Accelerated transsulfuration metabolically defines a discrete subclass of amyotrophic lateral sclerosis patients.

Amyotrophic lateral sclerosis is a disease characterized by progressive paralysis and death. Most ALS-cases are sporadic (sALS) and patient heterogeneity poses challenges for effective therapies. Applying metabolite profiling on 77-sALS patient-derived-fibroblasts and 43-controls, we found ~25% of sALS cases (termed sALS-1) are characterized by transsulfuration pathway upregulation, where methionine-derived-homocysteine is channeled into cysteine for glutathione synthesis. sALS-1 fibroblasts selectively exhibited a growth defect under oxidative conditions, fully-rescued by N-acetylcysteine (NAC). [U13C]-glucose tracing showed transsulfuration pathway activation with accelerated glucose flux into the Krebs cycle. We established a four-metabolite support vector machine model predicting sALS-1 metabotype with 97.5% accuracy. Both sALS-1 metabotype and growth phenotype were validated in an independent cohort of sALS cases. Importantly, plasma metabolite profiling identified a system-wide cysteine metabolism perturbation as a hallmark of sALS-1. Findings reveal that sALS patients can be stratified into distinct metabotypes with differential sensitivity to metabolic stress, providing novel insights for personalized therapy.

Methionine and Kynurenine Activate Oncogenic Kinases in Glioblastoma, and Methionine Deprivation Compromises Proliferation.

PURPOSE: We employed a metabolomics-based approach with the goal to better understand the molecular signatures of glioblastoma cells and tissues, with an aim toward identifying potential targetable biomarkers for developing more effective and novel therapies. EXPERIMENTAL DESIGN: We used liquid chromatography coupled with mass spectrometry (LC-MS/Q-TOF and LC-MS/QQQ) for the discovery and validation of metabolites from primary and established glioblastoma cells, glioblastoma tissues, and normal human astrocytes. RESULTS: We identified tryptophan, methionine, kynurenine, and 5-methylthioadenosine as differentially regulated metabolites (DRM) in glioblastoma cells compared with normal human astrocytes (NHAs). Unlike NHAs, glioblastoma cells depend on dietary methionine for proliferation, colony formation, survival, and to maintain a deregulated methylome (SAM:SAH ratio). In methylthioadenosine phosphorylase (MTAP)-deficient glioblastoma cells, expression of MTAP transgene did not alter methionine dependency, but compromised tumor growth in vivo We discovered that a lack of the kynurenine-metabolizing enzymes kynurenine monooxygenase and/or kynureninase promotes the accumulation of kynurenine, which triggers immune evasion in glioblastoma cells. In silico analysis of the identified DRMs mapped the activation of key oncogenic kinases that promotes tumorigenesis in glioblastoma. We validated this result by demonstrating that the exogenous addition of DRMs to glioblastoma cells in vitro results in oncogene activation as well as the simultaneous downregulation of Ser/Thr phosphatase PP2A. CONCLUSIONS: We have connected a four-metabolite signature, implicated in the methionine and kynurenine pathways, to the promotion and maintenance of glioblastoma. Together, our data suggest that these metabolites and their respective metabolic pathways serve as potential therapeutic targets for glioblastoma. Clin Cancer Res; 22(14); 3513-23. ©2016 AACR.

Metabolomics in toxicology and preclinical research.

Metabolomics, the comprehensive analysis of metabolites in a biological system, provides detailed information about the biochemical/physiological status of a biological system, and about the changes caused by chemicals. Metabolomics analysis is used in many fields, ranging from the analysis of the physiological status of genetically modified organisms in safety science to the evaluation of human health conditions. In toxicology, metabolomics is the -omics discipline that is most closely related to classical knowledge of disturbed biochemical pathways. It allows rapid identification of the potential targets of a hazardous compound. It can give information on target organs and often can help to improve our understanding regarding the mode-of-action of a given compound. Such insights aid the discovery of biomarkers that either indicate pathophysiological conditions or help the monitoring of the efficacy of drug therapies. The first toxicological applications of metabolomics were for mechanistic research, but different ways to use the technology in a regulatory context are being explored. Ideally, further progress in that direction will position the metabolomics approach to address the challenges of toxicology of the 21st century. To address these issues, scientists from academia, industry, and regulatory bodies came together in a workshop to discuss the current status of applied metabolomics and its potential in the safety assessment of compounds. We report here on the conclusions of three working groups addressing questions regarding 1) metabolomics for in vitro studies 2) the appropriate use of metabolomics in systems toxicology, and 3) use of metabolomics in a regulatory context.

