific to schizophrenia. After adjusting for medication and metabolic comorbidity in linear mixed models, schizophrenia remained independently associated with higher levels in seven of these eight clusters (P < 0.05 in each cluster). The metabolic abnormalities were less pronounced in persons with ONAP or affective psychosis. CONCLUSIONS: Our findings suggest that specific metabolic abnormalities related to glucoregulatory processes and proline metabolism are specifically associated with schizophrenia and reflect two different disease-related pathways. Metabolomics, which is sensitive to both genetic and environmental variation, may become a powerful tool in psychiatric research to investigate disease susceptibility, clinical course, and treatment response., Funding agencies:NARSAD Maltz InvestigatorAward Academy of Finland 129434

Background & AimsAnimal studies suggest that endocannabinoids could contribute to the development of nonalcoholic fatty liver disease (NAFLD). In addition, NAFLD has been shown to be associated with multiple changes in lipid concentrations in liver biopsies. There are no data on splanchnic free fatty acid (FFA), glycerol, ketone body, endocannabinoid, and lipid fluxes in vivo in subjects with NAFLD.MethodsWe performed hepatic venous catheterization studies in combination with [2H2]palmitate infusion in the fasting state and during a low-dose insulin infusion in 9 subjects with various degrees of hepatic steatosis as determined using liver biopsy. Splanchnic balance of endocannabinoids and individual lipids was determined using ultra performance liquid chromatography coupled to mass spectrometry.ResultsConcentrations of the endocannabinoid 2-arachidonoylglycerol were higher in arterialized (91 ± 33 μg/L basally) than in hepatic venous (51 ± 19 μg/L; P < .05) plasma. Fasting arterial (r = 0.72; P = .031) and hepatic venous (r = 0.70; P = .037) concentrations of 2-arachidonoylglycerol were related positively to liver fat content. Analysis of fluxes of 85 different triglycerides showed that the fatty liver overproduces saturated triglycerides. In the plasma FFA fraction in the basal state, the relative amounts of palmitoleate and linoleate were lower and those of stearate and oleate were higher in the hepatic vein than in the artery. Absolute concentrations of all nontriglyceride lipids were comparable in arterialized venous plasma and the hepatic vein both in the basal and insulin-stimulated states.ConclusionsThe human fatty liver takes up 2-arachidonoylglycerol and overproduces triacylglycerols containing saturated fatty acids, which might reflect increased de novo lipogenesis.

Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.

Breast cancer is a biologically inhomogeneous disease that has been extensively studied using molecular high-throughput platforms like microarrays. Molecular methods have been shown to be useful for therapy selection, prediction of disease outcome or complications such as metastases. Worldwide, immunhistological determination of estrogene receptor (ER) and HER2 status is part of the everyday routine diagnostics. A bunch of new biomarkers and biomarker signatures is currently under development and expected to further individualize and improve breast cancer treatment. Here, we present results of a GC-TOF mass spectrometry (GC-MS) metabolite study conducted by the METACancer consortium. In this project, 275 breast cancer tissues collected as fresh-frozen samples in the METACancer tumor bank were analyzed using GC-MS. Prior to metabolic profiling, tumors were divided in a training (187 tumors) and a validation cohort (88 tumors) with comparable clinicopathological characteristics. Both cohorts were profiled at the Fiehn lab (UC Davis, CA), the training cohort at the end of 2008, the validation cohort at the beginning of 2009. Analysis of the training cohort led to the identification of 468 metabolites that are abundant in breast cancer tissues. 161 out of these could be mapped to known chemical structures and metabolite names. Metabolite-by-metabolite analysis of the training cohort revealed 70 metabolites with significantly (p < 0.05, Welch's t-test) different concentrations between between ER+ and ER- tumors. Many of these differences (59%) could be affirmed by analysis of the validation cohort. Only are few changes (9 metabolites), possibly false positives, could be detected between HER2+ and HER2- tumors. None of these changes could be reproduced in the validation cohort. Next, we asked if the tumor cells exhibit metabolite patterns that are specific for ER status. To this end, a metabolic index (MI) was constructed as linear combination of 15 metabolites. The MI was constructed and optimized using only tumors of the training cohort. ROC analysis showed an excellent perfomance of the MI for prediction of the ER status in the training cohort (AUC = 0.91, leave-one-out cross-validation). Validation of the MI in the validation cohort affirmed the excellent performance (AUC = 0.97). A similar approach for prediction of the HER2 status failed (AUC not significantly better than 0.50). In this project, we have shown that metabolic profiling of fresh-frozen breast cancer samples with GC-MS is feasible. Interestingly, we detected a strong dependence of the metabolite patterns on ER status, but no dependence on HER2 status. Analysis of the changes in metabolic pathways between ER+ and ER- breast cancers may contribute to a better understanding of estrogen driven tumor growth. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5573.

