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Puchades-Carrasco L, Palomino-Schätzlein M, Pérez-Rambla C, Pineda-Lucena A. Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers. Brief Bioinform 2015; 17:541-52. [PMID: 26342127 DOI: 10.1093/bib/bbv077] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Indexed: 12/29/2022] Open
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152
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Holmes E, Wijeyesekera A, Taylor-Robinson SD, Nicholson JK. The promise of metabolic phenotyping in gastroenterology and hepatology. Nat Rev Gastroenterol Hepatol 2015. [PMID: 26194948 DOI: 10.1038/nrgastro.2015.114] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.
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Affiliation(s)
- Elaine Holmes
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Anisha Wijeyesekera
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | | | - Jeremy K Nicholson
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
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153
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Liesenfeld DB, Habermann N, Toth R, Owen RW, Frei E, Staffa J, Schrotz-King P, Klika KD, Ulrich CM. Changes in urinary metabolic profiles of colorectal cancer patients enrolled in a prospective cohort study (ColoCare). Metabolomics 2015; 11:998-1012. [PMID: 29250455 PMCID: PMC5730072 DOI: 10.1007/s11306-014-0758-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Metabolomics is a valuable tool for biomarker screening of colorectal cancer (CRC). In this study, we profiled the urinary metabolomes of patients enrolled in a prospective patient cohort (ColoCare). We aimed to describe changes in the metabolome in the longer clinical follow-up and describe initial predictors as candidate markers with possibly prognostic significance. METHODS In total, 199 urine samples from CRC patients pre-surgery (n=97), 1-8 days post-surgery (n=12) and then after 6 and 12 months (n=52 and 38, respectively) were analyzed using both GC-MS and 1H-NMR. Both datasets were analyzed separately with built in uni- and multivariate analyses of Metaboanalyst 2.0. Furthermore, adjusted linear mixed effects regression models were constructed. RESULTS Many concentrations of the metabolites derived from the gut microbiome were affected by CRC surgery, presumably indicating a tumor-induced shift in bacterial species. Associations of the microbial metabolites with disease stage indicate an important role of the gut microbiome in CRC.We were able to differentiate the metabolite profiles of CRC patients prior to surgery from those at any post-surgery timepoint using a multivariate model containing 20 marker metabolites (AUCROC=0.89; 95% CI:0.84-0.95). CONCLUSION To the best of our knowledge, this is one of the first metabolomic studies to follow CRC patients in a prospective setting with repeated urine sampling over time. We were able to confirm markers initially identified in case-control studies and pin point metabolites which may serve as candidates for prognostic biomarkers of CRC.
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Affiliation(s)
- David B. Liesenfeld
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Nina Habermann
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Reka Toth
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Robert W. Owen
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Eva Frei
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Jürgen Staffa
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Petra Schrotz-King
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
| | - Karel D. Klika
- Genomics and Proteomics Core Facility, Molecular Structure Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cornelia M. Ulrich
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany and German Consortium for Translational Cancer Research (DKTK)
- Fred Hutchinson Cancer Research Center (FHCRC), Seattle, Washington
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154
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Affiliation(s)
- Robert A Quinn
- Department of Biology, San Diego State University, San Diego, CA, 92182, USA,
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155
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Daghir-Wojtkowiak E, Struck-Lewicka W, Waszczuk-Jankowska M, Markuszewski M, Kaliszan R, Markuszewski MJ. Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer. Biomark Med 2015; 9:577-95. [DOI: 10.2217/bmm.15.20] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Aim: We aimed at evaluation the potential diagnostic role of urinary nucleosides in urogenital tract cancer. Materials & methods: Concentrations of 12 nucleosides determined by LC-MS/MS were subjected to correlation, association and interaction analyses. Results: We identified six pairs of nucleosides differently correlated in the group of patients and controls (p < 0.05). N-2-methylguanosine (odds ratio: 4.82; 95% CI: 1.78–12.93; p = 0.002) and N,N-dimethylguanosine (odds ratio: 5.45; 95% CI: 1.78–16.44; p = 0.003), were significantly associated with the disease risk (p-corrected = 0.004). Interaction between N-2-methylguanosine and adenosine (p-interaction = 0.019) suggested their multiplicative effect on the outcome. Conclusion: Urinary nucleosides, namely N,N-dimethylguanosine and N-2-methylguanosine may have the potential to serve as prognostic biomarkers. Gender-specific differences in urogenital tract cancer are likely to occur.