Application of combined omics platforms to accelerate biomedical discovery in diabesity.

Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large-scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes.

Global mass spectrometry based metabolomics profiling of erythrocytes infected with Plasmodium falciparum.

Malaria is a global infectious disease that threatens the lives of millions of people. Transcriptomics, proteomics and functional genomics studies, as well as sequencing of the Plasmodium falciparum and Homo sapiens genomes, have shed new light on this host-parasite relationship. Recent advances in accurate mass measurement mass spectrometry, sophisticated data analysis software, and availability of biological pathway databases, have converged to facilitate our global, untargeted biochemical profiling study of in vitro P. falciparum-infected (IRBC) and uninfected (NRBC) erythrocytes. In order to expand the number of detectable metabolites, several key analytical steps in our workflows were optimized. Untargeted and targeted data mining resulted in detection of over one thousand features or chemical entities. Untargeted features were annotated via matching to the METLIN metabolite database. For targeted data mining, we queried the data using a compound database derived from a metabolic reconstruction of the P. falciparum genome. In total, over one hundred and fifty differential annotated metabolites were observed. To corroborate the representation of known biochemical pathways from our data, an inferential pathway analysis strategy was used to map annotated metabolites onto the BioCyc pathway collection. This hypothesis-generating approach resulted in over-representation of many metabolites onto several IRBC pathways, most prominently glycolysis. In addition, components of the "branched" TCA cycle, partial urea cycle, and nucleotide, amino acid, chorismate, sphingolipid and fatty acid metabolism were found to be altered in IRBCs. Interestingly, we detected and confirmed elevated levels for cyclic ADP ribose and phosphoribosyl AMP in IRBCs, a novel observation. These metabolites may play a role in regulating the release of intracellular Ca(2+) during P. falciparum infection. Our results support a strategy of global metabolite profiling by untargeted data acquisition. Untargeted and targeted data mining workflows, when used together to perform pathway-inferred metabolomics, have the benefit of obviating MS/MS confirmation for every detected compound.

The chromosome-centric human proteome project: a call to action.

The grand vision of the human proteome project (HPP) is moving closer to reality with the recent announcement by HUPO of the creation of the HPP consortium in charge of the development of a two-part HPP, one focused on the description of proteomes of biological samples or related to diseases (B/D-HPP) and the other dedicated to a systematic description of proteins as gene products encoded in the human genome (the C-HPP). This new initiative of HUPO seeks to identify and characterize at least one representative protein from every gene, create a protein distribution atlas and a protein pathway or network map. This vision for proteomics can be the roadmap of biological and clinical research for years to come if it delivers on its promises. The Industrial Advisory Board (IAB) to HUPO shares the visions of C-HPP. The IAB will support and critically accompany the overall project goals and the definitions of the critical milestones. The member companies are in a unique position to develop hardware and software, reagents and standards, procedures, and workflows to ensure a reliable source of tools available to the proteomics community worldwide. In collaboration with academia, the IAB member companies can and must develop the tools to reach the ambitious project goals. We offer to partner with and challenge the academic groups leading the C-HPP to define both ambitious and obtainable goals and milestones to make the C-HPP a real and trusted resource for future biology.

Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae.

Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for transcriptomics analysis. Thus, broad discovery-based data mining combined with targeted pathway projections can be an important asset for rapidly distilling, testing and evaluating a large amount of information for further investigation.

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice.

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.

Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression.

