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Marco-Ramell A, Palau-Rodriguez M, Alay A, Tulipani S, Urpi-Sarda M, Sanchez-Pla A, Andres-Lacueva C. Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinformatics 2018; 19:1. [PMID: 29291722 PMCID: PMC5749025 DOI: 10.1186/s12859-017-2006-0] [Citation(s) in RCA: 148] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 12/18/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. RESULTS An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. CONCLUSIONS We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.
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Affiliation(s)
- Anna Marco-Ramell
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain
- CIBER Fragilidad y Envejecimiento Saludable [CIBERfes], Instituto de Salud Carlos III [ISCIII], Madrid, Spain
| | - Magali Palau-Rodriguez
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain
- CIBER Fragilidad y Envejecimiento Saludable [CIBERfes], Instituto de Salud Carlos III [ISCIII], Madrid, Spain
| | - Ania Alay
- Genetics, Microbiology and Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain
| | - Sara Tulipani
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain
| | - Mireia Urpi-Sarda
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain
- CIBER Fragilidad y Envejecimiento Saludable [CIBERfes], Instituto de Salud Carlos III [ISCIII], Madrid, Spain
| | - Alex Sanchez-Pla
- Genetics, Microbiology and Statistics Department, Biology Faculty, University of Barcelona, Barcelona, Spain
- Statistics and Bioinformatics Unit, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain
| | - Cristina Andres-Lacueva
- Biomarkers & Nutrimetabolomics Laboratory, Nutrition, Food Science and Gastronomy Department, Food Technology Reference Net (XaRTA), Nutrition and Food Safety Research Institute (INSA-UB), Faculty of Pharmacy and Food Sciences, Pharmacy and Food Science Faculty, University of Barcelona, Barcelona, Spain
- CIBER Fragilidad y Envejecimiento Saludable [CIBERfes], Instituto de Salud Carlos III [ISCIII], Madrid, Spain
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Bowler RP, Wendt CH, Fessler MB, Foster MW, Kelly RS, Lasky-Su J, Rogers AJ, Stringer KA, Winston BW. New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2017; 14:1721-1743. [PMID: 29192815 PMCID: PMC5946579 DOI: 10.1513/annalsats.201710-770ws] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.
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Guitton Y, Tremblay-Franco M, Le Corguillé G, Martin JF, Pétéra M, Roger-Mele P, Delabrière A, Goulitquer S, Monsoor M, Duperier C, Canlet C, Servien R, Tardivel P, Caron C, Giacomoni F, Thévenot EA. Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics. Int J Biochem Cell Biol 2017; 93:89-101. [DOI: 10.1016/j.biocel.2017.07.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Revised: 06/14/2017] [Accepted: 07/10/2017] [Indexed: 12/11/2022]
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Metabolomics applied to diabetes-lessons from human population studies. Int J Biochem Cell Biol 2017; 93:136-147. [PMID: 29074437 DOI: 10.1016/j.biocel.2017.10.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 09/30/2017] [Accepted: 10/20/2017] [Indexed: 02/08/2023]
Abstract
The 'classical' distribution of type 2 diabetes (T2D) across the globe is rapidly changing and it is no longer predominantly a disease of middle-aged/elderly adults of western countries, but it is becoming more common through Asia and the Middle East, as well as increasingly found in younger individuals. This global altered incidence of T2D is most likely associated with the spread of western diets and sedentary lifestyles, although there is still much debate as to whether the increased incidence rates are due to an overconsumption of fats, sugars or more generally high-calorie foods. In this context, understanding the interactions between genes of risk and diet and how they influence the incidence of T2D will help define the causative pathways of the disease. This review focuses on the use of metabolomics in large cohort studies to follow the incidence of type 2 diabetes in different populations. Such approaches have been used to identify new biomarkers of pre-diabetes, such as branch chain amino acids, and associate metabolomic profiles with genes of known risk in T2D from large scale GWAS studies. As the field develops, there are also examples of meta-analysis across metabolomics cohort studies and cross-comparisons with different populations to allow us to understand how genes and diet contribute to disease risk. Such approaches demonstrate that insulin resistance and T2D have far reaching metabolic effects beyond raised blood glucose and how the disease impacts systemic metabolism.
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Burgess KEV, Borutzki Y, Rankin N, Daly R, Jourdan F. MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1071:68-74. [PMID: 29030098 PMCID: PMC5726607 DOI: 10.1016/j.jchromb.2017.08.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 08/07/2017] [Accepted: 08/13/2017] [Indexed: 01/19/2023]
Abstract
An update to the ab-initio network construction tool MetaNetter has been produced. The tool creates networks of masses from high resolution mass spectrometry data. The new plugin provides both chemical transformation and adduct mapping. Tables mapping adduct and transform counts across samples can be generated. Retention time windows are supported for both adduct and transform network generation.
Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships. We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis.
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Affiliation(s)
- K E V Burgess
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.
| | - Y Borutzki
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - N Rankin
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - R Daly
- Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom
| | - F Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
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Chazalviel M, Frainay C, Poupin N, Vinson F, Merlet B, Gloaguen Y, Cottret L, Jourdan F. MetExploreViz: web component for interactive metabolic network visualization. Bioinformatics 2017; 34:312-313. [PMID: 28968733 PMCID: PMC5860210 DOI: 10.1093/bioinformatics/btx588] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 08/04/2017] [Accepted: 09/14/2017] [Indexed: 12/29/2022] Open
Abstract
Summary MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc/
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Affiliation(s)
- Maxime Chazalviel
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France.,MedDay Pharmaceuticals, Paris, France
| | - Clément Frainay
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Nathalie Poupin
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Florence Vinson
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Benjamin Merlet
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Yoann Gloaguen
- Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Gascube Campus, Bearsden, UK
| | - Ludovic Cottret
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | - Fabien Jourdan
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
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Meier R, Ruttkies C, Treutler H, Neumann S. Bioinformatics can boost metabolomics research. J Biotechnol 2017; 261:137-141. [PMID: 28554829 DOI: 10.1016/j.jbiotec.2017.05.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 05/10/2017] [Accepted: 05/16/2017] [Indexed: 11/18/2022]
Abstract
Metabolomics is the modern term for the field of small molecule research in biology and biochemistry. Currently, metabolomics is undergoing a transition where the classic analytical chemistry is combined with modern cheminformatics and bioinformatics methods, paving the way for large-scale data analysis. We give some background on past developments, highlight current state-of-the-art approaches, and give a perspective on future requirements.
