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The chaperone HSP90 is used by B-cell lymphomas to support the stability of proteins involved in oncogenic processes such as signaling and anti-apoptosis. While HSP90 inhibitors decrease the levels of these client proteins favoring cell death they also prompt cellular counter-regulatory mechanisms that diminish the efficacy of these drugs. Improving the clinical activity of HSP90 inhibitors will depend on understanding the complexity of HSP90 functions. Here we show that HSP90 facilitates the function of MYC by improving the efficiency of metabolic pathways through the orchestration of enzymatic networks, and that HSP90 inhibition impairs the metabolic fitness of DLBCLs without client protein degradation. Moreover, drugs inducing sub-lethal metabolic stress in DLBCL cells cause apoptosis upon HSP90 inhibition. To identify metabolic enzymes actively chaperoned by HSP90 we integrated the information from proteomics and metabolomics in DLBCL cell lines. Proteomics was performed from the cytoplasmic fraction of OCI-Ly1 and OCI-Ly7 cells chemically precipitated with PU-H71, an HSP90 inhibitor that selectively binds to HSP90 contained in active multi-chaperone complexes.STRING network analysis of the metabolic client proteins identified several hubs highly enriched for enzymes involved in metabolism of nucleotides (e.g. IMPDH2, CTPS1, CAD), carbohydrates (e.g. G6PD, HK2) and proteins (e.g. MTHFR, ASNS). Functionality of the network was assessed by metabolomics from OCI-Ly1 cells treated with PU-H71 500 nM for 6 h (sub-lethal). This dose and timing assured HSP90 inhibition but no client protein degradation. The proteomics and metabolomics mapping into KEGG pathways showed a significant overlap, indicating that HSP90 preferentially interacts with proteins representing regulatory hubs to coordinate their committed activity and thus secure the flow of the pathway. We quantified the effect of HSP90 on the activity of metabolic networks by measuring glycolysis (by lactate production and medium acidification) and mitochondrial respiration (by oxygen consumption) in OCI-LY1 and OCI-LY7 cell lines upon PU-H71. We found that inhibition of HSP90 decreased glycolysis by 20-25% and respiration by 25% (p To understand the mechanistic relevance of these findings to lymphomagenesis, we analyzed the HSP90 metabolic proteome for common features and found it was significantly enriched (chi-square p Disclosures Yang: Regeneron Pharmaceuticals: Employment. Cerchietti: Lymphoma Research Foundation: Research Funding; Leukemia and Lymphoma Society: Research Funding; Weill Cornell Medicine - New York Presbyterian Hospital: Employment; Celgene: Research Funding.

Protein interactions and complexes are important components of biological systems. Recently, two genome-wide applications of tandem affinity purification (TAP) in yeast have increased significantly the available information on interactions in complexes. Several approaches have been developed to predict protein complexes from these measurements, which generally depend heavily on additional training data in the form of known complexes. In this article, we present an unsupervised algorithm for the identification of protein complexes which is independent of the availability of such additional complex information. Based on a Bootstrap approach, we calculate intuitive confidence scores for interactions more accurate than all other published scoring methods and predict complexes with the same quality as the best supervised predictions. Although there are considerable differences between the Bootstrap and the best published predictions, the set of consistently identified complexes is more than four times as large as for complexes derived from one data set only. Our results illustrate that meaningful and reliable complexes can be determined from the purification experiments alone. As a consequence, the approach presented in this article is easily applicable to large-scale TAP experiments for any species even if few complexes are already known.

