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R scripts to reproduce the statistical tests for the TCGA survival analyses.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities. Cancer Res; 76(4); 796–804. ©2015 AACR.

Results of statistical tests for all metabolites in serum samples.

Supplementary Tables related to clinicopathological data (S1), metabolomics results (S3, S5, S6), gene expression and copy number survival analyses (S7-S8) and gene set enrichment analyses (S9-10) as well as Supplementary Figures (S1-S6) related to metabolomics results.

Supplementary Figure Legends

Sarraceniaceae is a New World carnivorous plant family comprising three genera: Darlingtonia, Heliamphora, and Sarracenia. The plants occur in nutrient-poor environments and have developed insectivorous capability in order to supplement their nutrient uptake. Sarracenia flava contains the alkaloid coniine, otherwise only found in Conium maculatum, in which its biosynthesis has been studied, and several Aloe species. Its ecological role and biosynthetic origin in S. flava is speculative. The aim of the current research was to investigate the occurrence of coniine in Sarracenia and Darlingtonia and to identify common constituents of both genera, unique compounds for individual variants and floral scent chemicals. In this comprehensive metabolic profiling study, we looked for compound patterns that are associated with the taxonomy of Sarracenia species. In total, 57 different Sarracenia and D. californica accessions were used for metabolite content screening by gas chromatography-mass spectrometry. The resulting high-dimensional data were studied using a data mining approach. The two genera are characterized by a large number of metabolites and huge chemical diversity between different species. By applying feature selection for clustering and by integrating new biochemical data with existing phylogenetic data, we were able to demonstrate that the chemical composition of the species can be explained by their known classification. Although transcriptome analysis did not reveal a candidate gene for coniine biosynthesis, the use of a sensitive selected ion monitoring method enabled the detection of coniine in eight Sarracenia species, showing that it is more widespread in this genus than previously believed.

Background & Aims\ud \ud Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409.\ud \ud Methods\ud \ud We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling.\ud \ud Results\ud \ud Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.792 (95% confidence interval, 0.726–0.859). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820–0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone.\ud \ud Conclusions\ud \ud A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities. Cancer Res; 76(4); 796–804. ©2015 AACR.

Aims/hypothesisVirally induced inflammatory responses, beta cell destruction and release of beta cell autoantigens may lead to autoimmune reactions culminating in type 1 diabetes. Therefore, viral capability to induce beta cell death and the nature of virus-induced immune responses are among key determinants of diabetogenic viruses. We hypothesised that enterovirus infection induces a specific gene expression pattern that results in islet destruction and that such a host response pattern is not shared among all enterovirus infections but varies between virus strains.MethodsThe changes in global gene expression and secreted cytokine profiles induced by lytic or benign enterovirus infections were studied in primary human pancreatic islet using DNA microarrays and viral strains either isolated at the clinical onset of type 1 diabetes or capable of causing a diabetes-like condition in mice.ResultsThe expression of pro-inflammatory cytokine genes (IL-1-α, IL-1-β and TNF-α) that also mediate cytokine-induced beta cell dysfunction correlated with the lytic potential of a virus. Temporally increasing gene expression levels of double-stranded RNA recognition receptors, antiviral molecules, cytokines and chemokines were detected for all studied virus strains. Lytic coxsackievirus B5 (CBV-5)-DS infection also downregulated genes involved in glycolysis and insulin secretion.Conclusions/interpretationThe results suggest a distinct, virus-strain-specific, gene expression pattern leading to pancreatic islet destruction and pro-inflammatory effects after enterovirus infection. However, neither viral replication nor cytotoxic cytokine production alone are sufficient to induce necrotic cell death. More likely the combined effect of these and possibly cellular energy depletion lie behind the enterovirus-induced necrosis of islets.