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Affiliation(s)
- Emilia Daghir-Wojtkowiak
- Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
- Department of Toxicology, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, M. Curie-Sklodowskiej 9, 85-094 Bydgoszcz, Poland
| | - Wiktoria Struck-Lewicka
- Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Malgorzata Waszczuk-Jankowska
- Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Marcin Markuszewski
- Department of Urology, Medical University of Gdańsk, Smoluchowskiego 17, 80–214 Gdańsk, Poland
| | - Roman Kaliszan
- Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Michal Jan Markuszewski
- Department of Biopharmaceutics & Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
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156
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Fahrmann J, Grapov D, Yang J, Hammock B, Fiehn O, Bell GI, Hara M. Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia. Am J Physiol Endocrinol Metab 2015; 308:E978-89. [PMID: 25852003 PMCID: PMC4451288 DOI: 10.1152/ajpendo.00019.2015] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 04/01/2015] [Indexed: 11/22/2022]
Abstract
Nonobese diabetic (NOD) mice are a commonly used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra-high-performance liquid chromatography-accurate mass quadruple time-of-flight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spectrometry-based methodologies, was used to capture metabolic alterations in the metabolome and lipidome of plasma from NOD mice progressing or not progressing to T1D. Using this multi-platform approach, we identified >1,000 circulating lipids and metabolites in male and female progressor and nonprogressor animals (n = 71). Statistical and multivariate analyses were used to identify age- and sex-independent metabolic markers, which best differentiated metabolic profiles of progressors and nonprogressors. Key T1D-associated perturbations were related with 1) increases in oxidation products glucono-δ-lactone and galactonic acid and reductions in cysteine, methionine and threonic acid, suggesting increased oxidative stress; 2) reductions in circulating polyunsaturated fatty acids and lipid signaling mediators, most notably arachidonic acid (AA) and AA-derived eicosanoids, implying impaired states of systemic inflammation; 3) elevations in circulating triacylglyercides reflective of hypertriglyceridemia; and 4) reductions in major structural lipids, most notably lysophosphatidylcholines and phosphatidylcholines. Taken together, our results highlight the systemic perturbations that accompany a loss of glycemic control and development of overt T1D.
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Affiliation(s)
- Johannes Fahrmann
- National Institutes of Health West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Dmitry Grapov
- National Institutes of Health West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Jun Yang
- Department of Entomology and Cancer Center, University of California Davis, Davis, California; and
| | - Bruce Hammock
- Department of Entomology and Cancer Center, University of California Davis, Davis, California; and
| | - Oliver Fiehn
- National Institutes of Health West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Graeme I Bell
- Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Manami Hara
- Department of Medicine, The University of Chicago, Chicago, Illinois
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157
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Meissen JK, Hirahatake KM, Adams SH, Fiehn O. Temporal metabolomic responses of cultured HepG2 liver cells to high fructose and high glucose exposures. Metabolomics 2015; 11:707-721. [PMID: 26190955 PMCID: PMC4504739 DOI: 10.1007/s11306-014-0729-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
High fructose consumption has been implicated with deleterious effects on human health, including hyperlipidemia elicited through de novo lipogenesis. However, more global effects of fructose on cellular metabolism have not been elucidated. In order to explore the metabolic impact of fructose-containing nutrients, we applied both GC-TOF and HILIC-QTOF mass spectrometry metabolomic strategies using extracts from cultured HepG2 cells exposed to fructose, glucose, or fructose + glucose. Cellular responses were analyzed in a time-dependent manner, incubated in media containing 5.5 mM glucose + 5.0 mM fructose in comparison to controls incubated in media containing either 5.5 mM glucose or 10.5 mM glucose. Mass spectrometry identified 156 unique known metabolites and a large number of unknown compounds, which revealed metabolite changes due to both utilization of fructose and high-carbohydrate loads independent of hexose structure. Fructose was shown to be partially converted to sorbitol, and generated higher levels of fructose-1-phosphate as a precursor for glycolytic intermediates. Differentially regulated ratios of 3-phosphoglycerate to serine pathway intermediates in high fructose media indicated a diversion of carbon backbones away from energy metabolism. Additionally, high fructose conditions changed levels of complex lipids toward phosphatidylethanolamines. Patterns of acylcarnitines in response to high hexose exposure (10.5 mM glucose or glucose/fructose combination) suggested a reduction in mitochondrial beta-oxidation.
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Affiliation(s)
- John K. Meissen
- UC Davis Genome Center, University of California Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
- West Coast Metabolomics Center, University of California Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
| | - Kristin M. Hirahatake
- Department of Nutrition, University of California Davis, One Shields Avenue., Davis, CA 95616, USA
- Obesity and Metabolism Research Unit, USDA-Agricultural Research Service Western Human Nutrition Research Center, 430 W. Health Sciences Dr., Davis, CA 95616, USA
| | - Sean H. Adams
- Department of Nutrition, University of California Davis, One Shields Avenue., Davis, CA 95616, USA
- Obesity and Metabolism Research Unit, USDA-Agricultural Research Service Western Human Nutrition Research Center, 430 W. Health Sciences Dr., Davis, CA 95616, USA
| | - Oliver Fiehn
- UC Davis Genome Center, University of California Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
- West Coast Metabolomics Center, University of California Davis, 451 Health Sciences Dr., Davis, CA 95616, USA
- To whom correspondence should be addressed: Oliver Fiehn, 451 Health Sciences Dr., Davis, CA 95616, Tel: +1-530-754-8258, Fax: +1-530-754-9658,
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158
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Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry. Metabolites 2015; 5:192-210. [PMID: 25859693 PMCID: PMC4495369 DOI: 10.3390/metabo5020192] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 03/24/2015] [Accepted: 04/01/2015] [Indexed: 12/21/2022] Open
Abstract
Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.
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159
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Grapov D, Wanichthanarak K, Fiehn O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics 2015; 31:2757-60. [PMID: 25847005 DOI: 10.1093/bioinformatics/btv194] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 03/30/2015] [Indexed: 02/06/2023] Open
Abstract
UNLABELLED Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. AVAILABILITY AND IMPLEMENTATION Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. CONTACT ofiehn@ucdavis.edu.