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Affiliation(s)
- René Meier
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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59
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Kikuchi J, Yamada S. NMR window of molecular complexity showing homeostasis in superorganisms. Analyst 2017; 142:4161-4172. [DOI: 10.1039/c7an01019b] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.
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Affiliation(s)
- Jun Kikuchi
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
| | - Shunji Yamada
- RIKEN Center for Sustainable Resource Science
- Yokohama
- Japan
- Graduate School of Bioagricultural Sciences
- Nagoya University
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60
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Casbas Pinto F, Ravipati S, Barrett DA, Hodgman TC. A methodology for elucidating regulatory mechanisms leading to changes in lipid profiles. Metabolomics 2017; 13:81. [PMID: 28596719 PMCID: PMC5447331 DOI: 10.1007/s11306-017-1214-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 05/03/2017] [Indexed: 11/24/2022]
Abstract
INTRODUCTION It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations. OBJECTIVES This work simplifies this process. METHODS A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids. RESULTS The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1-an mRNA stabiliser. CONCLUSION This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.
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Affiliation(s)
- Ferran Casbas Pinto
- School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD UK
- Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD UK
| | - Srinivarao Ravipati
- Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD UK
| | - David A. Barrett
- Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD UK
| | - T. Charles Hodgman
- School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD UK
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Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis. Metabolites 2016; 6:metabo6040047. [PMID: 28009836 PMCID: PMC5192453 DOI: 10.3390/metabo6040047] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 12/03/2016] [Accepted: 12/13/2016] [Indexed: 12/29/2022] Open
Abstract
Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate.
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Wu H, von Kamp A, Leoncikas V, Mori W, Sahin N, Gevorgyan A, Linley C, Grabowski M, Mannan AA, Stoy N, Stewart GR, Ward LT, Lewis DJM, Sroka J, Matsuno H, Klamt S, Westerhoff HV, McFadden J, Plant NJ, Kierzek AM. MUFINS: multi-formalism interaction network simulator. NPJ Syst Biol Appl 2016; 2:16032. [PMID: 28725480 PMCID: PMC5516860 DOI: 10.1038/npjsba.2016.32] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 07/27/2016] [Accepted: 08/29/2016] [Indexed: 12/19/2022] Open
Abstract
Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.
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Affiliation(s)
- Huihai Wu
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Vytautas Leoncikas
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Wataru Mori
- Graduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan
| | - Nilgun Sahin
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
| | | | - Catherine Linley
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Marek Grabowski
- Institute of Informatics, University of Warsaw, Warsaw, Poland
| | - Ahmad A Mannan
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Nicholas Stoy
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Graham R Stewart
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Lara T Ward
- Oncology DMPK, AstraZeneca, Alderley Park, Cheshire, UK
| | - David J M Lewis
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Jacek Sroka
- Institute of Informatics, University of Warsaw, Warsaw, Poland
| | - Hiroshi Matsuno
- Graduate School of Science and Engineering and Faculty of Science, Yamaguchi University, Yoshida, Yamaguchi, Japan
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Hans V Westerhoff
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK
- Synthetic Systems Biology, Netherlands Institute for Systems Biology, University of Amsterdam, Amsterdam, The Netherlands
| | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Nicholas J Plant
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Andrzej M Kierzek
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
- Simcyp Limited (a Certara Company), Sheffield, UK
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A Resource Allocation Trade-Off between Virulence and Proliferation Drives Metabolic Versatility in the Plant Pathogen Ralstonia solanacearum. PLoS Pathog 2016; 12:e1005939. [PMID: 27732672 PMCID: PMC5061431 DOI: 10.1371/journal.ppat.1005939] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 09/17/2016] [Indexed: 11/19/2022] Open
Abstract
Bacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments. Metabolic versatility is a critical element for pathogen’s virulence and their ability to survive in the host. Beyond the necessity to collect resources during infection, pathogens face a resource allocation dilemma: they have to use nutritional resources to proliferate inside the host, and in the other hand they need to mobilize matter and energy for the production of essential virulence factors. In this study, we provide evidence of that such a trade-off constrains antagonistically bacterial proliferation and virulence in the bacterial plant pathogen Ralstonia solanacearum. We determined the energetic cost required by R. solanacearum to produce and secrete exopolysaccharide, which is a major virulence factor required for wilting symptom appearance. We validated this result by showing that bacterial mutants defective for exopolysaccharide production or other virulence factor indeed have an increased growth rate compared to the wild-type strain. We provide evidence that this trade-off mechanism is orchestrated by the phcA master regulatory gene, which directly connects quorum-sensing regulation to metabolic versatility and virulence. Our results also support the view that R. solanacearum specializes towards a restricted number of substrates used during in planta growth.
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Aurich MK, Fleming RMT, Thiele I. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models. Front Physiol 2016; 7:327. [PMID: 27536246 PMCID: PMC4971542 DOI: 10.3389/fphys.2016.00327] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 07/18/2016] [Indexed: 11/13/2022] Open
Abstract
Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.
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Affiliation(s)
- Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
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Julien-Laferrière A, Bulteau L, Parrot D, Marchetti-Spaccamela A, Stougie L, Vinga S, Mary A, Sagot MF. A Combinatorial Algorithm for Microbial Consortia Synthetic Design. Sci Rep 2016; 6:29182. [PMID: 27373593 PMCID: PMC4931573 DOI: 10.1038/srep29182] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/07/2016] [Indexed: 11/16/2022] Open
Abstract
Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.