Protein sequences are the most important source of evolutionary and functional information for new proteins. In order to facilitate the computationally intensive tasks of sequence analysis, the Similarity Matrix of Proteins (SIMAP) database aims to provide a comprehensive and up-to-date dataset of the pre-calculated sequence similarity matrix and sequence-based features like InterPro domains for all proteins contained in the major public sequence databases. As of September 2007, SIMAP covers approximately 17 million proteins and more than 6 million non-redundant sequences and provides a complete annotation based on InterPro 16. Novel features of SIMAP include a new, portlet-based web portal providing multiple, structured views on retrieved proteins and integration of protein clusters and a unique search method for similar domain architectures. Access to SIMAP is freely provided for academic use through the web portal for individuals at http://mips.gsf.de/simap/and through Web Services for programmatic access at http://mips.gsf.de/webservices/services/SimapService2.0?wsdl.

Small molecules are extensively metabolized and cleared by the kidney. Changes in serum metabolite concentrations may result from impaired kidney function and can be used to estimate filtration (e.g., the established marker creatinine) or may precede and potentially contribute to CKD development. Here, we applied a nontargeted metabolomics approach using gas and liquid chromatography coupled to mass spectrometry to quantify 493 small molecules in human serum. The associations of these molecules with GFR estimated on the basis of creatinine (eGFRcr) and cystatin C levels were assessed in = 0.50) with established kidney function measures: C-mannosyltryptophan, pseudouridine, N-acetylalanine, erythronate, myo-inositol, and N-acetylcarnosine. Higher C-mannosyltryptophan, pseudouridine, and O-sulfo-L-tyrosine concentrations associated with incident CKD (eGFRcr

Background Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. Methods We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. Results Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10−4 to 1.2 × 10−24). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. Conclusions Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0282-y) contains supplementary material, which is available to authorized users.

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease., Author Summary Biological systems operate on multiple, intertwined organizational layers that can nowadays be accesses by high-throughput measurement methods, the so-called ‘omics’ technologies. A major aim in the field of systems biology is to understand the flow of biological information between the different layers at a systems level in both health and disease. To unravel the complex mechanisms underlying those molecular processes and to understand how the different functional levels interact with each other, an integrated analysis of multiple layers, i.e. a ‘multi-omics‘ approach is required. In our present study, we investigate the relationship between circulating metabolites in serum and whole-blood gene expression measured in the blood of individuals from a population-based cohort. To this end, we constructed a correlation network that displays which transcript and metabolite show the same trend of up- and down-regulation. We derived a functional characterization of the network by developing a novel computational analysis. The analysis revealed systematic signatures of signaling, transport and metabolic processes on both a regulatory and a pathway level. Moreover, integrating the network with associations to clinical markers such as HDL-cholesterol, LDL-cholesterol and TG identified coordinately activated pathways or modules which might help to assess the molecular machinery behind such an intermediate phenotype.

Aims/hypothesis Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. Methods Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. Results Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

Although the activation of immune cells is the first and thereby pivotal step in the immunological cascade, the current knowledge about the details of this process is quite limited. Recent studies have shown that aromatic compounds, such as B[ a ]P, influence the immune system even at low concentrations. We investigated the effect of a subtoxic B[ a ]P concentration (50 nM) on the proteome and the metabolome of non-activated and activated Jurkat T cells. The GeLC-MS/MS analysis yielded 2624 unambiguously identified proteins. In addition to typical regulatory pathways associated with T cell activation, pathway analysis by Ingenuity IPA revealed several metabolic processes, for instance purine and pyruvate metabolism. The activation process seems to be influenced by B[ a ]P suggesting an important role of the mTOR pathway in the cellular adaptation. B[ a ]P exposure of non-activated Jurkat cells induced signaling pathways such as protein ubiquitination and NRF2 mediated oxidative stress response as well as metabolic adaptation involving pyruvate, purine and fatty acid metabolism. Thus, we validated the proteome results by determining the concentrations of 183 metabolites with FIA-MS/MS and IC-MS/MS. Furthermore, we were able to show that Jurkat cells metabolize B[ a ]P to B[ a ]P-1,6-dione. The combined evaluation of proteome and metabolome data with an integrated, genome-scale metabolic model provided novel systems biological insights into the complex relation between metabolic and proteomic processes in Jurkat T cells during activation and subtoxic chemical exposure.