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Affiliation(s)
- Dmitry Grapov
- National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and
| | - Kwanjeera Wanichthanarak
- National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and
| | - Oliver Fiehn
- National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and King Abdulaziz University, Biochemistry Department, Jeddah, Saudi Arabia
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160
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Grapov D, Fahrmann J, Hwang J, Poudel A, Jo J, Periwal V, Fiehn O, Hara M. Diabetes Associated Metabolomic Perturbations in NOD Mice. Metabolomics 2015; 11:425-437. [PMID: 25755629 PMCID: PMC4351755 DOI: 10.1007/s11306-014-0706-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Non-obese diabetic (NOD) mice are a widely-used model oftype1 diabetes (T1D). However, not all animals develop overt diabetes. This study examined the circulating metabolomic profiles of NOD mice progressing or not progressing to T1D. Total beta-cell mass was quantified in the intact pancreas using transgenic NOD mice expressinggreen fluorescent protein under the control of mouse insulin I promoter.While both progressor and non-progressor animals displayed lymphocyte infiltration and endoplasmic reticulum stress in the pancreas tissue;overt T1D did not develop until animals lost ~70% of the total beta-cell mass.Gas chromatography time of flight mass spectrometry (GC-TOF) was used to measure >470 circulating metabolites in male and female progressor and non-progressor animals (n=76) across a wide range of ages (neonates to >40-wk).Statistical and multivariate analyses were used to identify age and sex independent metabolic markers which best differentiated progressor and non-progressor animals' metabolic profiles. Key T1D-associated perturbations were related with: (1) increased plasma glucose and reduced 1,5-anhydroglucitol markers of glycemic control; (2) increased allantoin, gluconic acid and nitric oxide-derived saccharic acid markers of oxidative stress; (3) reduced lysine, an insulin secretagogue; (4) increased branched-chain amino acids, isoleucine and valine; (5) reduced unsaturated fatty acids including arachidonic acid; and (6)perturbations in urea cycle intermediates suggesting increased arginine-dependent NO synthesis. Together these findings highlight the strength of the unique approach of comparing progressor and non-progressor NOD mice to identify metabolic perturbations involved in T1D progression.
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Affiliation(s)
- Dmitry Grapov
- NIH West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Johannes Fahrmann
- NIH West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Jessica Hwang
- Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Ananta Poudel
- Department of Medicine, The University of Chicago, Chicago, Illinois
| | - Junghyo Jo
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Vipul Periwal
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, California
| | - Manami Hara
- Department of Medicine, The University of Chicago, Chicago, Illinois
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161
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Lo YC, Senese S, Li CM, Hu Q, Huang Y, Damoiseaux R, Torres JZ. Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. PLoS Comput Biol 2015; 11:e1004153. [PMID: 25826798 PMCID: PMC4380459 DOI: 10.1371/journal.pcbi.1004153] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 01/26/2015] [Indexed: 01/17/2023] Open
Abstract
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). Determining the targets of compounds identified in cell-based high-throughput chemical screens is a critical step for downstream drug development and understanding of compound mechanism of action. However, current computational target prediction approaches like chemical similarity database searches are limited to single or sequential ligand analyses, which limits their ability to accurately deconvolve a large number of compounds that often have chemically diverse structures. Here, we have developed a new computational drug target prediction method, called CSNAP that is based on chemical similarity networks. By clustering diverse chemical structures into distinct sub-networks corresponding to chemotypes, we show that CSNAP improves target prediction accuracy and consistency over a board range of drug classes. We further coupled CSNAP to a mitotic database and successfully determined the major mitotic drug targets of a diverse compound set identified in a cell-based chemical screen. We demonstrate that CSNAP can easily integrate with diverse knowledge-based databases for on/off target prediction and post-target validation, thus broadening its applicability for identifying the targets of bioactive compounds from a wide range of chemical screens.
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Affiliation(s)
- Yu-Chen Lo
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
- Program in Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Silvia Senese
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Chien-Ming Li
- Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America
| | - Qiyang Hu
- Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Yong Huang
- Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America
| | - Robert Damoiseaux
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Jorge Z. Torres
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail:
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162
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Bouhifd M, Andersen ME, Baghdikian C, Boekelheide K, Crofton KM, Fornace AJ, Kleensang A, Li H, Livi C, Maertens A, McMullen PD, Rosenberg M, Thomas R, Vantangoli M, Yager JD, Zhao L, Hartung T. The human toxome project. ALTEX 2015; 32:112-24. [PMID: 25742299 PMCID: PMC4778566 DOI: 10.14573/altex.1502091] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 03/02/2015] [Indexed: 12/26/2022]
Abstract
The Human Toxome Project, funded as an NIH Transformative Research grant 2011-2016, is focused on developing the concepts and the means for deducing, validating and sharing molecular pathways of toxicity (PoT). Using the test case of estrogenic endocrine disruption, the responses of MCF-7 human breast cancer cells are being phenotyped by transcriptomics and mass-spectroscopy-based metabolomics. The bioinformatics tools for PoT deduction represent a core deliverable. A number of challenges for quality and standardization of cell systems, omics technologies and bioinformatics are being addressed. In parallel, concepts for annotation, validation and sharing of PoT information, as well as their link to adverse outcomes, are being developed. A reasonably comprehensive public database of PoT, the Human Toxome Knowledge-base, could become a point of reference for toxicological research and regulatory test strategies.