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Affiliation(s)
- Alice Julien-Laferrière
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Laurent Bulteau
- Université Paris-Est, LIGM (UMR 8049), CNRS, UPEM, ESIEE Paris, ENPC, F-77454, Marne-la-Vallée, France
| | - Delphine Parrot
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Alberto Marchetti-Spaccamela
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- Sapienza University of Rome, Italy
| | - Leen Stougie
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- VU University and CWI, Amsterdam, The Netherlands
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
| | - Arnaud Mary
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
| | - Marie-France Sagot
- Erable team, INRIA Grenoble Rhône-Alpes, 655 avenue de I’Europe, 38330 Montbonnot-Saint-Martin, France
- University Lyon 1, CNRS UMR 5558, F-69622 Villeurbanne, France
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Jardinaud MF, Boivin S, Rodde N, Catrice O, Kisiala A, Lepage A, Moreau S, Roux B, Cottret L, Sallet E, Brault M, Emery RJN, Gouzy J, Frugier F, Gamas P. A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis. PLANT PHYSIOLOGY 2016; 171:2256-76. [PMID: 27217496 PMCID: PMC4936592 DOI: 10.1104/pp.16.00711] [Citation(s) in RCA: 86] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 05/18/2016] [Indexed: 05/19/2023]
Abstract
Nod factors (NFs) are lipochitooligosaccharidic signal molecules produced by rhizobia, which play a key role in the rhizobium-legume symbiotic interaction. In this study, we analyzed the gene expression reprogramming induced by purified NF (4 and 24 h of treatment) in the root epidermis of the model legume Medicago truncatula Tissue-specific transcriptome analysis was achieved by laser-capture microdissection coupled to high-depth RNA sequencing. The expression of 17,191 genes was detected in the epidermis, among which 1,070 were found to be regulated by NF addition, including previously characterized NF-induced marker genes. Many genes exhibited strong levels of transcriptional activation, sometimes only transiently at 4 h, indicating highly dynamic regulation. Expression reprogramming affected a variety of cellular processes, including perception, signaling, regulation of gene expression, as well as cell wall, cytoskeleton, transport, metabolism, and defense, with numerous NF-induced genes never identified before. Strikingly, early epidermal activation of cytokinin (CK) pathways was indicated, based on the induction of CK metabolic and signaling genes, including the CRE1 receptor essential to promote nodulation. These transcriptional activations were independently validated using promoter:β-glucuronidase fusions with the MtCRE1 CK receptor gene and a CK response reporter (TWO COMPONENT SIGNALING SENSOR NEW). A CK pretreatment reduced the NF induction of the EARLY NODULIN11 (ENOD11) symbiotic marker, while a CK-degrading enzyme (CYTOKININ OXIDASE/DEHYDROGENASE3) ectopically expressed in the root epidermis led to increased NF induction of ENOD11 and nodulation. Therefore, CK may play both positive and negative roles in M. truncatula nodulation.
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Affiliation(s)
- Marie-Françoise Jardinaud
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Stéphane Boivin
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Nathalie Rodde
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Olivier Catrice
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Anna Kisiala
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Agnes Lepage
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Sandra Moreau
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Brice Roux
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Ludovic Cottret
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Erika Sallet
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Mathias Brault
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - R J Neil Emery
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Jérôme Gouzy
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Florian Frugier
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
| | - Pascal Gamas
- LIPM, Université de Toulouse, Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique, 31326 Castanet-Tolosan, France (M.-F.J., N.R., O.C., A.L., S.M., B.R., L.C., E.S., J.G., P.G.);INPT-Université de Toulouse, ENSAT, 31326 Castanet-Tolosan, France (M.-F.J.);Institute of Plant Sciences-Paris Saclay University, Centre National de la Recherche Scientifique/Institut National de la Recherche Agronomique/Universités Paris-Sud/Paris-Diderot/d'Evry, 91190 Gif-sur-Yvette, France (S.B., M.B., F.F.);Biology Department, Trent University, Peterborough, Ontario, Canada K9J 7B8 (A.K., R.J.N.E.); andDepartment of Plant Genetics, Physiology, and Biotechnology, University of Technology and Life Sciences, 85-789 Bydgoszcz, Poland (A.K.)
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Baa-Puyoulet P, Parisot N, Febvay G, Huerta-Cepas J, Vellozo AF, Gabaldón T, Calevro F, Charles H, Colella S. ArthropodaCyc: a CycADS powered collection of BioCyc databases to analyse and compare metabolism of arthropods. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw081. [PMID: 27242037 PMCID: PMC5630938 DOI: 10.1093/database/baw081] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 04/25/2016] [Indexed: 01/25/2023]
Abstract
Arthropods interact with humans at different levels with highly beneficial roles (e.g. as pollinators), as well as with a negative impact for example as vectors of human or animal diseases, or as agricultural pests. Several arthropod genomes are available at present and many others will be sequenced in the near future in the context of the i5K initiative, offering opportunities for reconstructing, modelling and comparing their metabolic networks. In-depth analysis of these genomic data through metabolism reconstruction is expected to contribute to a better understanding of the biology of arthropods, thereby allowing the development of new strategies to control harmful species. In this context, we present here ArthropodaCyc, a dedicated BioCyc collection of databases using the Cyc annotation database system (CycADS), allowing researchers to perform reliable metabolism comparisons of fully sequenced arthropods genomes. Since the annotation quality is a key factor when performing such global genome comparisons, all proteins from the genomes included in the ArthropodaCyc database were re-annotated using several annotation tools and orthology information. All functional/domain annotation results and their sources were integrated in the databases for user access. Currently, ArthropodaCyc offers a centralized repository of metabolic pathways, protein sequence domains, Gene Ontology annotations as well as evolutionary information for 28 arthropod species. Such database collection allows metabolism analysis both with integrated tools and through extraction of data in formats suitable for systems biology studies. Database URL:http://arthropodacyc.cycadsys.org/