Although alterations in xenobiotic metabolism are considered causal in the development of bladder cancer, the precise mechanisms involved are poorly understood. In this study, we used high-throughput mass spectrometry to measure over 2,000 compounds in 58 clinical specimens, identifying 35 metabolites which exhibited significant changes in bladder cancer. This metabolic signature distinguished both normal and benign bladder from bladder cancer. Exploratory analyses of this metabolomic signature in urine showed promise in distinguishing bladder cancer from controls and also nonmuscle from muscle-invasive bladder cancer. Subsequent enrichment-based bioprocess mapping revealed alterations in phase I/II metabolism and suggested a possible role for DNA methylation in perturbing xenobiotic metabolism in bladder cancer. In particular, we validated tumor-associated hypermethylation in the cytochrome P450 1A1 (CYP1A1) and cytochrome P450 1B1 (CYP1B1) promoters of bladder cancer tissues by bisulfite sequence analysis and methylation-specific PCR and also by in vitro treatment of T-24 bladder cancer cell line with the DNA demethylating agent 5-aza-2'-deoxycytidine. Furthermore, we showed that expression of CYP1A1 and CYP1B1 was reduced significantly in an independent cohort of bladder cancer specimens compared with matched benign adjacent tissues. In summary, our findings identified candidate diagnostic and prognostic markers and highlighted mechanisms associated with the silencing of xenobiotic metabolism. The metabolomic signature we describe offers potential as a urinary biomarker for early detection and staging of bladder cancer, highlighting the utility of evaluating metabolomic profiles of cancer to gain insights into bioprocesses perturbed during tumor development and progression.

Improvement of peak shape in aqueous normal phase analysis of anionic metabolites.

The problem of poor peak shape for multiply charged negative-ion analytes under aqueous normal phase (ANP) conditions is investigated. Because less than adequate efficiency and symmetry can occur with a variety of mobile phases, gradients and additives, and to varying degrees depending on the instrument, sources other than solute/stationary phase interactions are more likely the cause. Since it is known that many of these compounds can interact strongly with metal ions, addition of a chelating agent to the mobile phase and/or the sample solvent was tested. In particular, ethylenediaminetetraacetic acid (EDTA) is a compound that forms strong complexes with most di-and tri-valent metal ions and can be used to verify whether trace amounts of these species are the source of the problem. In addition, the retention of a number of anionic compounds was measured at various concentrations of ammonium acetate and formate with EDTA in the mobile phase.

Metabolomic profiling reveals a role for androgen in activating amino acid metabolism and methylation in prostate cancer cells.

Prostate cancer is the second leading cause of cancer related death in American men. Development and progression of clinically localized prostate cancer is highly dependent on androgen signaling. Metastatic tumors are initially responsive to anti-androgen therapy, however become resistant to this regimen upon progression. Genomic and proteomic studies have implicated a role for androgen in regulating metabolic processes in prostate cancer. However, there have been no metabolomic profiling studies conducted thus far that have examined androgen-regulated biochemical processes in prostate cancer. Here, we have used unbiased metabolomic profiling coupled with enrichment-based bioprocess mapping to obtain insights into the biochemical alterations mediated by androgen in prostate cancer cell lines. Our findings indicate that androgen exposure results in elevation of amino acid metabolism and alteration of methylation potential in prostate cancer cells. Further, metabolic phenotyping studies confirm higher flux through pathways associated with amino acid metabolism in prostate cancer cells treated with androgen. These findings provide insight into the potential biochemical processes regulated by androgen signaling in prostate cancer. Clinically, if validated, these pathways could be exploited to develop therapeutic strategies that supplement current androgen ablative treatments while the observed androgen-regulated metabolic signatures could be employed as biomarkers that presage the development of castrate-resistant prostate cancer.

Metabolomics of Mycobacterium tuberculosis reveals compartmentalized co-catabolism of carbon substrates.

Metabolic adaptation to the host environment is a defining feature of the pathogenicity of Mycobacterium tuberculosis (Mtb), but we lack biochemical knowledge of its metabolic networks. Many bacteria use catabolite repression as a regulatory mechanism to maximize growth by consuming individual carbon substrates in a preferred sequence and growing with diauxic kinetics. Surprisingly, untargeted metabolite profiling of Mtb growing on ¹³C-labeled carbon substrates revealed that Mtb could catabolize multiple carbon sources simultaneously to achieve enhanced monophasic growth. Moreover, when co-catabolizing multiple carbon sources, Mtb differentially catabolized each carbon source through the glycolytic, pentose phosphate, and/or tricarboxylic acid pathways to distinct metabolic fates. This unusual topologic organization of bacterial intermediary metabolism has not been previously observed and may subserve the pathogenicity of Mtb.