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Affiliation(s)
- Mounir Bouhifd
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Baltimore, MD, USA
| | | | - Christina Baghdikian
- ASPPH Fellow, National Center for Computational Toxicology, US EPA, Research Triangle Park, NC, USA
| | - Kim Boekelheide
- Brown University, Pathology & Laboratory Medicine, Providence, RI, USA
| | - Kevin M. Crofton
- US EPA, National Center for Computational Toxicology, Research Triangle Park, NC, USA
| | | | - Andre Kleensang
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Baltimore, MD, USA
| | - Henghong Li
- Georgetown University Medical Center, Washington, DC, USA
| | | | - Alexandra Maertens
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Baltimore, MD, USA
| | | | | | - Russell Thomas
- US EPA, National Center for Computational Toxicology, Research Triangle Park, NC, USA
| | | | - James D. Yager
- Johns Hopkins Bloomberg School of Public Health, Department of Environmental Health Sciences, Baltimore, MD, USA
| | - Liang Zhao
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Baltimore, MD, USA
| | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing, Baltimore, MD, USA
- University of Konstanz, Center for Alternatives to Animal Testing Europe, Konstanz, Germany
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163
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Abstract
Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.
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Affiliation(s)
- Kelli M Sas
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | | | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
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164
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Wikoff WR, Grapov D, Fahrmann JF, DeFelice B, Rom WN, Pass HI, Kim K, Nguyen U, Taylor SL, Gandara DR, Kelly K, Fiehn O, Miyamoto S. Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prev Res (Phila) 2015; 8:410-8. [PMID: 25657018 DOI: 10.1158/1940-6207.capr-14-0329] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 01/29/2015] [Indexed: 12/25/2022]
Abstract
Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.
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Affiliation(s)
- William R Wikoff
- University of California, Davis Genome Center, Davis, California
| | - Dmitry Grapov
- University of California, Davis Genome Center, Davis, California
| | | | - Brian DeFelice
- University of California, Davis Genome Center, Davis, California
| | - William N Rom
- Division of Pulmonary, Critical Care and Sleep, New York University, School of Medicine New York, New York
| | - Harvey I Pass
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, New York
| | - Kyoungmi Kim
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, California
| | - UyenThao Nguyen
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, California
| | - Sandra L Taylor
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, California
| | - David R Gandara
- University of California, Davis Genome Center, Davis, California. Division of Pulmonary, Critical Care and Sleep, New York University, School of Medicine New York, New York. Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, New York. Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, California. Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California. Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Karen Kelly
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California
| | - Oliver Fiehn
- University of California, Davis Genome Center, Davis, California. Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Suzanne Miyamoto
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California.
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165
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Minamoto Y, Otoni CC, Steelman SM, Büyükleblebici O, Steiner JM, Jergens AE, Suchodolski JS. Alteration of the fecal microbiota and serum metabolite profiles in dogs with idiopathic inflammatory bowel disease. Gut Microbes 2015; 6:33-47. [PMID: 25531678 PMCID: PMC4615558 DOI: 10.1080/19490976.2014.997612] [Citation(s) in RCA: 220] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Idiopathic inflammatory bowel disease (IBD) is a common cause of chronic gastrointestinal (GI) disease in dogs. The combination of an underlying host genetic susceptibility, an intestinal dysbiosis, and dietary/environmental factors are suspected as main contributing factors in the pathogenesis of canine IBD. However, actual mechanisms of the host-microbe interactions remain elusive. The aim of this study was to compare the fecal microbiota and serum metabolite profiles between healthy dogs (n = 10) and dogs with IBD before and after 3 weeks of medical therapy (n = 12). Fecal microbiota and metabolite profiles were characterized by 454-pyrosequencing of 16 S rRNA genes and by an untargeted metabolomics approach, respectively. Significantly lower bacterial diversity and distinct microbial communities were observed in dogs with IBD compared to the healthy control dogs. While Gammaproteobacteria were overrepresented, Erysipelotrichia, Clostridia, and Bacteroidia were underrepresented in dogs with IBD. The functional gene content was predicted from the 16 S rRNA gene data using PICRUSt, and revealed overrepresented bacterial secretion system and transcription factors, and underrepresented amino acid metabolism in dogs with IBD. The serum metabolites 3-hydroxybutyrate, hexuronic acid, ribose, and gluconic acid lactone were significantly more abundant in dogs with IBD. Although a clinical improvement was observed after medical therapy in all dogs with IBD, this was not accompanied by significant changes in the fecal microbiota or in serum metabolite profiles. These results suggest the presence of oxidative stress and a functional alteration of the GI microbiota in dogs with IBD, which persisted even in the face of a clinical response to medical therapy.