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Affiliation(s)
| | - Nicolas Parisot
- Univ Lyon, INSA-Lyon, INRA, BF2I, UMR0203, F-69621, Villeurbanne, France
| | - Gérard Febvay
- Univ Lyon, INSA-Lyon, INRA, BF2I, UMR0203, F-69621, Villeurbanne, France
| | - Jaime Huerta-Cepas
- Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Augusto F Vellozo
- Univ Lyon, Univ Lyon1, CNRS, LBBE, UMR5558, F-69622, Villeurbanne, France
| | - Toni Gabaldón
- Centre for Genomic Regulation (CRG), the Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Federica Calevro
- Univ Lyon, INSA-Lyon, INRA, BF2I, UMR0203, F-69621, Villeurbanne, France
| | - Hubert Charles
- Univ Lyon, INSA-Lyon, INRA, BF2I, UMR0203, F-69621, Villeurbanne, France
| | - Stefano Colella
- Univ Lyon, INSA-Lyon, INRA, BF2I, UMR0203, F-69621, Villeurbanne, France
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Zalko D, Soto AM, Canlet C, Tremblay-Franco M, Jourdan F, Cabaton NJ. Bisphenol A Exposure Disrupts Neurotransmitters Through Modulation of Transaminase Activity in the Brain of Rodents. Endocrinology 2016; 157:1736-9. [PMID: 27149041 PMCID: PMC4870873 DOI: 10.1210/en.2016-1207] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Daniel Zalko
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
| | - Ana M Soto
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
| | - Cecile Canlet
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
| | - Marie Tremblay-Franco
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
| | - Fabien Jourdan
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
| | - Nicolas J Cabaton
- Toxalim (D.Z., C.C., M.T.-F., F.J., N.J.C.), Université de Toulouse, INRA (Institut National de la Recherche Agronomique), 31027, Toulouse, France; and Department of Integrative Physiology and Pathobiology (A.M.S.), Tufts University School of Medicine, Boston, Massachusetts 02111
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Gupta M, Prasad Y, Sharma SK, Jain CK. Identification of Phosphoribosyl-AMP cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis. J Biomol Struct Dyn 2016; 35:287-299. [DOI: 10.1080/07391102.2015.1137229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Money Gupta
- Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh 201307, India
| | - Yamuna Prasad
- Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Sanjeev Kumar Sharma
- Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh 201307, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh 201307, India
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Abstract
Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.
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71
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Merlet B, Paulhe N, Vinson F, Frainay C, Chazalviel M, Poupin N, Gloaguen Y, Giacomoni F, Jourdan F. A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks. Front Mol Biosci 2016; 3:2. [PMID: 26909353 PMCID: PMC4754433 DOI: 10.3389/fmolb.2016.00002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 01/25/2016] [Indexed: 11/13/2022] Open
Abstract
This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.
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Affiliation(s)
- Benjamin Merlet
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
| | - Nils Paulhe
- Nutrition Humaine, Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique, Centre Clermont-Ferrand–Theix, UMR 1019Saint-Genès-Champanelle, France
| | - Florence Vinson
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
| | - Clément Frainay
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
| | - Maxime Chazalviel
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
| | - Nathalie Poupin
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
| | - Yoann Gloaguen
- Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of GlasgowGlasgow, UK
| | - Franck Giacomoni
- Nutrition Humaine, Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique, Centre Clermont-Ferrand–Theix, UMR 1019Saint-Genès-Champanelle, France
| | - Fabien Jourdan
- TOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de ToulouseToulouse, France
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Frainay C, Jourdan F. Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief Bioinform 2016; 18:43-56. [PMID: 26822099 DOI: 10.1093/bib/bbv115] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 12/16/2015] [Indexed: 11/13/2022] Open
Abstract
Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.
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73
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Tremblay-Franco M, Cabaton NJ, Canlet C, Gautier R, Schaeberle CM, Jourdan F, Sonnenschein C, Vinson F, Soto AM, Zalko D. Dynamic Metabolic Disruption in Rats Perinatally Exposed to Low Doses of Bisphenol-A. PLoS One 2015; 10:e0141698. [PMID: 26517871 PMCID: PMC4627775 DOI: 10.1371/journal.pone.0141698] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Accepted: 10/12/2015] [Indexed: 11/19/2022] Open
Abstract
Along with the well-established effects on fertility and fecundity, perinatal exposure to endocrine disrupting chemicals, and notably to xeno-estrogens, is strongly suspected of modulating general metabolism. The metabolism of a perinatally exposed individual may be durably altered leading to a higher susceptibility of developing metabolic disorders such as obesity and diabetes; however, experimental designs involving the long term study of these dynamic changes in the metabolome raise novel challenges. 1H-NMR-based metabolomics was applied to study the effects of bisphenol-A (BPA, 0; 0.25; 2.5, 25 and 250 μg/kg BW/day) in rats exposed perinatally. Serum and liver samples of exposed animals were analyzed on days 21, 50, 90, 140 and 200 in order to explore whether maternal exposure to BPA alters metabolism. Partial Least Squares-Discriminant Analysis (PLS-DA) was independently applied to each time point, demonstrating a significant pair-wise discrimination for liver as well as serum samples at all time-points, and highlighting unequivocal metabolic shifts in rats perinatally exposed to BPA, including those exposed to lower doses. In BPA exposed animals, metabolism of glucose, lactate and fatty acids was modified over time. To further explore dynamic variation, ANOVA-Simultaneous Component Analysis (A-SCA) was used to separate data into blocks corresponding to the different sources of variation (Time, Dose and Time*Dose interaction). A-SCA enabled the demonstration of a dynamic, time/age dependent shift of serum metabolome throughout the rats’ lifetimes. Variables responsible for the discrimination between groups clearly indicate that BPA modulates energy metabolism, and suggest alterations of neurotransmitter signaling, the latter finding being compatible with the neurodevelopmental effect of this xenoestrogen. In conclusion, long lasting metabolic effects of BPA could be characterized over 200 days, despite physiological (and thus metabolic) changes connected with sexual maturation and aging.