Availability of standardized metabolite panels and genome-wide single nucleotide polymorphism (SNP) data endorse the comprehensive analysis of gene-metabolite association. Currently, many studies use genome-wide association analysis to investigate the genetic effects on single metabolites (mGWAS) separately. Such studies have identified several loci that are associated not only with one but with multiple metabolites, facilitated by the fact that metabolite panels often include metabolites of the same or related pathways. Strategies that analyse several phenotypes in a combined way were shown to be able to detect additional genetic loci. One of those methods is the phenotype set enrichment analysis (PSEA) that tests sets of metabolites for enrichment at genes. Here we applied PSEA on two different panels of serum metabolites together with genome-wide data. All analyses were performed as a two-step identification-validation approach, using data from the population-based KORA cohort and the TwinsUK study. In addition to confirming genes that were already known from mGWAS, we were able to identify and validate twelve new genes. Knowledge about gene function was supported by the enriched metabolite sets. For loci with unknown gene functions, the results suggest a function that is interrelated with the metabolites, and hint at the underlying pathways.

We recently developed a novel approach capable of identifying gene combinations to obtain maximal disease risk stratification. Type 1 diabetes has a preclinical phase including seroconversion to autoimmunity and subsequent progression to diabetes. Here, we applied our gene combination approach to identify combinations that contribute either to islet autoimmunity or to the progression from islet autoantibodies to diabetes onset. We examined 12 type 1 diabetes susceptibility genes (INS, ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10) in a cohort of children of parents with type 1 diabetes and prospectively followed from birth. The most predictive combination was subsequently applied to a smaller validation cohort. The combinations of genes only marginally contributed to the risk of developing islet autoimmunity, but could substantially modify risk of progression to diabetes in islet autoantibody-positive children. The greatest discrimination was provided by risk allele scores of five genes, INS, IFIH1, IL18RAP, CD25, and IL2 genes, which could identify 80 % of islet autoantibody-positive children who progressed to diabetes within 6 years of seroconversion and discriminate high risk (63 % within 6 years; 95 % CI 45-81 %) and low risk (11 % within 6 years; 95 % CI 0.1-22 %; p = 4 × 10(-5)) antibody-positive children. Risk stratification by these five genes was confirmed in a second cohort of islet autoantibody children. These findings highlight genes that may affect the rate of the beta-cell destruction process once autoimmunity has initiated and may help to identify islet autoantibody-positive subjects with rapid progression to diabetes.

Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout. © 2013 Nature America, Inc. All rights reserved.

Background Genome-wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites. Methods We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population-based KORA F4 study (N = 2925). In addition, we analysed effects of single-nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites. Results Increased levels of various phosphatidylcholines and decreased levels of various lyso-phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions. Conclusions Our study demonstrated the potential of metabolomics to infer asthma-related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.

Metabolomics is a relatively new high-throughput technology that aims at measuring all endogenous metabolites within a biological sample in an unbiased fashion. The resulting metabolic profiles may be regarded as functional signatures of the physiological state, and have been shown to comprise effects of genetic regulation as well as environmental factors. This potential to connect genotypic to phenotypic information promises new insights and biomarkers for different research fields, including biomedical and pharmaceutical research. In the statistical analysis of metabolomics data, many techniques from other omics fields can be reused. However recently, a number of tools specific for metabolomics data have been developed as well. The focus of this mini review will be on recent advancements in the analysis of metabolomics data especially by utilizing Gaussian graphical models and independent component analysis.

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms., Author Summary Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab.

1,3-Butadiene (BD) is a Class 1 carcinogen present at workplaces, in polluted air, in automobile exhaust, and in tobacco smoke. 2-Hydroxybutene-1-yl mercapturic acid (2-MHBMA) is a urinary metabolite often measured as a biomarker for exposure to BD. Here, we show for the first time that an additional MHBMA isomer is present at significant amounts in human urine, 1-hydroxybutene-2-yl mercapturic acid (1-MHBMA). For its quantification, a highly sensitive UPLC-HILIC-MS/MS method was developed and validated. Analyzing urinary samples of 183 volunteers, we demonstrate that 1-MHBMA is a novel and potentially more reliable biomarker for BD exposure than the commonly analyzed 2-MHBMA.