AIM: To investigate the effect of three weeks’ intervention with a probiotic Lactobacillus rhamnosus GG (LGG) bacteria on global serum lipidomic profiles and evaluate whether the changes in inflammatory variables (CRP, TNF-α and IL-6) are reflected in the global lipidomic profiles of healthy adults.METHODS: We performed UPLC/MS-based global lipidomic platform analysis of serum samples (n = 26) in a substudy of a randomised, double-blind, placebo-controlled 3-wk clinical intervention trial investigating the immunomodulatory effects of probiotics in healthy adults.RESULTS: A total of 407 lipids were identified, corresponding to 13 different lipid classes. Serum samples showed decreases in the levels of lysophosphatidylcholines (LysoGPCho), sphingomyelins (SM) and several glycerophosphatidylcholines (GPCho), while triacylglycerols (TAG) were mainly increased in the probiotic LGG group during the intervention. Among the inflammatory variables, IL-6 was moderately associated by changes in global lipidomic profiles, with the top-ranked lipid associated with IL-6 being the proinflammatory LysoGPCho (20:4). There was a weak association between the lipidomic profiles and the two other inflammatory markers, TNF-α and CRP.CONCLUSION: This was the first study to investigate the effects of probiotic intervention on global lipidomic profiles in humans. There are indications that probiotic LGG intervention may lead to changes in serum global lipid profiles, as reflected in decreased GPCho, LysoGPCho and SM as well as mainly increased TAG.

Colonization of the fetal and infant gut microbiome results in dynamic changes in diversity, which can impact disease susceptibility. To examine the relationship between human gut microbiome dynamics throughout infancy and type 1 diabetes (T1D), we examined a cohort of 33 infants genetically predisposed to T1D. Modeling trajectories of microbial abundances through infancy revealed a subset of microbial relationships shared across most subjects. Although strain composition of a given species was highly variable between individuals, it was stable within individuals throughout infancy. Metabolic composition and metabolic pathway abundance remained constant across time. A marked drop in alpha-diversity was observed in T1D progressors in the time window between seroconversion and T1D diagnosis, accompanied by spikes in inflammation-favoring organisms, gene functions, and serum and stool metabolites. This work identifies trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T1D progressors from non-progressors.

Background and purpose: Many drugs and environmental contaminants induce hypercholesterolemia and promote the risk of atherosclerotic cardiovascular disease. We tested the hypothesis that pregnane X receptor (PXR), a xenobiotic-sensing nuclear receptor, regulates the level of circulating atherogenic lipids in humans and utilized mouse experiments to identify the mechanisms involved. Experimental approach: We performed serum NMR metabolomics in healthy volunteers administered rifampicin, a prototypical human PXR ligand or placebo in a crossover setting. We used high-fat diet fed wild-type and PXR knockout mice to investigate the mechanisms mediating the PXR-induced alterations in cholesterol homeostasis. Key results: Activation of PXR induced cholesterogenesis both in pre-clinical and clinical settings. In human volunteers, rifampicin increased intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL) and total cholesterol and lathosterol–cholesterol ratio, a marker of cholesterol synthesis, suggesting increased cholesterol synthesis. Experiments in mice indicated that PXR activation causes widespread induction of the cholesterol synthesis genes including the rate-limiting Hmgcr and upregulates the intermediates in the Kandutsch–Russell cholesterol synthesis pathway in the liver. Additionally, PXR activation induced plasma proprotein convertase subtilisin/kexin type 9 (PCSK9), a negative regulator of hepatic LDL uptake, in both mice and humans. We propose that these effects were mediated through increased proteolytic activation of sterol regulatory element-binding protein 2 (SREBP2) in response to PXR activation. Conclusions and implications: PXR activation induces cholesterol synthesis, elevating LDL and total cholesterol in humans. The PXR–SREBP2 pathway is a novel regulator of the cholesterol and PCSK9 synthesis and a molecular mechanism for drug- and chemical-induced hypercholesterolemia.