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Affiliation(s)
- Marie Tremblay-Franco
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Nicolas J. Cabaton
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Cécile Canlet
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Roselyne Gautier
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Cheryl M. Schaeberle
- Department of Integrative Physiology & Pathobiology, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Fabien Jourdan
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Carlos Sonnenschein
- Department of Integrative Physiology & Pathobiology, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Florence Vinson
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
| | - Ana M. Soto
- Department of Integrative Physiology & Pathobiology, Tufts University School of Medicine, Boston, Massachusetts, United States of America
| | - Daniel Zalko
- UMR1331, TOXALIM, Research Centre in Food Toxicology, Institut National de la Recherche Agronomique, INRA, Université de Toulouse, Toulouse, France
- * E-mail:
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Rao Q, Rollat-Farnier PA, Zhu DT, Santos-Garcia D, Silva FJ, Moya A, Latorre A, Klein CC, Vavre F, Sagot MF, Liu SS, Mouton L, Wang XW. Genome reduction and potential metabolic complementation of the dual endosymbionts in the whitefly Bemisia tabaci. BMC Genomics 2015; 16:226. [PMID: 25887812 PMCID: PMC4438442 DOI: 10.1186/s12864-015-1379-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 02/21/2015] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The whitefly Bemisia tabaci is an important agricultural pest with global distribution. This phloem-sap feeder harbors a primary symbiont, "Candidatus Portiera aleyrodidarum", which compensates for the deficient nutritional composition of its food sources, and a variety of secondary symbionts. Interestingly, all of these secondary symbionts are found in co-localization with the primary symbiont within the same bacteriocytes, which should favor the evolution of strong interactions between symbionts. RESULTS In this paper, we analyzed the genome sequences of the primary symbiont Portiera and of the secondary symbiont Hamiltonella in the B. tabaci Mediterranean (MED) species in order to gain insight into the metabolic role of each symbiont in the biology of their host. The genome sequences of the uncultured symbionts Portiera and Hamiltonella were obtained from one single bacteriocyte of MED B. tabaci. As already reported, the genome of Portiera is highly reduced (357 kb), but has kept a number of genes encoding most essential amino-acids and carotenoids. On the other hand, Portiera lacks almost all the genes involved in the synthesis of vitamins and cofactors. Moreover, some pathways are incomplete, notably those involved in the synthesis of some essential amino-acids. Interestingly, the genome of Hamiltonella revealed that this secondary symbiont can not only provide vitamins and cofactors, but also complete the missing steps of some of the pathways of Portiera. In addition, some critical amino-acid biosynthetic genes are missing in the two symbiotic genomes, but analysis of whitefly transcriptome suggests that the missing steps may be performed by the whitefly itself or its microbiota. CONCLUSIONS These data suggest that Portiera and Hamiltonella are not only complementary but could also be mutually dependent to provide a full complement of nutrients to their host. Altogether, these results illustrate how functional redundancies can lead to gene losses in the genomes of the different symbiotic partners, reinforcing their inter-dependency.
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Affiliation(s)
- Qiong Rao
- Ministry of Agriculture Key Laboratory of Agricultural Entomology, Institute of Insect Sciences, Zhejiang University, 310058, Hangzhou, China.
- School of Agriculture and Food Science, Zhejiang A & F University, 311300, Lin'an, Zhejiang, China.
| | - Pierre-Antoine Rollat-Farnier
- Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 558, 69622, Villeurbanne, Cedex, France.
- Inria Grenoble Rhône-Alpes, Grenoble, France.
| | - Dan-Tong Zhu
- Ministry of Agriculture Key Laboratory of Agricultural Entomology, Institute of Insect Sciences, Zhejiang University, 310058, Hangzhou, China.
| | - Diego Santos-Garcia
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Valencia, Spain.
| | - Francisco J Silva
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Valencia, Spain.
- Unidad Mixta de Investigación en Genómica y Salud FISABIO-Salud Pública and Universitat de València, Valencia, Spain.
| | - Andrés Moya
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Valencia, Spain.
- Unidad Mixta de Investigación en Genómica y Salud FISABIO-Salud Pública and Universitat de València, Valencia, Spain.
| | - Amparo Latorre
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, Valencia, Spain.
- Unidad Mixta de Investigación en Genómica y Salud FISABIO-Salud Pública and Universitat de València, Valencia, Spain.
| | - Cecilia C Klein
- Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 558, 69622, Villeurbanne, Cedex, France.
- Inria Grenoble Rhône-Alpes, Grenoble, France.
| | - Fabrice Vavre
- Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 558, 69622, Villeurbanne, Cedex, France.
| | - Marie-France Sagot
- Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 558, 69622, Villeurbanne, Cedex, France.
- Inria Grenoble Rhône-Alpes, Grenoble, France.
| | - Shu-Sheng Liu
- Ministry of Agriculture Key Laboratory of Agricultural Entomology, Institute of Insect Sciences, Zhejiang University, 310058, Hangzhou, China.
| | - Laurence Mouton
- Université de Lyon, Université Lyon1, Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 558, 69622, Villeurbanne, Cedex, France.
| | - Xiao-Wei Wang
- Ministry of Agriculture Key Laboratory of Agricultural Entomology, Institute of Insect Sciences, Zhejiang University, 310058, Hangzhou, China.
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Alonso A, Marsal S, Julià A. Analytical methods in untargeted metabolomics: state of the art in 2015. Front Bioeng Biotechnol 2015; 3:23. [PMID: 25798438 PMCID: PMC4350445 DOI: 10.3389/fbioe.2015.00023] [Citation(s) in RCA: 388] [Impact Index Per Article: 43.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/18/2015] [Indexed: 12/20/2022] Open
Abstract
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
- Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
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Giacomoni F, Le Corguillé G, Monsoor M, Landi M, Pericard P, Pétéra M, Duperier C, Tremblay-Franco M, Martin JF, Jacob D, Goulitquer S, Thévenot EA, Caron C. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 2014; 31:1493-5. [PMID: 25527831 PMCID: PMC4410648 DOI: 10.1093/bioinformatics/btu813] [Citation(s) in RCA: 272] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2014] [Accepted: 12/04/2014] [Indexed: 11/12/2022] Open
Abstract
SUMMARY The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. AVAILABILITY AND IMPLEMENTATION http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). CONTACT contact@workflow4metabolomics.org.