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Key Words
- 16 S rRNA, 16 S ribosomal RNA
- ANOSIM, analysis of similarities
- CIBDAI, canine IBD activity index
- FDR, false discovery rate
- Faecalibacterium
- GC-TOF/MS, gas chromatography coupled with time-of-flight mass spectrometry
- GI, gastrointestinal
- IBD
- IBD, idiopathic inflammatory bowel disease
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- LEfSe, linear discriminant analysis (LDA) effect size
- PCA, principal component analysis
- PCoA, principal coordinates analysis
- PICRUSt, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States
- ROC, receiver operating characteristic
- dog
- dysbiosis
- feces
- metabolomics
- microbiome
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Affiliation(s)
- Yasushi Minamoto
- Gastrointestinal Laboratory; Department of Small Animal Clinical Sciences; College of Veterinary Medicine and Biomedical Sciences; Texas A&M University; College Station, TX USA
| | - Cristiane C Otoni
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Iowa State University; Ames, IA USA
| | - Samantha M Steelman
- Department of Veterinary Integrative Biosciences; College of Veterinary Medicine and Biomedical Sciences; Texas A&M University; College Station, TX USA
| | - Olga Büyükleblebici
- Department of Biochemistry; College of Veterinary Medicine; Aksaray University; Aksaray, Turkey
| | - Jörg M Steiner
- Gastrointestinal Laboratory; Department of Small Animal Clinical Sciences; College of Veterinary Medicine and Biomedical Sciences; Texas A&M University; College Station, TX USA
| | - Albert E Jergens
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Iowa State University; Ames, IA USA
| | - Jan S Suchodolski
- Gastrointestinal Laboratory; Department of Small Animal Clinical Sciences; College of Veterinary Medicine and Biomedical Sciences; Texas A&M University; College Station, TX USA,Correspondence to: Jan S. Suchodolski;
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166
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Affiliation(s)
- Caroline H. Johnson
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Julijana Ivanisevic
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - H. Paul Benton
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Gary Siuzdak
- Scripps
Center for Metabolomics and Mass Spectrometry, The Scripps Research Institute, La Jolla, California 92037, United States
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167
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Vaz FM, Pras-Raves M, Bootsma AH, van Kampen AHC. Principles and practice of lipidomics. J Inherit Metab Dis 2015; 38:41-52. [PMID: 25409862 DOI: 10.1007/s10545-014-9792-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 10/24/2014] [Accepted: 11/03/2014] [Indexed: 01/16/2023]
Abstract
The technical advances in mass spectrometry, particularly the development of (ultra)-high-resolution/mass accuracy measurement capabilities in combination with refinement of soft ionization techniques, have increased the application and success of lipidomics to answer biological questions in relation to lipid metabolism. Together with other omics technologies, lipidomics has become an important tool to practice systems biology as lipids comprise a very significant part of the metabolome and play pleiotropic roles in cellular functions. As an increasing number of disorders are linked to lipid metabolism, lipidomics is used to search for biomarkers, understand disease mechanism and follow the efficacy of therapeutic options. This review provides a first introduction to the major methodological strategies currently used for mass spectrometry-based lipidomics and associated data pre-processing and analysis.
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Affiliation(s)
- Frédéric M Vaz
- Laboratory Genetic Metabolic Disease (F0-224), Departments of Clinical Chemistry and Pediatrics, University of Amsterdam, Academic Medical Center (AMC), Amsterdam, 1105 AZ, The Netherlands,
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168
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Armstrong AW, Wu J, Johnson MA, Grapov D, Azizi B, Dhillon J, Fiehn O. Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis. F1000Res 2014; 3:248. [PMID: 25580230 PMCID: PMC4288418 DOI: 10.12688/f1000research.4709.1] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/16/2014] [Indexed: 01/10/2023] Open
Abstract
Importance: While “omics” studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology. Objective: We sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls. Design: We employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls. Setting: Study participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States. Participants: We examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry. Results: Multivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88; Control: 209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980; Control: 7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000; Control: 111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250; Control: 347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02). Conclusions and relevance: The metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development.
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Affiliation(s)
- April W Armstrong
- Department of Dermatology, University of Colorado Denver, Aurora, CO, 12801, USA
| | - Julie Wu
- Department of Dermatology, University of Colorado Denver, Aurora, CO, 12801, USA
| | - Mary Ann Johnson
- Department of Dermatology, University of California Davis, Sacramento, CA, 95816, USA
| | - Dmitry Grapov
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
| | - Baktazh Azizi
- Department of Dermatology, University of Colorado Denver, Aurora, CO, 12801, USA
| | - Jaskaran Dhillon
- Department of Dermatology, University of Colorado Denver, Aurora, CO, 12801, USA
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
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169
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Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A. The blood exposome and its role in discovering causes of disease. ENVIRONMENTAL HEALTH PERSPECTIVES 2014; 122:769-74. [PMID: 24659601 PMCID: PMC4123034 DOI: 10.1289/ehp.1308015] [Citation(s) in RCA: 235] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 03/20/2014] [Indexed: 05/18/2023]
Abstract
BACKGROUND Since 2001, researchers have examined the human genome (G) mainly to discover causes of disease, despite evidence that G explains relatively little risk. We posit that unexplained disease risks are caused by the exposome (E; representing all exposures) and G × E interactions. Thus, etiologic research has been hampered by scientists' continuing reliance on low-tech methods to characterize E compared with high-tech omics for characterizing G. OBJECTIVES Because exposures are inherently chemical in nature and arise from both endogenous and exogenous sources, blood specimens can be used to characterize exposomes. To explore the "blood exposome" and its connection to disease, we sought human blood concentrations of many chemicals, along with their sources, evidence of chronic-disease risks, and numbers of metabolic pathways. METHODS From the literature we obtained human blood concentrations of 1,561 small molecules and metals derived from foods, drugs, pollutants, and endogenous processes. We mapped chemical similarities after weighting by blood concentrations, disease-risk citations, and numbers of human metabolic pathways. RESULTS Blood concentrations spanned 11 orders of magnitude and were indistinguishable for endogenous and food chemicals and drugs, whereas those of pollutants were 1,000 times lower. Chemical similarities mapped by disease risks were equally distributed by source categories, but those mapped by metabolic pathways were dominated by endogenous molecules and essential nutrients. CONCLUSIONS For studies of disease etiology, the complexity of human exposures motivates characterization of the blood exposome, which includes all biologically active chemicals. Because most small molecules in blood are not human metabolites, investigations of causal pathways should expand beyond the endogenous metabolome.