OBJECTIVE Nonalcoholic fatty liver disease (i.e., increased intrahepatic triglyceride [IHTG] content), predisposes to type 2 diabetes and cardiovascular disease. Adipose tissue lipolysis and hepatic de novo lipogenesis (DNL) are the main pathways contributing to IHTG. We hypothesized that dietary macronutrient composition influences the pathways, mediators, and magnitude of weight gain-induced changes in IHTG. RESEARCH DESIGN AND METHODS We overfed 38 overweight subjects (age 48 ± 2 years, BMI 31 ± 1 kg/m2, liver fat 4.7 ± 0.9%) 1,000 extra kcal/day of saturated (SAT) or unsaturated (UNSAT) fat or simple sugars (CARB) for 3 weeks. We measured IHTG (1H-MRS), pathways contributing to IHTG (lipolysis ([2H5]glycerol) and DNL (2H2O) basally and during euglycemic hyperinsulinemia), insulin resistance, endotoxemia, plasma ceramides, and adipose tissue gene expression at 0 and 3 weeks. RESULTS Overfeeding SAT increased IHTG more (+55%) than UNSAT (+15%, P < 0.05). CARB increased IHTG (+33%) by stimulating DNL (+98%). SAT significantly increased while UNSAT decreased lipolysis. SAT induced insulin resistance and endotoxemia and significantly increased multiple plasma ceramides. The diets had distinct effects on adipose tissue gene expression. CONCLUSIONS Macronutrient composition of excess energy influences pathways of IHTG: CARB increases DNL, while SAT increases and UNSAT decreases lipolysis. SAT induced the greatest increase in IHTG, insulin resistance, and harmful ceramides. Decreased intakes of SAT could be beneficial in reducing IHTG and the associated risk of diabetes.

Bile acids are synthesized from cholesterol in the liver and further metabolized by the gut microbiota into secondary bile acids. Bile acid synthesis is under negative feedback control through activation of the nuclear receptor farnesoid X receptor (FXR) in the ileum and liver. Here we profiled the bile acid composition throughout the enterohepatic system in germ-free (GF) and conventionally raised (CONV-R) mice. We confirmed a dramatic reduction in muricholic acid, but not cholic acid, levels in CONV-R mice. Rederivation of Fxr-deficient mice as GF demonstrated that the gut microbiota regulated expression of fibroblast growth factor 15 in the ileum and cholesterol 7α-hydroxylase (CYP7A1) in the liver by FXR-dependent mechanisms. Importantly, we identified tauro-conjugated beta- and alpha-muricholic acids as FXR antagonists. These studies suggest that the gut microbiota not only regulates secondary bile acid metabolism but also inhibits bile acid synthesis in the liver by alleviating FXR inhibition in the ileum., Human Frontier of Science Program RGY64/2008Novo NordiskLars Hierta'sNanna SvartzFredrik foundationIngrid Thuring foundationLUA-ALF grants from Vastra GotalandsregionenStockholm County Council

We present a new method for exploring multidimensional energy landscapes by using Gaussian packets to characterize metastable states. Focusing on spatial scaling properties of the energy function, we derive nonlinear self‐consistent packet equations which determine the parameters of these Gaussian packets. This provides a unique definition of the thermodynamic properties of metastable states and a computational prescription for their calculation. The packet equations allow bifurcation and enable us to follow the ‘‘trajectories’’ of states as functions of temperature and to determine the hierarchical relationships between states. The method is demonstrated on simple one‐dimensional models and on Lennard‐Jones six‐, seven‐, and eight‐atom microclusters. We find that the microcluster state trajectories that are connected to lowest zero‐temperature energy conformations are also connected to the lowest free‐energy trajectories up to their destabilization temperatures. That is, the global minima have a ‘‘strong scaling’’ property that can accelerate searches for global minima by scale‐dependent annealing methods.