Activity-based metabolomic profiling of enzymatic function: identification of Rv1248c as a mycobacterial 2-hydroxy-3-oxoadipate synthase.

Activity based metabolomic profiling (ABMP) allows unbiased discovery of enzymatic activities encoded by genes of unknown function, and applies liquid-chromatography mass spectrometry (LC-MS) to analyze the impact of a recombinant enzyme on the homologous cellular extract as a physiologic library of potential substrates and products. The Mycobacterium tuberculosis protein Rv1248c was incompletely characterized as a thiamine diphosphate-dependent alpha-ketoglutarate decarboxylase. Here, recombinant Rv1248c catalyzed consumption of alpha-ketoglutarate in a mycobacterial small molecule extract with matched production of 5-hydroxylevulinate (HLA) in a reaction predicted to require glyoxylate. As confirmed using pure substrates by LC-MS, (1)H-NMR, chemical trapping, and intracellular metabolite profiling, Rv1248c catalyzes C-C bond formation between the activated aldehyde of alpha-ketoglutarate and the carbonyl of glyoxylate to yield 2-hydroxy-3-oxoadipate (HOA), which decomposes to HLA. Thus, Rv1248c encodes an HOA synthase.

Aqueous normal phase retention of nucleotides on silica hydride-based columns: method development strategies for analytes relevant in clinical analysis.

An aqueous normal phase HPLC method coupled with UV or ESI/MS detection was used for the determination of a wide variety of nucleotides, essential in metabolomics studies. Fifteen nucleotides were tested in clinically relevant mixtures at levels of 100 microg/mL for UV detection and 1 microg/mL for ESI-MS detection. Analysis times for all protocols developed were less than 20 min. The chromatographic conditions were changed to achieve optimized retention and separation of the nucleotides studied. The aqueous normal phase-HPLC methods were developed utilizing two columns, one having a minimally modified hydride surface another having an undecanoic acid moiety on a hydride surface. Volatile, low ionic strength mobile phases were used. Negative ion mode ESI-MS at near neutral pH mobile phase, combined with a TOF detector provided a highly sensitive and specific method, which is equally suitable for quadrupole and ion trap instruments.

Analysis of hydrophilic metabolites in physiological fluids by HPLC-MS using a silica hydride-based stationary phase.

Aqueous normal-phase chromatography was used for the analysis of metabolites in human saliva and urine samples. The column was packed with a silica hydride type separation material. Several gradients were tested with different mobile phase additives in order to produce retention for amino acids, small organic acids, and carbohydrates. Detection was done by TOF MS. In some cases the relative concentration levels of various metabolites in human saliva were compared for normal patients and patients with pancreatic cancer or pancreatitis. The reproducibility of retention of individual metabolites in these complex matrices was tested for several compounds.

Suitability of silica hydride stationary phase, aqueous normal phase chromatography for untargeted metabolomic profiling of Enterococcus faecium and Staphylococcus aureus.

We report the robustness of silica hydride stationary phase, aqueous normal phase (ANP) chromatography to the chemical complexity of the intracellular metabolomes of Staphylococcus aureus and Enterococcus faecium. We specifically demonstrate that the chromatographic behavior of known metabolites is unaffected by the intracellular chemical matrix of these microbes and that this method enables untargeted profiling of their intracellular metabolites using accurate mass-retention time (AMRT) identifiers. We further demonstrate the ability of AMRT-based metabolite profiling to differentiate bacteria along genetic and phenotypic lines. Overall, these data commend the utility of ANP-based chromatography for untargeted metabolomics-based studies of microbial physiology and antibiotic resistance.

Analysis of hydrophilic metabolites by high-performance liquid chromatography-mass spectrometry using a silica hydride-based stationary phase.