The human colon contains a diverse microbial population which contributes to degradation and metabolism of food components. Drug metabolism in the colon is generally poorly understood. Metabolomics techniques and in vitro colon models are now available which afford detailed characterization of drug metabolites in the context of colon metabolism. The aim of this work was to identify novel drug metabolites of Simvastatin (SV) by using an anaerobic human in vitro colon model at body temperature coupled with systems biology platform, excluding the metabolism of the host liver and intestinal epithelia. Comprehensive two-dimensional gas chromatography with a time-of-flight mass spectrometry (GC*GC-TOFMS) was used for the metabolomic analysis. Metabolites showing the most significant differences in the active faecal suspension were elucidated in reference with SV fragmentation and compared with controls: inactive suspension or buffer with SV, or with active suspension alone. Finally, time courses of selected metabolites were investigated. Our data suggest that SV is degraded by hydrolytic cleavage of methylbutanoic acid from the SV backbone. Metabolism involves demethylation of dimethylbutanoic acid, hydroxylation/dehydroxylation and ß-oxidation resulting in the production of 2-hydroxyisovaleric acid (3-methyl-2-hydroxybutanoic acid), 3-hydroxybutanoic acid and lactic acid (2-hydroxypropanoic acid), and finally re-cyclisation of heptanoic acid (possibly de-esterified and cleaved methylpyranyl arm) to produce cyclohexanecarboxylic acid. Our study elucidates a pathway of colonic microbial metabolism of SV as well as demonstrates the applicability of the in vitro colon model and metabolomics to the discovery of novel drug metabolites from drug response profiles.

Background and aims Whole-grain cereals and diets with a low glycemic index may protect against the development of type 2 diabetes and heart disease, but the mechanisms are poorly understood. We studied the effect of carbohydrate modification on serum metabolic profiles, including lipids and branched chain amino acids, and dependencies between these and specific gene expression pathways in adipose tissue. Methods and results Twenty subjects with metabolic syndrome were selected from the larger FUNGENUT study population, randomized either to a diet high in oat and wheat bread and potato (OWP) or rye bread and pasta (RP). Serum metabolomics analyses were performed using ultra-performance liquid chromatography coupled to electrospray ionization mass spectrometry (UPLC/MS), gas chromatography (GC) and UPLC. In the OWP group multiple proinflammatory lysophosphatidylcholines increased, while in the RP group docosahexaenoic acid (DHA 22:6n-3) increased and isoleucine decreased. mRNA expression of stress reactions- and adipose tissue differentiation-related genes were up-regulated in adipose tissue in the OWP group. In the RP group, however, pathways related to stress reactions and insulin signaling and energy metabolism were down-regulated. The lipid profiles had the strongest association with the changes in the adipose tissue differentiation pathway when using the elastic net regression model of the lipidomic profiles on selected pathways. Conclusion Our results suggest that the dietary carbohydrate modification alters the serum metabolic profile, especially in lysoPC species, and may, thus, contribute to proinflammatory processes which in turn promote adverse changes in insulin and glucose metabolism.

The gut microbiota has recently been identified as an environmental factor that may promote metabolic diseases. To investigate the effect of gut microbiota on host energy and lipid metabolism, we compared the serum metabolome and the lipidomes of serum, adipose tissue, and liver of conventionally raised (CONV-R) and germ-free mice. The serum metabolome of CONV-R mice was characterized by increased levels of energy metabolites, e.g., pyruvic acid, citric acid, fumaric acid, and malic acid, while levels of cholesterol and fatty acids were reduced. We also showed that the microbiota modified a number of lipid species in the serum, adipose tissue, and liver, with its greatest effect on triglyceride and phosphatidylcholine species. Triglyceride levels were lower in serum but higher in adipose tissue and liver of CONV-R mice, consistent with increased lipid clearance. Our findings show that the gut microbiota affects both host energy and lipid metabolism and highlights its role in the development of metabolic diseases., Human Frontier of Science Program RGY64/2008 Swedish Research Council K2007-65X-20421-01-04 Lars Hierta's Foundation Nanna Svartz Foundation Fredrik and Ingrid Thurings Foundations Swedish Nutrition Foundation Vastra Gotalandsregionen

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.

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package., Drug Discovery Collection: The authors take a pan-cancer approach that provides functional insights into molecular vulnerabilities across various cancer subtypes and patient-specific target addiction patterns that could lead to actionable treatment strategies.

Summary We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

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.

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.

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.