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Affiliation(s)
- Stephen M Rappaport
- Center for Exposure Biology, School of Public Health, University of California, Berkeley, Berkeley, California, USA
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170
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Duren W, Weymouth T, Hull T, Omenn GS, Athey B, Burant C, Karnovsky A. MetDisease--connecting metabolites to diseases via literature. ACTA ACUST UNITED AC 2014; 30:2239-41. [PMID: 24713438 DOI: 10.1093/bioinformatics/btu179] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
MOTIVATION In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks. AVAILABILITY AND IMPLEMENTATION MetDisease can be downloaded from http://apps.cytoscape.org/apps/metdisease or installed via the Cytoscape app manager. Further information about MetDisease can be found at http://metdisease.ncibi.org CONTACT akarnovs@med.umich.edu SUPPLEMENTARY INFORMATION Supplementary Data are available at Bioinformatics online.
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Affiliation(s)
- William Duren
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Terry Weymouth
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tim Hull
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brian Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Charles Burant
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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171
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Xu YJ, Wang C, Ho WE, Ong CN. Recent developments and applications of metabolomics in microbiological investigations. Trends Analyt Chem 2014. [DOI: 10.1016/j.trac.2013.12.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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172
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Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk. Biotechnol Lett 2014; 36:1461-8. [DOI: 10.1007/s10529-014-1508-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 03/03/2014] [Indexed: 11/25/2022]
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173
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Steelman SM, Johnson P, Jackson A, Schulze J, Chowdhary BP. Serum metabolomics identifies citrulline as a predictor of adverse outcomes in an equine model of gut-derived sepsis. Physiol Genomics 2014; 46:339-47. [PMID: 24619519 DOI: 10.1152/physiolgenomics.00180.2013] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Acute laminitis is an inflammatory disease of the equine foot that often occurs secondarily to sepsis or systemic inflammation associated with gastrointestinal disease. It has been suggested that laminitis is similar to multiple organ dysfunction syndrome in humans, although in horses the weight-bearing laminar epithelium of the foot appears to be the tissue most sensitive to insult and the first "organ" to fail. Metabolomics performed on serum samples collected before (Con) and after (Lmn) experimental induction of gastrointestinal-associated sepsis in six horses detected 1,177 metabolites of both mammalian and bacterial origin in equine serum. Network and correlation analyses suggested a dysregulation of fatty acid metabolism in the Lmn group, as well as an accumulation of organic acids such as lactate. Furthermore, concentrations of the amino acid citrulline were decreased in Lmn samples from all study animals, suggesting that citrulline might be useful as a biomarker to identify critically ill animals that are at risk of developing laminitis. We therefore established normal ranges of plasma citrulline concentrations in a separate group of horses (n = 36) and tested the ability of citrulline to predict adverse outcomes (laminitis or death) in critically ill horses (n = 23). Plasma citrulline was significantly lower in critically ill horses that went on to experience adverse outcomes (n = 6). Further study is required to accurately determine a diagnostic cutoff, but the present data are suggestive of the predictive value of citrulline as a biomarker for laminar failure in equine sepsis.
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Affiliation(s)
- Samantha M Steelman
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas;
| | - Philip Johnson
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, Missouri; and
| | - Amy Jackson
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas
| | | | - Bhanu P Chowdhary
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas
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174
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Ernst M, Silva DB, Silva RR, Vêncio RZN, Lopes NP. Mass spectrometry in plant metabolomics strategies: from analytical platforms to data acquisition and processing. Nat Prod Rep 2014; 31:784-806. [DOI: 10.1039/c3np70086k] [Citation(s) in RCA: 129] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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175
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Application of “Omics” Technologies to In Vitro Toxicology. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/978-1-4939-0521-8_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2022]
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176
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Barupal DK, Lee SJ, Karoly ED, Adhya S. Inactivation of metabolic genes causes short- and long-range dys-regulation in Escherichia coli metabolic network. PLoS One 2013; 8:e78360. [PMID: 24363806 PMCID: PMC3868466 DOI: 10.1371/journal.pone.0078360] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 09/19/2013] [Indexed: 01/01/2023] Open
Abstract
The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research.