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Affiliation(s)
- Franck Giacomoni
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Gildas Le Corguillé
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Misharl Monsoor
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Marion Landi
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Pierre Pericard
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Mélanie Pétéra
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Christophe Duperier
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Marie Tremblay-Franco
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Jean-François Martin
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Daniel Jacob
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Sophie Goulitquer
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Etienne A Thévenot
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
| | - Christophe Caron
- INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France
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Paul MLS, Kaur A, Geete A, Sobhia ME. Essential gene identification and drug target prioritization in Leishmania species. MOLECULAR BIOSYSTEMS 2014; 10:1184-95. [PMID: 24643243 DOI: 10.1039/c3mb70440h] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Leishmaniasis is one of the neglected tropical diseases (NTDs), mainly affecting impoverished communities and having varied ranges of pathogenicity according to the diverse spectrum of clinical manifestations. It is endemic in many countries and poses major challenges to healthcare systems in developing countries. Despite the fact that most of the current mono and combination therapies are found to be failures, clear perception of gene essentiality for parasite survival are now desideratum to identify potential biochemical targets through selection. Here we used the metabolic network of L. major, to perform a comprehensive set of in silico deletion mutants and have systematically recognized a clearly defined set of essential proteins by combining several essential criteria. In this paper we summarize the efforts to prioritize potential drug targets up to a five-fold enrichment compared with a random selection.
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Affiliation(s)
- M L Stanly Paul
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S.A.S. Nagar, Mohali, India-160062.
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Junot C, Fenaille F, Colsch B, Bécher F. High resolution mass spectrometry based techniques at the crossroads of metabolic pathways. MASS SPECTROMETRY REVIEWS 2014; 33:471-500. [PMID: 24288070 DOI: 10.1002/mas.21401] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Revised: 05/14/2013] [Accepted: 05/15/2013] [Indexed: 06/02/2023]
Abstract
The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.
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Affiliation(s)
- Christophe Junot
- Commissariat à l'Energie Atomique, Centre de Saclay, DSV/iBiTec-S/SPI, Laboratoire d'Etude du Métabolisme des Médicaments, 91191, Gif-sur-Yvette Cedex, France
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79
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Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks. Bioanalysis 2014; 6:511-24. [PMID: 24568354 DOI: 10.4155/bio.13.348] [Citation(s) in RCA: 135] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.
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Shameer S, Logan-Klumpler FJ, Vinson F, Cottret L, Merlet B, Achcar F, Boshart M, Berriman M, Breitling R, Bringaud F, Bütikofer P, Cattanach AM, Bannerman-Chukualim B, Creek DJ, Crouch K, de Koning HP, Denise H, Ebikeme C, Fairlamb AH, Ferguson MAJ, Ginger ML, Hertz-Fowler C, Kerkhoven EJ, Mäser P, Michels PAM, Nayak A, Nes DW, Nolan DP, Olsen C, Silva-Franco F, Smith TK, Taylor MC, Tielens AGM, Urbaniak MD, van Hellemond JJ, Vincent IM, Wilkinson SR, Wyllie S, Opperdoes FR, Barrett MP, Jourdan F. TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei. Nucleic Acids Res 2014; 43:D637-44. [PMID: 25300491 PMCID: PMC4384016 DOI: 10.1093/nar/gku944] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.
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Affiliation(s)
- Sanu Shameer
- Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | | | - Florence Vinson
- Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Ludovic Cottret
- Institut National de la Recherche Agronomique (INRA), UMR441, Laboratoire des Interactions Plantes-Microorganismes (LIPM), Auzeville, France
| | - Benjamin Merlet
- Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Fiona Achcar
- University of Glasgow, Glasgow, Scotland, G12 8QQ, UK
| | - Michael Boshart
- Ludwig-Maximilians-Universität München, Biocenter, 82152-Martinsried, Germany
| | - Matthew Berriman
- The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Rainer Breitling
- Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, UK
| | | | | | | | | | - Darren J Creek
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville 3052, Australia
| | | | | | - Hubert Denise
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | | | | | | | - Michael L Ginger
- Divisionof Biomedical and Life Sciences, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK
| | | | - Eduard J Kerkhoven
- Chalmers University of Technology, Kemivägen 10, 412 96, Göteborg, Sweden
| | - Pascal Mäser
- Swiss Tropical and Public Health Institute, Socinstr. 57, Basel 4051, Switzerland
| | | | - Archana Nayak
- University of Glasgow, Glasgow, Scotland, G12 8QQ, UK
| | | | | | | | | | - Terry K Smith
- University of St Andrews, St Andrews, Scotland, KY16 9ST, UK
| | | | - Aloysius G M Tielens
- Utrecht University, Utrecht, 3508 TD, The Netherlands Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Michael D Urbaniak
- Divisionof Biomedical and Life Sciences, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK
| | | | | | | | - Susan Wyllie
- University of Dundee, Dundee, Scotland, DD1 4HN, UK
| | | | | | - Fabien Jourdan
- Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
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Courant F, Antignac JP, Dervilly-Pinel G, Le Bizec B. Basics of mass spectrometry based metabolomics. Proteomics 2014; 14:2369-88. [PMID: 25168716 DOI: 10.1002/pmic.201400255] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 07/18/2014] [Accepted: 08/26/2014] [Indexed: 11/08/2022]
Abstract
The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).
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Affiliation(s)
- Frédérique Courant
- Department of Environmental Sciences and Public Health, University of Montpellier 1, UMR 5569 Hydrosciences, Montpellier, France; Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), LUNAM Université Oniris, USC INRA 1329, Nantes, France
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Shang D, Li C, Yao Q, Yang H, Xu Y, Han J, Li J, Su F, Zhang Y, Zhang C, Li D, Li X. Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways. PLoS One 2014; 9:e104934. [PMID: 25153931 PMCID: PMC4143229 DOI: 10.1371/journal.pone.0104934] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 07/14/2014] [Indexed: 11/18/2022] Open
Abstract
Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.
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Affiliation(s)
- Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Chunquan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, P. R. China
| | - Qianlan Yao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Jing Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Fei Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Dongguo Li
- School of Biomedical Engineering, Capital Medical University, No. 10 You An Men Wai Xi Tou Tiao, Beijing, P.R. China
- * E-mail: (DL); (XL)
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
- * E-mail: (DL); (XL)
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Garcia-Albornoz M, Thankaswamy-Kosalai S, Nilsson A, Väremo L, Nookaew I, Nielsen J. BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data. Nucleic Acids Res 2014; 42:W175-81. [PMID: 24792167 PMCID: PMC4086127 DOI: 10.1093/nar/gku371] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.