Background Genome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain. Results Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs. Conclusions We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.

Genome-wide association studies have identified gene regions associated with type 1 diabetes. The aim of this study was to determine how the combined allele frequency of multiple susceptibility genes can stratify islet autoimmunity and/or type 1 diabetes risk. Children of parents with type 1 diabetes and prospectively followed from birth for the development of islet autoantibodies and diabetes were genotyped for single-nucleotide polymorphisms at 12 type 1 diabetes susceptibility genes (ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10 and COBL). Non-human leukocyte antigen (HLA) risk score was defined by the total number of risk alleles at these genes. Receiver operator curve analysis showed that the non-HLA gene combinations were highly effective in discriminating diabetes and most effective in children with a high-risk HLA genotype. The greatest diabetes discrimination was obtained by the sum of risk alleles for eight genes (IFIH1, CTLA4, PTPN22, IL18RAP, SH2B3, KIAA0350, COBL and ERBB3) in the HLA-risk children. Non-HLA-risk allele scores stratified risk for developing islet autoantibodies and diabetes, and progression from islet autoimmunity to diabetes. Genotyping at multiple susceptibility loci in children from affected families can identify neonates with sufficient genetic risk of type 1 diabetes to be considered for early intervention.

In the preceding chapters many aspects of metabolite quantification and relation to trait and disease phenotypes have been described, in particular the linkage of intermediate metabolic traits to genetic heterogeneities. Although many analyses start on the genome-wide level, they end up picking out single polymorphisms or other variations and study these in detail. This reductionist approach is very common in molecular biology and has proven hugely successful over the past decades. In recent years however, a second paradigm has become increasingly popular, namely that of integrating multiple such analyses into larger ones commonly called models. This paradigm, nowadays, is known as systems biology and is expected to penetrate many classical molecular analyses.

ObjectiveTo characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 study.Subjects and methodsAnalyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).ResultsWe found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.ConclusionA set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.

Vital functions of living organisms, such as immune responses or the metabolism, are controlled by complex regulatory networks. These networks comprise, amongst others, regulatory genes called transcription factors and cascades of information-processing proteins such as enzymes. The ultimate goal of the increasingly popular systems biology approach is to set-up extensive computer models that closely reflect the real-life behavior of these biological networks (Kitano, 2002; Werner, 2007). With a reasonable in silico implementation at hand, novel predictions, e.g. about the effect of gene mutations, can be generated by the computer. The two basic modes of regulation we concentrate on here, are inhibition and activation between two factors. Figure 1A visualizes the relation between the concentrations of e.g. two transcription factors, which are linked by an activation (left-hand figure) or inhibition (right-hand figure). Figure 1B shows a network of interacting activations and inhibitions as it might be found in living cells. While a single regulatory interaction can easily be understood, the complex wiring of several interactions, even for a medium-scale model as depicted here, renders themanual investigation of the system’s dynamics unfeasible. For further information on the concepts of regulation, we refer biologically interested readers to Alon (2006). Classical computational modeling approaches attempt to describe biochemical reaction networks as systems of ordinary differential equations (ODEs) (Klipp et al., 2005; Tyson et al., 2002). This requires detailed knowledge about the molecular mechanisms in order to implement precise kinetic rate laws for each biochemical reaction. However, for many biological systems, and especially gene-regulatory networks, only qualitative information about interactions, like “A inhibits B“, is available. A well-established workaround for this lack of information is the application of discrete modeling approaches. In Boolean methodologywe abstract from actual molecule quantities and assign each player in the system the state on or off (e.g. active or inactive). Despite their simplicity, Boolean models have been shown to provide valuable information about the general dynamics and capabilities of the underlying system (Albert & Othmer, 2003; Faure et al., 2006; Samaga et al., 2009). To bridge the gap between discrete and fully quantitative models, we developed Odefy, a MATLABand Octave-compatible toolbox for the automated transformation of Boolean 2