Mitochondria are dynamic organelles that play a key role in energy conversion. Optimal mitochondrial function is ensured by a quality-control system tightly coupled to fusion and fission. In this connection, mitofusin 2 (Mfn2) participates in mitochondrial fusion and undergoes repression in muscle from obese or type 2 diabetic patients. Here, we provide in vivo evidence that Mfn2 plays an essential role in metabolic homeostasis. Liver-specific ablation of Mfn2 in mice led to numerous metabolic abnormalities, characterized by glucose intolerance and enhanced hepatic gluconeogenesis. Mfn2 deficiency impaired insulin signaling in liver and muscle. Furthermore, Mfn2 deficiency was associated with endoplasmic reticulum stress, enhanced hydrogen peroxide concentration, altered reactive oxygen species handling, and active JNK. Chemical chaperones or the antioxidant N -acetylcysteine ameliorated glucose tolerance and insulin signaling in liver-specific Mfn2 KO mice. This study provides an important description of a unique unexpected role of Mfn2 coordinating mitochondria and endoplasmic reticulum function, leading to modulation of insulin signaling and glucose homeostasis in vivo.

The insulin/IGF signaling pathway is a highly conserved regulator of metabolism in flies and mammals, regulating multiple physiological functions including lipid metabolism. Although insulin signaling is known to regulate the activity of a number of enzymes in metabolic pathways, a comprehensive understanding of how the insulin signaling pathway regulates metabolic pathways is still lacking. Accepted knowledge suggests the key regulated step in triglyceride (TAG) catabolism is the release of fatty acids from TAG via the action of lipases. We show here that an additional, important regulated step is the activation of fatty acids for beta-oxidation via Acyl Co-A synthetases (ACS). We identify pudgy as an ACS that is transcriptionally regulated by direct FOXO action in Drosophila. Increasing or reducing pudgy expression in vivo causes a decrease or increase in organismal TAG levels respectively, indicating that pudgy expression levels are important for proper lipid homeostasis. We show that multiple ACSs are also transcriptionally regulated by insulin signaling in mammalian cells. In sum, we identify fatty acid activation onto CoA as an important, regulated step in triglyceride catabolism, and we identify a mechanistic link through which insulin regulates lipid homeostasis., Author Summary Type 2 diabetes, which is reaching epidemic proportions worldwide, is often associated with obesity and an imbalance in organismal lipid homeostasis. Therefore, understanding how insulin regulates lipid biosynthesis and breakdown is necessary. Surprisingly, the molecular mechanisms by which insulin regulates fatty acid catabolism are not entirely understood. We show here that insulin signaling regulates expression of acyl-CoA Synthetases (ACS). ACSs couple fatty acids to Coenzyme A, thereby activating them for subsequent biochemical reactions. In Drosophila, we find that insulin signaling modulates expression of one ACS called Pudgy, which activates fatty acids for beta-oxidation. Modulation of pudgy expression leads to changes in overall organismal lipid homeostasis. Likewise, we show that in mammalian cells insulin signaling regulates expression of a number of ACSs and that ACS expression modulates steady-state lipid levels.

We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.

Summary: We present metabolite pathway enrichment analysis (MPEA) for the visualization and biological interpretation of metabolite data at the system level. Our tool follows the concept of gene set enrichment analysis (GSEA) and tests whether metabolites involved in some predefined pathway occur towards the top (or bottom) of a ranked query compound list. In particular, MPEA is designed to handle many-to-many relationships that may occur between the query compounds and metabolite annotations. For a demonstration, we analysed metabolite profiles of 14 twin pairs with differing body weights. MPEA found significant pathways from data that had no significant individual query compounds, its results were congruent with those discovered from transcriptomics data and it detected more pathways than the competing metabolic pathway method did. Availability: The web server and source code of MPEA are available at http://ekhidna.biocenter.helsinki.fi/poxo/mpea/. Contact: matti.kankainen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online.