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Affiliation(s)
- Manuel Garcia-Albornoz
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | | | - Avlant Nilsson
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Leif Väremo
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Intawat Nookaew
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
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84
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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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85
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Milreu PV, Klein CC, Cottret L, Acuña V, Birmelé E, Borassi M, Junot C, Marchetti-Spaccamela A, Marino A, Stougie L, Jourdan F, Crescenzi P, Lacroix V, Sagot MF. Telling metabolic stories to explore metabolomics data: a case study on the yeast response to cadmium exposure. ACTA ACUST UNITED AC 2013; 30:61-70. [PMID: 24167155 PMCID: PMC3866556 DOI: 10.1093/bioinformatics/btt597] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Motivation: The increasing availability of metabolomics data enables to better understand the metabolic processes involved in the immediate response of an organism to environmental changes and stress. The data usually come in the form of a list of metabolites whose concentrations significantly changed under some conditions, and are thus not easy to interpret without being able to precisely visualize how such metabolites are interconnected. Results: We present a method that enables to organize the data from any metabolomics experiment into metabolic stories. Each story corresponds to a possible scenario explaining the flow of matter between the metabolites of interest. These scenarios may then be ranked in different ways depending on which interpretation one wishes to emphasize for the causal link between two affected metabolites: enzyme activation, enzyme inhibition or domino effect on the concentration changes of substrates and products. Equally probable stories under any selected ranking scheme can be further grouped into a single anthology that summarizes, in a unique subnetwork, all equivalently plausible alternative stories. An anthology is simply a union of such stories. We detail an application of the method to the response of yeast to cadmium exposure. We use this system as a proof of concept for our method, and we show that we are able to find a story that reproduces very well the current knowledge about the yeast response to cadmium. We further show that this response is mostly based on enzyme activation. We also provide a framework for exploring the alternative pathways or side effects this local response is expected to have in the rest of the network. We discuss several interpretations for the changes we see, and we suggest hypotheses that could in principle be experimentally tested. Noticeably, our method requires simple input data and could be used in a wide variety of applications. Availability and implementation: The code for the method presented in this article is available at http://gobbolino.gforge.inria.fr. Contact: pvmilreu@gmail.com; vincent.lacroix@univ-lyon1.fr; marie-france.sagot@inria.fr Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Paulo Vieira Milreu
- INRIA Grenoble Rhône-Alpes & Université de Lyon, F-69000 Lyon, Université Lyon 1; CNRS, UMR5558 LBBE, France, Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil, LISBP, UMR CNRS 5504 - INRA 792, Toulouse, France, Mathomics, Center for Mathematical Modeling (UMI-2807 CNRS) and Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile Lab. Statistique et Génome, CNRS UMR8071 INRA1152, Université d'Évry, France, Scuola Normale Superiore, 56126 Pisa, Italy, Laboratoire d'Etude du Métabolisme des Médicaments, DSV/iBiTecS/SPI, CEA/Saclay, 91191 Gif-sur-Yvette, France, La Sapienza University of Rome, Rome, Dipartimento di Sistemi e Informatica, Università di Firenze, I-50134 Firenze, Italy, VU University and CWI, Amsterdam, The Netherlands and INRA UMR1331 - Toxalim, Toulouse, France
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86
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Hyun SH, Lee SY, Sung GH, Kim SH, Choi HK. Metabolic profiles and free radical scavenging activity of Cordyceps bassiana fruiting bodies according to developmental stage. PLoS One 2013; 8:e73065. [PMID: 24058459 PMCID: PMC3772819 DOI: 10.1371/journal.pone.0073065] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 07/15/2013] [Indexed: 11/18/2022] Open
Abstract
The metabolic profiles of Cordyceps bassiana according to fruiting body developmental stage were investigated using gas chromatography-mass spectrometry. We were able to detect 62 metabolites, including 48 metabolites from 70% methanol extracts and 14 metabolites from 100% n-hexane extracts. These metabolites were classified as alcohols, amino acids, organic acids, phosphoric acids, purine nucleosides and bases, sugars, saturated fatty acids, unsaturated fatty acids, or fatty amides. Significant changes in metabolite levels were found according to developmental stage. Relative levels of amino acids, purine nucleosides, and sugars were higher in development stage 3 than in the other stages. Among the amino acids, valine, isoleucine, lysine, histidine, glutamine, and aspartic acid, which are associated with ABC transporters and aminoacyl-tRNA biosynthesis, also showed higher levels in stage 3 samples. The free radical scavenging activities, which were significantly higher in stage 3 than in the other stages, showed a positive correlation with purine nucleoside metabolites such as adenosine, guanosine, and inosine. These results not only show metabolic profiles, but also suggest the metabolic pathways associated with fruiting body development stages in cultivated C. bassiana.
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Affiliation(s)
- Sun-Hee Hyun
- College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Seok-Young Lee
- College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
| | - Gi-Ho Sung
- Mushroom Research Division, Department of Herbal Crop Research, National Institute of Horticultural and Herbal Science, RDA, Suwon, Republic of Korea
| | - Seong Hwan Kim
- Department of Microbiology, Dankook University, Cheonan, Republic of Korea
| | - Hyung-Kyoon Choi
- College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea
- * E-mail:
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Pey J, Tobalina L, de Cisneros JPJ, Planes FJ. A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype. BMC SYSTEMS BIOLOGY 2013; 7:62. [PMID: 23870038 PMCID: PMC3733687 DOI: 10.1186/1752-0509-7-62] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Accepted: 07/16/2013] [Indexed: 12/28/2022]
Abstract
Background The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question. Results In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases. Conclusions With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.