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Affiliation(s)
- Dinesh Kumar Barupal
- Genome Center, University of California Davis, Davis, California, United States of America
| | - Sang Jun Lee
- Infection and Immunity Research Center, KRIBB and University of Science and Technology, Daejeon, Korea
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Edward D. Karoly
- Metabolon, Inc., Durham, North Carolina, United States of America
| | - Sankar Adhya
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
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177
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Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules. Proc Natl Acad Sci U S A 2013; 110:E4407-16. [PMID: 24191063 DOI: 10.1073/pnas.1315492110] [Citation(s) in RCA: 93] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Glycosyl groups are an essential mediator of molecular interactions in cells and on cellular surfaces. There are very few methods that directly relate sugar-containing molecules to their biosynthetic machineries. Here, we introduce glycogenomics as an experiment-guided genome-mining approach for fast characterization of glycosylated natural products (GNPs) and their biosynthetic pathways from genome-sequenced microbes by targeting glycosyl groups in microbial metabolomes. Microbial GNPs consist of aglycone and glycosyl structure groups in which the sugar unit(s) are often critical for the GNP's bioactivity, e.g., by promoting binding to a target biomolecule. GNPs are a structurally diverse class of molecules with important pharmaceutical and agrochemical applications. Herein, O- and N-glycosyl groups are characterized in their sugar monomers by tandem mass spectrometry (MS) and matched to corresponding glycosylation genes in secondary metabolic pathways by a MS-glycogenetic code. The associated aglycone biosynthetic genes of the GNP genotype then classify the natural product to further guide structure elucidation. We highlight the glycogenomic strategy by the characterization of several bioactive glycosylated molecules and their gene clusters, including the anticancer agent cinerubin B from Streptomyces sp. SPB74 and an antibiotic, arenimycin B, from Salinispora arenicola CNB-527.
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178
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Ramautar R, Berger R, van der Greef J, Hankemeier T. Human metabolomics: strategies to understand biology. Curr Opin Chem Biol 2013; 17:841-6. [PMID: 23849548 DOI: 10.1016/j.cbpa.2013.06.015] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Revised: 06/14/2013] [Accepted: 06/14/2013] [Indexed: 12/21/2022]
Abstract
Metabolomics provides a direct functional read-out of the physiological status of an organism and is in principle ideally suited to describe someone's health status. Whereas only a limited number of small metabolites are used in the clinics, in inborn errors of metabolism an extensive repertoire of metabolites are used as biomarkers. We discuss that the proper clinical phenotyping is crucial to find biomarkers and obtain biological insights for multifactorial diseases. This requires to study the phenotype dynamics including the concepts of homeostasis and allostasis, that is, the ability to adapt and cope with a challenge. We also elaborate that biology-driven metabolomics platforms (i.e. development of metabolomics technology driven by the need of studying and answering important biomedical questions) addressing clinically relevant pathways and at the same time providing absolute concentrations are key to allow discovery and validation of biomarkers across studies and labs. Following individuals over years will require high throughput metabolomics approaches, which are emerging for nuclear magnetic resonance spectroscopy and direct-infusion mass spectrometry, but should also include the biochemical networks needed for personalized health monitoring.
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Affiliation(s)
- Rawi Ramautar
- Leiden Academic Center for Drug Research, Division of Analytical Biosciences, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands; The Netherlands Metabolomics Centre, Einsteinweg 55, 2333 CC Leiden, The Netherlands
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179
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Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS One 2013; 8:e57639. [PMID: 23536766 PMCID: PMC3594230 DOI: 10.1371/journal.pone.0057639] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2012] [Accepted: 01/28/2013] [Indexed: 02/06/2023] Open
Abstract
Antihypertensive drugs are among the most commonly prescribed drugs for chronic disease worldwide. The response to antihypertensive drugs varies substantially between individuals and important factors such as race that contribute to this heterogeneity are poorly understood. In this study we use metabolomics, a global biochemical approach to investigate biochemical changes induced by the beta-adrenergic receptor blocker atenolol in Caucasians and African Americans. Plasma from individuals treated with atenolol was collected at baseline (untreated) and after a 9 week treatment period and analyzed using a GC-TOF metabolomics platform. The metabolomic signature of atenolol exposure included saturated (palmitic), monounsaturated (oleic, palmitoleic) and polyunsaturated (arachidonic, linoleic) free fatty acids, which decreased in Caucasians after treatment but were not different in African Americans (p<0.0005, q<0.03). Similarly, the ketone body 3-hydroxybutyrate was significantly decreased in Caucasians by 33% (p<0.0001, q<0.0001) but was unchanged in African Americans. The contribution of genetic variation in genes that encode lipases to the racial differences in atenolol-induced changes in fatty acids was examined. SNP rs9652472 in LIPC was found to be associated with the change in oleic acid in Caucasians (p<0.0005) but not African Americans, whereas the PLA2G4C SNP rs7250148 associated with oleic acid change in African Americans (p<0.0001) but not Caucasians. Together, these data indicate that atenolol-induced changes in the metabolome are dependent on race and genotype. This study represents a first step of a pharmacometabolomic approach to phenotype patients with hypertension and gain mechanistic insights into racial variability in changes that occur with atenolol treatment, which may influence response to the drug.