Hematopoiesis is an ideal model system for stem cell biology with advanced experimental access. A systems view on the interactions of core transcription factors is important for understanding differentiation mechanisms and dynamics. In this manuscript, we construct a Boolean network to model myeloid differentiation, specifically from common myeloid progenitors to megakaryocytes, erythrocytes, granulocytes and monocytes. By interpreting the hematopoietic literature and translating experimental evidence into Boolean rules, we implement binary dynamics on the resulting 11-factor regulatory network. Our network contains interesting functional modules and a concatenation of mutual antagonistic pairs. The state space of our model is a hierarchical, acyclic graph, typifying the principles of myeloid differentiation. We observe excellent agreement between the steady states of our model and microarray expression profiles of two different studies. Moreover, perturbations of the network topology correctly reproduce reported knockout phenotypes in silico. We predict previously uncharacterized regulatory interactions and alterations of the differentiation process, and line out reprogramming strategies.

Advances in high-throughput measurements of biological specimens necessitate the development of biologically driven computational techniques. To understand the molecular level of many human diseases, such as cancer, lipid quantifications have been shown to offer an excellent opportunity to reveal disease-specific regulations. The data analysis of the cell lipidome, however, remains a challenging task and cannot be accomplished solely based on intuitive reasoning. We have developed a method to identify a lipid correlation network which is entirely disease-specific. A powerful method to correlate experimentally measured lipid levels across the various samples is a Gaussian Graphical Model (GGM), which is based on partial correlation coefficients. In contrast to regular Pearson correlations, partial correlations aim to identify only direct correlations while eliminating indirect associations. Conventional GGM calculations on the entire dataset can, however, not provide information on whether a correlation is truly disease-specific with respect to the disease samples and not a correlation of control samples. Thus, we implemented a novel differential GGM approach unraveling only the disease-specific correlations, and applied it to the lipidome of immortal Glioblastoma tumor cells. A large set of lipid species were measured by mass spectrometry in order to evaluate lipid remodeling as a result to a combination of perturbation of cells inducing programmed cell death, while the other perturbations served solely as biological controls. With the differential GGM, we were able to reveal Glioblastoma-specific lipid correlations to advance biomedical research on novel gene therapies.

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values

Protein interactions and complexes are major components of biological systems. Recent genome-wide applications of tandem affinity purification (TAP) in yeast have increased significantly the available information on such interactions. From these experiments, protein complexes were predicted with different approaches first from the individual experiments only and later from their combination. The resulting predictions showed surprisingly little agreement and all of the corresponding methods rely on additional training data. In this article, we present an unsupervised algorithm for the identification of protein complexes which is independent of the availability of additional complex information. Based on a bootstrap approach, we calculated intuitive confidence scores for interactions which are more accurate than previous scoring metrics. The complexes determined from this confidence network are of similar quality as the complexes identified by the best supervised approaches. Despite the similar quality of the latest predictions and our predictions, considerable differences are still observed between all of them. Nevertheless, the set of consistently identified complexes is more than four times as large as for the first two studies. Our results illustrate that meaningful and reliable complexes can be determined from the purification experiments alone. As a consequence, the approach presented in this article is easily applicable to large-scale TAP experiments for any organism.