Mild cognitive impairment (MCI) is considered as a transition phase between normal aging and Alzheimer's disease (AD). MCI confers an increased risk of developing AD, although the state is heterogeneous with several possible outcomes, including even improvement back to normal cognition. We sought to determine the serum metabolomic profiles associated with progression to and diagnosis of AD in a prospective study. At the baseline assessment, the subjects enrolled in the study were classified into three diagnostic groups: healthy controls (n=46), MCI (n=143) and AD (n=47). Among the MCI subjects, 52 progressed to AD in the follow-up. Comprehensive metabolomics approach was applied to analyze baseline serum samples and to associate the metabolite profiles with the diagnosis at baseline and in the follow-up. At baseline, AD patients were characterized by diminished ether phospholipids, phosphatidylcholines, sphingomyelins and sterols. A molecular signature comprising three metabolites was identified, which was predictive of progression to AD in the follow-up. The major contributor to the predictive model was 2,4-dihydroxybutanoic acid, which was upregulated in AD progressors (P=0.0048), indicating potential involvement of hypoxia in the early AD pathogenesis. This was supported by the pathway analysis of metabolomics data, which identified upregulation of pentose phosphate pathway in patients who later progressed to AD. Together, our findings primarily implicate hypoxia, oxidative stress, as well as membrane lipid remodeling in progression to AD. Establishment of pathogenic relevance of predictive biomarkers such as ours may not only facilitate early diagnosis, but may also help identify new therapeutic avenues., Funding Agencies:Health Research Council of The Academy of Finland 121038 EVO Grant from Kuopio University Hospital 5772709

Metabolomics encompasses the study of small molecules in a biological sample. Liquid Chromatography coupled with Mass Spectrometry (LC–MS) profiling is an important approach for the identification and quantification of metabolites from complex biological samples. The amount and complexity of data produced in an LC–MS profiling experiment demand automatic tools for the preprocessing, analysis, and extraction of useful biological information. Data preprocessing—a topic that covers noise filtering, peak detection, deisotoping, alignment, identification, and normalization—is thus an active area of metabolomics research. Recent years have witnessed development of many software for data preprocessing, and still there is a need for further improvement of the data preprocessing pipeline. This review presents an overview of selected software tools for preprocessing LC–MS based metabolomics data and tries to provide future directions.

Identification of early mechanisms that may lead from obesity towards complications such as metabolic syndrome is of great interest. Here we performed lipidomic analyses of adipose tissue in twin pairs discordant for obesity but still metabolically compensated. In parallel we studied more evolved states of obesity by investigating a separated set of individuals considered to be morbidly obese. Despite lower dietary polyunsaturated fatty acid intake, the obese twin individuals had increased proportions of palmitoleic and arachidonic acids in their adipose tissue, including increased levels of ethanolamine plasmalogens containing arachidonic acid. Information gathered from these experimental groups was used for molecular dynamics simulations of lipid bilayers combined with dependency network analysis of combined clinical, lipidomics, and gene expression data. The simulations suggested that the observed lipid remodeling maintains the biophysical properties of lipid membranes, at the price, however, of increasing their vulnerability to inflammation. Conversely, in morbidly obese subjects, the proportion of plasmalogens containing arachidonic acid in the adipose tissue was markedly decreased. We also show by in vitro Elovl6 knockdown that the lipid network regulating the observed remodeling may be amenable to genetic modulation. Together, our novel approach suggests a physiological mechanism by which adaptation of adipocyte membranes to adipose tissue expansion associates with positive energy balance, potentially leading to higher vulnerability to inflammation in acquired obesity. Further studies will be needed to determine the cause of this effect.

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

In response to environmental challenges, biological systems respond with dynamic adaptive changes in order to maintain the functionality of the system. Such adaptations may lead to cumulative stress over time, possibly leading to global failure of the system. When studying such systems responses, it is therefore important to understand them in system-wide and dynamic context. Here we hypothesize that dynamic changes in the topology of functional modules of integrated biological networks reflect their activity under specific environmental challenges. We introduce topological enrichment analysis of functional subnetworks (TEAFS), a method for the analysis of integrated molecular profile and interactome data, which we validated by comprehensive metabolomic analysis of dynamic yeast response under oxidative stress. TEAFS identified activation of multiple stress response related mechanisms, such as lipid metabolism and phospholipid biosynthesis. We identified, among others, a fatty acid elongase IFA38 as a hub protein which was absent at all time points under oxidative stress conditions. The deletion mutant of the IFA38 encoding gene is known for the accumulation of ceramides. By applying a comprehensive metabolomic analysis, we confirmed the increased concentrations over time of ceramides and palmitic acid, a precursor of de novoceramide biosynthesis. Our results imply that the connectivity of the system is being dynamically modulated in response to oxidative stress, progressively leading to the accumulation of (lipo)toxic lipids such as ceramides. Studies of local network topology dynamics can be used to investigate as well as predict the activity of biological processes and the system’s responses to environmental challenges and interventions.

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.

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.

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