Background Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution. Results The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells. Conclusions The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

Carriers of the hydroxysteroid 17-beta dehydrogenase 13 (HSD17B13) gene variant (rs72613567:TA) have a reduced risk of NASH and cirrhosis but not steatosis. We determined its effect on liver histology, lipidome, and transcriptome using ultra performance liquid chromatography-mass spectrometry and RNA-seq. In carriers and noncarriers of the gene variant, we also measured pathways of hepatic fatty acids (de novo lipogenesis [ONLI and adipose tissue lipolysis [ATL] using (H2O)-H-2 and H-2-glycerol) and insulin sensitivity using H-3-glucose and euglycemic-hyperinsulinemic clamp) and plasma cytokines. Carriers and noncarriers had similar age, sex and BMI. Fibrosis was significantly less frequent while phospholipids, but not other lipids, were enriched in the liver in carriers compared with noncarriers. Expression of 274 genes was altered in carriers compared with noncarriers, consisting predominantly of downregulated inflammation-related gene sets. Plasma IL-6 concentrations were lower, but DNL, ATL and hepatic insulin sensitivity were similar between the groups. In conclusion, carriers of the HSD17B13 variant have decreased fibrosis and expression of inflammation-related genes but increased phospholipids in the liver. These changes are not secondary to steatosis, ONL, ATL, or hepatic insulin sensitivity. The increase in phospholipids and decrease in fibrosis are opposite to features of choline-deficient models of liver disease and suggest HSD17B13 as an attractive therapeutic target.

Motivation: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining.Results: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein–protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.

Background The mechanism behind the lowered postprandial insulin demand observed after rye bread intake compared to wheat bread is unknown. The aim of this study was to use the metabolomics approach to identify potential metabolites related to amino acid metabolism involved in this mechanism. Methods A sourdough fermented endosperm rye bread (RB) and a standard white wheat bread (WB) as a reference were served in random order to 16 healthy subjects. Test bread portions contained 50 g available carbohydrate. In vitro hydrolysis of starch and protein were performed for both test breads. Blood samples for measuring glucose and insulin concentrations were drawn over 4 h and gastric emptying rate (GER) was measured. Changes in the plasma metabolome were investigated by applying a comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry metabolomics platform (GC×GC-TOF-MS). Results Plasma insulin response to RB was lower than to WB at 30 min (P = 0.004), 45 min (P = 0.002) and 60 min (P < 0.001) after bread intake, and plasma glucose response was significantly higher at time point 90 min after RB than WB intake (P = 0.045). The starch hydrolysis rate was higher for RB than WB, contrary to the in vitro protein digestibility. There were no differences in GER between breads. From 255 metabolites identified by the metabolomics platform, 26 showed significant postprandial relative changes after 30 minutes of bread intake (p and q values < 0.05). Among them, there were changes in essential amino acids (phenylalanine, methionine, tyrosine and glutamic acid), metabolites involved in the tricarboxylic acid cycle (alpha-ketoglutaric, pyruvic acid and citric acid) and several organic acids. Interestingly, the levels of two compounds involved in the tryptophan metabolism (picolinic acid, ribitol) significantly changed depending on the different bread intake. Conclusions A single meal of a low fibre sourdough rye bread producing low postprandial insulin response brings in several changes in plasma amino acids and their metabolites and some of these might have properties beneficial for health.

An analytical method for the global profiling of molecular lipids in biological samples, with particular emphasis on the plasmalogen lipids, is described. The global profiling method is based on ultrahigh-performance liquid chromatography combined with quadrupole time-of-flight-mass spectrometry (UHPLC–QTOF-MS). The profiling approach is complemented by UHPLC–LTQ-Orbitrap mass spectrometry in MS n mode for de novo lipid identification.

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little “arm twisting” in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER-EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.