Summary: Gepard provides a user-friendly, interactive application for the quick creation of dotplots. It utilizes suffix arrays to reduce the time complexity of dotplot calculation to Θ(m*log n). A client–server mode, which is a novel feature for dotplot creation software, allows the user to calculate dotplots and color them by functional annotation without any prior downloading of sequence or annotation data. Availability: Both source codes and executable binaries are available at http://mips.gsf.de/services/analysis/gepard Contact: krumsiek@in.tum.de

Reprogramming of tumor cell metabolism contributes to disease aggressiveness and chemoresistance, but how this process is regulated on the molecular levels is unclear. Hsp90 regulates diverse cellular processes through its interaction with co-chaperones and client proteins. Although basally expressed, Hsp90 is almost universally overexpressed in malignant cells due to increased levels of internal (oxidants, genomic instability, unfolded proteins) and external (hypoxia, nutrients limitation, drugs) stressors. Indeed, Hsp90 is starting to be acknowledged also for its role as an integrator of nutrient sensing, tumor bioenergetics and metabolic stress response (Chae et al, Cancer Cell, 2012). Here we report that active cytosolic Hsp90 binds and stabilizes enzymes regulating central metabolic processes in DLBCL, and provides an actionable target for this disease. Despite its overexpression, not all DLBCL are equally responsive to Hsp90 inhibitors. Using a panel of 10 DLBCL cell lines we found that those with higher glycolytic activity were more likely to respond to the Hsp90 inhibitors 17-DMAG and PU-H71. Global metabolomics profiling (GC-MS and UPLC-MS) of Hsp90-dependent (OCI-Ly7, OCI-Ly1, SU-DHL6, OCI-Ly10) vs. Hsp90-independent (Karpas422) cells identified “purine metabolism” (p=0.000027) and “pyrimidine metabolism” (p=0.0017) as upregulated, whereas “TCA cycle” was downregulated (p=0.000027). We validated these findings by short-term (6 h) exposure of OCI-Ly1 and OCI-Ly7 cells with non-cytotoxic doses of PU-H71 and found a highly reduced oxygen consumption rate (OCR) as well as glycolytic rate (ECAR). This was accompanied by a sharp decrease in glucose consumption and lactate production and by only a slight reduction in glutamine consumption. Unexpectedly, the drop in glycolysis and respiration in OCI-Ly1 and OCI-Ly7 did not lead to a significant decrease either in ATP or reducing power, suggesting that actually both the production and the utilization of ATP and redox equivalents are shut down when Hsp90 activity (an ATP-hydrolyzing enzyme) is inhibited. To identify the molecular basis for these differences, we characterized the active Hsp90-associated proteome in DLBCL by using chemical affinity purification followed by MS in cytosolic fractions of two Hsp90-dependent DLBCL cell lines (OCI-Ly1 and OCI-Ly7) in duplicates. This method pulls down only active Hsp90 multichaperone complexes (Moulick et al, Nat Chem Biol, 2011). Pathway enrichment analysis identified “cellular metabolism” as a significant process actively chaperoned by Hsp90 (ratio of enrichment 4.35, p=8.22E-15). Specifically, “nucleotides” and “carbohydrates” metabolism were the two most represented pathways, suggesting their critical role in Hsp90-dependent DLBCL cells. To functionally validate these findings, we integrated the proteomics analysis with serum metabolomics obtained from 10 OCI-Ly7-xenografted mice treated with PU-H71 vs vehicle (75 mg/m2 for 24 h (n=5)). Bioinformatic analysis revealed that PU-H71-treated mice had significant changes in 122 metabolites including lower levels of xanthine, hypoxanthine, adenosine and inosine, suggesting an alteration of the purines metabolism. IMPDH1/2 is the enzyme catalyzing the first committed and rate-limiting step of de novo guanine nucleotide biosynthesis. To determine whether IMPDH stability depends on Hsp90 activity, we treated a panel of 6 DLBCL cell lines (including OCI-Ly7) with their GI50 for PU-H71 or 17-DMAG for up to 24 h and found a time-dependent decrease in IMPDH2 protein levels. Finally, SCID mice were xenografted with OCI-Ly1 and SU-DHL6 and treated with vehicle, PU-H71, mycophenolate mofetil (MMF, the pro-drug of the IMPDH inhibitor mycophenolic acid) and the combination of PU-H71 and MMF. We found that the combination of drugs exhibited a greater antitumor effect than each drug alone (p=0.002 for SU-DHL6 and P Altogether, our work supports a role for Hsp90 as a necessary component in a subset of DLBCLs to build-up the metabolic features that allow these tumor cells to meet the requirements for their unrestrained growth and proliferation. Disclosures No relevant conflicts of interest to declare.