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Booth SC, Weljie AM, Turner RJ. Computational tools for the secondary analysis of metabolomics experiments. Comput Struct Biotechnol J 2013; 4:e201301003. [PMID: 24688685 PMCID: PMC3962093 DOI: 10.5936/csbj.201301003] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2012] [Revised: 12/17/2012] [Accepted: 12/24/2012] [Indexed: 01/30/2023] Open
Abstract
Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.
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Affiliation(s)
- Sean C Booth
- Department of Biological Sciences, University of Calgary, Calgary, AB. 2500 University Dr. NW, Calgary, Alberta, T2N 1N4, Canada
| | - Aalim M Weljie
- Department of Pharmacology, University of Pennsylvania, Philadelphia, United States
| | - Raymond J Turner
- Department of Biological Sciences, University of Calgary, Calgary, AB. 2500 University Dr. NW, Calgary, Alberta, T2N 1N4, Canada
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Zhou B, Xiao JF, Ressom HW. Prioritization of putative metabolite identifications in LC-MS/MS experiments using a computational pipeline. Proteomics 2013; 13:248-60. [PMID: 23307777 DOI: 10.1002/pmic.201200306] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Revised: 10/14/2012] [Accepted: 10/24/2012] [Indexed: 01/12/2023]
Abstract
One of the major bottle-necks in current LC-MS-based metabolomic investigations is metabolite identification. An often-used approach is to first look up metabolites from databases through peak mass, followed by verification of the obtained putative identifications using MS/MS data. However, the mass-based search may provide inappropriate putative identifications when the observed peak is from isotopes, fragments, or adducts. In addition, a large fraction of peaks is often left with multiple putative identifications. To differentiate these putative identifications, manual verification of metabolites through comparison between biological samples and authentic compounds is necessary. However, such experiments are laborious, especially when multiple putative identifications are encountered. It is desirable to use computational approaches to obtain more reliable putative identifications and prioritize them before performing experimental verification of the metabolites. In this article, a computational pipeline is proposed to assist metabolite identification with improved metabolome coverage and prioritization capability. Multiple publicly available software tools and databases, along with in-house developed algorithms, are utilized to fully exploit the information acquired from LC-MS/MS experiments. The pipeline is successfully applied to identify metabolites on the basis of LC-MS as well as MS/MS data. Using accurate masses, retention time values, MS/MS spectra, and metabolic pathways/networks, more appropriate putative identifications are retrieved and prioritized to guide subsequent metabolite verification experiments.
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Affiliation(s)
- Bin Zhou
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
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182
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Fukushima A, Kusano M. Recent progress in the development of metabolome databases for plant systems biology. FRONTIERS IN PLANT SCIENCE 2013; 4:73. [PMID: 23577015 PMCID: PMC3616245 DOI: 10.3389/fpls.2013.00073] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Accepted: 03/15/2013] [Indexed: 05/19/2023]
Abstract
Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.
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Affiliation(s)
- Atsushi Fukushima
- RIKEN Plant Science CenterYokohama, Kanagawa, Japan
- *Correspondence: Atsushi Fukushima, RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. e-mail:
| | - Miyako Kusano
- RIKEN Plant Science CenterYokohama, Kanagawa, Japan
- Department of Genome System Sciences, Graduate School of Nanobioscience, Kihara Institute for Biological ResearchYokohama, Kanagawa, Japan
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183
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Meissen JK, Yuen BTK, Kind T, Riggs JW, Barupal DK, Knoepfler PS, Fiehn O. Induced pluripotent stem cells show metabolomic differences to embryonic stem cells in polyunsaturated phosphatidylcholines and primary metabolism. PLoS One 2012; 7:e46770. [PMID: 23077522 PMCID: PMC3471894 DOI: 10.1371/journal.pone.0046770] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Accepted: 09/05/2012] [Indexed: 12/18/2022] Open
Abstract
Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses. Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS), gas chromatography-time of flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine the metabolome of induced pluripotent stem cells (iPSCs) compared to parental fibroblasts as well as to reference embryonic stem cells (ESCs). With over 250 identified metabolites and a range of structurally unknown compounds, quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids. However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanolamine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis. Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and induction of pluripotency.
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Affiliation(s)
- John K. Meissen
- University of California Davis Genome Center, University of California Davis, Davis, California, United States of America
| | - Benjamin T. K. Yuen
- Department of Cell Biology and Human Anatomy, University of California Davis School of Medicine, Davis, California, United States of America
| | - Tobias Kind
- University of California Davis Genome Center, University of California Davis, Davis, California, United States of America
| | - John W. Riggs
- Department of Cell Biology and Human Anatomy, University of California Davis School of Medicine, Davis, California, United States of America
| | - Dinesh K. Barupal
- University of California Davis Genome Center, University of California Davis, Davis, California, United States of America
| | - Paul S. Knoepfler
- University of California Davis Genome Center, University of California Davis, Davis, California, United States of America
- Department of Cell Biology and Human Anatomy, University of California Davis School of Medicine, Davis, California, United States of America
- * E-mail: (PSK); (OF)
| | - Oliver Fiehn
- University of California Davis Genome Center, University of California Davis, Davis, California, United States of America
- * E-mail: (PSK); (OF)
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