A novel silica hydride-based stationary phase was used to evaluate the retention behavior in the aqueous normal-phase (ANP) mode of standards representing three classes of metabolites. The effects on retention behavior of amino acids, carbohydrates and small organic acids were examined by altering the column temperature, and by adding different additives to both the mobile phase and sample solvent. Gradient mode results revealed the repeatability of retention times to be very stable for these compound classes. At both 15 and 30 degrees C, excellent RSD values were obtained with less than 1% variation for over 50 injections of an amino acid mixture. The ability to separate the 19 nonderivatized amino acid standards, organic acids and carbohydrates was demonstrated as well as the potential for this material to separate polar metabolites in complex fluids such as urine.

A sample extraction and chromatographic strategy for increasing LC/MS detection coverage of the erythrocyte metabolome.

Reproducible and comprehensive sample extraction and detection of metabolites with a broad range of physico-chemical properties from biological matrices can be a highly challenging process. A single LC/MS separation method was developed for a 2.1 mm x 100 mm, 1.8 microm ZORBAX SB-Aq column that was used to separate human erythrocyte metabolites extracted under sample extraction solvent conditions where the pH was neutral or had been adjusted to either, pH 2, 6 or 9. Internal standards were included and evaluated for tracking sample extraction efficiency. Through the combination of electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) techniques in both positive (+) and negative (-) ion modes, a total of 2370 features (compounds and associated compound related components: isotopes, adducts and dimers) were detected across all pHs. Broader coverage of the detected metabolome was achieved by observing that (1) performing extractions at pH 2 and 9, leads to a combined 92% increase in detected features over pH 7 alone; and (2) including APCI in the analysis results in a 34% increase in detected features, across all pHs, than the total number detected by ESI. A significant dependency of extraction solvent pH on the recovery of heme and other compounds was observed in erythrocytes and underscores the need for a comprehensive sample extraction strategy and LC/MS analysis in metabolomics profiling experiments.

Effect of organic mobile phase composition on signal responses for selected polyalkene additive compounds by liquid chromatography-mass spectrometry.

The high performance liquid chromatography (HPLC) separation methodology employed in the study of polyalkene additive compounds by atmospheric pressure ionization mass spectrometry (API-MS) was undertaken. Both atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) were examined. APPI (including dopant-assisted APPI) was found to be an inferior ionization technique to APCI in all cases. APCI ion responses were found to be highly dependent upon the organic solvent type used in the HPLC separations. Namely, employing a water/methanol gradient in place of a water/acetonitrile or a water/acetone gradient yielded improvements in analyte ion intensities between 2.3- and 52-fold for the liquid chromatography-mass spectrometry (LC-MS) experiments. Analyte and mobile phase solvent ionization energies were found to be only partially responsible, whereas mobile phase cluster formation and hydration was also implicated. Mobile phase component modification is demonstrated to be an important consideration when developing new, or modifying existing HPLC separations for use in LC-MS experiments in order to enhance analyte sensitivity for a wide variety of common polyalkene additives.

Molecular formula and METLIN Personal Metabolite Database matching applied to the identification of compounds generated by LC/TOF-MS.

In an effort to simplify and streamline compound identification from metabolomics data generated by liquid chromatography time-of-flight mass spectrometry, we have created software for constructing Personalized Metabolite Databases with content from over 15,000 compounds pulled from the public METLIN database (http://metlin.scripps.edu/). Moreover, we have added extra functionalities to the database that (a) permit the addition of user-defined retention times as an orthogonal searchable parameter to complement accurate mass data; and (b) allow interfacing to separate software, a Molecular Formula Generator (MFG), that facilitates reliable interpretation of any database matches from the accurate mass spectral data. To test the utility of this identification strategy, we added retention times to a subset of masses in this database, representing a mixture of 78 synthetic urine standards. The synthetic mixture was analyzed and screened against this METLIN urine database, resulting in 46 accurate mass and retention time matches. Human urine samples were subsequently analyzed under the same analytical conditions and screened against this database. A total of 1387 ions were detected in human urine; 16 of these ions matched both accurate mass and retention time parameters for the 78 urine standards in the database. Another 374 had only an accurate mass match to the database, with 163 of those masses also having the highest MFG score. Furthermore, MFG calculated a formula for a further 849 ions that had no match to the database. Taken together, these results suggest that the METLIN Personal Metabolite database and MFG software offer a robust strategy for confirming the formula of database matches. In the event of no database match, it also suggests possible formulas that may be helpful in interpreting the experimental results.