Summary: Recent advances in high-throughput technology have increased the quantity of available data on protein complexes and stimulated the development of many new prediction methods. In this article, we present ProCope, a Java software suite for the prediction and evaluation of protein complexes from affinity purification experiments which integrates the major methods for calculating interaction scores and predicting protein complexes published over the last years. Methods can be accessed via a graphical user interface, command line tools and a Java API. Using ProCope, existing algorithms can be applied quickly and reproducibly on new experimental results, individual steps of the different algorithms can be combined in new and innovative ways and new methods can be implemented and integrated in the existing prediction framework. Availability: Source code and executables are available at http://www.bio.ifi.lmu.de/Complexes/ProCope/ Contact: Caroline.Friedel@bio.ifi.lmu.de

Revealing biologically relevant information from metabolomic experiments typically involves multiple data analysis steps, which are performed on different abstraction levels: primary data analysis (peak detection, metabolite identification, etc.), data preprocessing (normalization, outlier detection, etc.), biostatistic analysis (hypothesis testing, clustering, etc.), and data integration for putting the results into a larger biological context. For many of these tasks, a large pool of well-established methods exists and in various aspects we can borrow from experience on other omics technologies. However, specificities of metabolomic data such as the usually incomplete coverage of the metabolic space also require novel concepts for data analysis and interpretation. After a brief overview over typical challenges in metabolomic data interpretation, this lecture will introduce two examples for such concepts. First, the analysis of metabolite ratios as proxies for reaction constants will be discussed. Second, the reconstruction of in vivo metabolic networks from metabolomic data and the application of these networks for data integration and interpretation will be shown. Citation Format: Gabi Kastenmüller, Jan Krumsiek, Christian Gieger, Karsten Suhre. Interpretation of metabolomic data: Challenges and approaches. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr ED04-03.

daf-2 is one of the most studied mutants in C. elegans: it contains a deletion in the gene orthologue of the insulin/insulin-like growth factor (IGF) receptor. Using high resolution (1)H NMR spectroscopy, metabolomics has helped to dissect the metabolic consequences of altered daf-2 signalling. Here, we present a detailed metabolomic analysis of daf-2, using NMR spectroscopy, gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) to integrate information from different pathways. We have then used Pearson and partial correlation analysis to build networks to explore the central role of daf-2 in regulating fatty acid and amino acid metabolism. The results show the tight regulation between these two parts of the metabolome.

Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to result from a complex interplay between genetic, environmental and lifestyle factors. In order to describe the metabolic vicinity of serum urate, we analyzed 355 metabolites in 1,764 individuals of the population-based KORA F4 study and constructed a metabolite network around serum urate using Gaussian Graphical Modeling in a hypothesis-free approach. We subsequently investigated the effect of sex and urate lowering medication on all 38 metabolites assigned to the network. Within the resulting network three main clusters could be detected around urate, including the well-known pathway of purine metabolism, as well as several dipeptides, a group of essential amino acids, and a group of steroids. Of the 38 assigned metabolites, 25 showed strong differences between sexes. Association with uricostatic medication intake was not only confined to purine metabolism but seen for seven metabolites within the network. Our findings highlight pathways that are important in the regulation of serum urate and suggest that dipeptides, amino acids, and steroid hormones are playing a role in its regulation. The findings might have an impact on the development of specific targets in the treatment and prevention of hyperuricemia. Electronic supplementary material The online version of this article (doi:10.1007/s11306-013-0565-2) contains supplementary material, which is available to authorized users.

Introduction A general detrimental effect of smoking during pregnancy on the health of newborn children is well-documented, but the detailed mechanisms remain elusive. Objectives Beside the specific influence of environmental tobacco smoke derived toxicants on developmental regulation the impact on the metabolism of newborn children is of particular interest, first as a general marker of foetal development and second due to its potential predictive value for the later occurrence of metabolic diseases. Methods Tobacco smoke exposure information from a questionnaire was confirmed by measuring the smoking related metabolites S-Phenyl mercapturic acid, S-Benzyl mercapturic acid and cotinine in maternal urine by LC–MS/MS. The impact of smoking on maternal endogenous serum metabolome and children’s cord blood metabolome was assessed in a targeted analysis of 163 metabolites by an LC–MS/MS based assay. The anti-oxidative status of maternal serum samples was analysed by chemoluminiscence based method. Results Here we present for the first time results of a metabolomic assessment of the cordblood of 40 children and their mothers. Several analytes from the group of phosphatidylcholines, namely PCaaC28:1, PCaaC32:3, PCaeC30:1, PCaeC32:2, PCaeC40:1, and sphingomyelin SM C26:0, differed significantly in mothers and children’s sera depending on smoking status. In serum of smoking mothers the antioxidative capacity of water soluble compounds was not significantly changed while there was a significant decrease in the lipid fraction. Conclusion Our data give evidence that smoking during pregnancy alters both the maternal and children’s metabolome. Whether the different pattern found in adults compared to newborn children could be related to different disease outcomes should be in the focus of future studies. Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-0983-z) contains supplementary material, which is available to authorized users.

Background MicroRNA-mediated control of gene expression via translational inhibition has substantial impact on cellular regulatory mechanisms. About 37% of mammalian microRNAs appear to be located within introns of protein coding genes, linking their expression to the promoter-driven regulation of the host gene. In our study we investigate this linkage towards a relationship beyond transcriptional co-regulation. Results Using measures based on both annotation and experimental data, we show that intronic microRNAs tend to support their host genes by regulation of target gene expression with significantly correlated expression patterns. We used expression data of three differentiating cell types and compared gene expression profiles of host and target genes. Many microRNA target genes show expression patterns significantly correlated with the expressions of the microRNA host genes. By calculating functional similarities between host and predicted microRNA target genes based on GO annotations, we confirm that many microRNAs link host and target gene activity in an either synergistic or antagonistic manner. Conclusions These two regulatory effects may result from fine tuning of target gene expression functionally related to the host or knock-down of remaining opponent target gene expression. This finding allows to extend the common practice of mapping large scale gene expression data to protein associated genes with functionality of co-expressed intronic microRNAs.

Background With the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions. Results In our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination. Conclusions In summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.

The processes contributing to β cell dysfunction in type 2 diabetes (T2D) are uncertain, largely because it is difficult to access β cells in their intact immediate environment. We examined the pathophysiology of βcells under T2D progression directly in pancreatic tissues. We used MALDI imaging of Langerhans islets (LHIs) within mouse tissues or from human tissues to generate insitu-omics data, which we supported with invitro experiments. Molecular interaction networks provided information on functional pathways and molecules. We found that stearoylcarnitine accumulated in β cells, leading to arrest of insulin synthesis and energy deficiency via excessive β-oxidation and depletion of TCA cycle and oxidative phosphorylation metabolites. Acetylcarnitine and an accumulation ofN-acyl taurines, a group not previously detected in βcells, provoked insulin secretion. Thus, βcell dysfunction results from enhanced insulin secretion combined with an arrest of insulin synthesis.

Background The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems., Ministry of Education of Saxony-Anhalt (Research Center "Dynamic Systems"), German Federal Ministry of Education and Research (MaCS, Magdeburg Centre for Systems Biology), Helmholtz Alliance on Systems Biology (project CoReNe) (Initiative and Networking Fund of the Helmholtz Association)