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Affiliation(s)
- Jon Pey
- CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, San Sebastian 20018, Spain
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88
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Meyer H, Weidmann H, Lalk M. Methodological approaches to help unravel the intracellular metabolome of Bacillus subtilis. Microb Cell Fact 2013; 12:69. [PMID: 23844891 PMCID: PMC3722095 DOI: 10.1186/1475-2859-12-69] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Accepted: 07/01/2013] [Indexed: 11/16/2022] Open
Abstract
Background Bacillus subtilis (B. subtilis) has become widely accepted as a model organism for studies on Gram-positive bacteria. A deeper insight into the physiology of this prokaryote requires advanced studies of its metabolism. To provide a reliable basis for metabolome investigations, a validated experimental protocol is needed since the quality of the analytical sample and the final data are strongly affected by the sampling steps. To ensure that the sample analyzed precisely reflects the biological condition of interest, outside biases have to be avoided during sample preparation. Results Procedures for sampling, quenching, extraction of metabolites, cell disruption, as well as metabolite leakage were tested and optimized for B. subtilis. In particular the energy status of the bacterial cell, characterized by the adenylate energy charge, was used to evaluate sampling accuracy. Moreover, the results of the present study demonstrate that the cultivation medium can affect the efficiency of the developed sampling procedure. Conclusion The final workflow presented here allows for the reproducible and reliable generation of physiological data. The method with the highest qualitative and quantitative metabolite yield was chosen, and when used together with complementary bioanalytical methods (i.e., GC-MS, LC-MS and 1H-NMR) provides a solid basis to gather information on the metabolome of B. subtilis.
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Affiliation(s)
- Hanna Meyer
- Institute of Biochemistry, Ernst-Moritz-Arndt-University Greifswald, Felix-Hausdorff-Strasse 4, 17487 Greifswald, Germany
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89
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Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B. Predicting network activity from high throughput metabolomics. PLoS Comput Biol 2013; 9:e1003123. [PMID: 23861661 PMCID: PMC3701697 DOI: 10.1371/journal.pcbi.1003123] [Citation(s) in RCA: 589] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2012] [Accepted: 05/15/2013] [Indexed: 12/26/2022] Open
Abstract
The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells. Mass spectrometry based untargeted metabolomics can now profile several thousand of metabolites simultaneously. However, these metabolites have to be identified before any biological meaning can be drawn from the data. Metabolite identification is a challenging and low throughput process, therefore becomes the bottleneck of the filed. We report here a novel approach to predict biological activity directly from mass spectrometry data without a priori identification of metabolites. By unifying network analysis and metabolite prediction under the same computational framework, the organization of metabolic networks and pathways helps resolve the ambiguity in metabolite prediction to a large extent. We validated our algorithms on a set of activation experiment of innate immune cells. The predicted activities were confirmed by both gene expression and metabolite identification. This method shall greatly accelerate the application of high throughput metabolomics, as the tedious task of identifying hundreds of metabolites upfront can be shifted to a handful of validation experiments after our computational prediction.
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Affiliation(s)
- Shuzhao Li
- Emory Vaccine Center, Emory University, Atlanta, Georgia, USA.
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90
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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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Crutchfield CA, Olson MT, Gourgari E, Nesterova M, Stratakis CA, Yergey AL. Comprehensive analysis of LC/MS data using pseudocolor plots. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2013; 24:230-237. [PMID: 23283727 PMCID: PMC4141469 DOI: 10.1007/s13361-012-0524-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Revised: 10/15/2012] [Accepted: 10/23/2012] [Indexed: 06/01/2023]
Abstract
We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing's disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.
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92
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Abstract
Spurred by recent innovations in genome sequencing, the reconstruction of genome-scale models has increased in recent years. Genome-scale models are now available for a wide range of organisms, and models have been successfully applied to a number of research topics including metabolic engineering, genome annotation, biofuel production, and interpretation of omics data sets. The challenge is how to manage the large amount of data in genome-scale models and perform comparative analysis to gain new biological insights. In this chapter, important standards for genome-scale modeling are outlined. Furthermore, management strategies as well as existing repository and construction tools are discussed. As the comparison of models is an important aspect during the development and analysis stages, available methods are presented and existing software solutions are reviewed.
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Affiliation(s)
- Stephan Pabinger
- Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria.
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93
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Yang L, Tang K, Qi Y, Ye H, Chen W, Zhang Y, Cao Z. Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 3:S19. [PMID: 23282096 PMCID: PMC3524310 DOI: 10.1186/1752-0509-6-s3-s19] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Central precocious puberty (CPP) is a common pediatric endocrine disease caused by early activation of hypothalamic-putuitary-gonadal (HPG) axis, yet the exact mechanism was poorly understood. Although there were some proofs that an altered metabolic profile was involved in CPP, interpreting the biological implications at a systematic level is still in pressing need. To gain a systematic understanding of the biological implications, this paper analyzed the CPP differential urine metabolites from a network point of view. RESULTS In this study, differential urine metabolites between CPP girls and age-matched normal ones were identified by LC-MS. Their basic topological parameters were calculated in the background network. The network decomposition suggested that CPP differential urine metabolites were most relevant to amino acid metabolism. Further proximity analysis of CPP differential urine metabolites and neuro-endocrine metabolites showed a close relationship between CPP metabolism and neuro-endocrine system. Then the core metabolic network of CPP was successfully constructed among all these differential urine metabolites. As can be demonstrated in the core network, abnormal aromatic amino acid metabolism might influence the activity of HPG and hypothalamic pituitary adrenal (HPA) axis. Several adjustments to the early activation of puberty in CPP girls could also be revealed by urine metabonomics. CONCLUSIONS The present article demonstrated the ability of urine metabonomics to provide several potential metabolic clues for CPP's mechanism. It was revealed that abnormal metabolism of amino acid, especially aromatic amino acid, might have a close correlation with CPP's pathogenesis by activating HPG axis and suppressing HPA axis. Such a method of network-based analysis could also be applied to other metabonomics analysis to provide an overall perspective at a systematic level.
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Affiliation(s)
- Linlin Yang
- School of Life Science and Technology, Tongji University, Shanghai 200092, China
- Shanghai Center for Bioinformation Technology, Shanghai 200235, China
| | - Kailin Tang
- Shanghai Center for Bioinformation Technology, Shanghai 200235, China
| | - Ying Qi
- Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Hao Ye
- Shanghai Center for Bioinformation Technology, Shanghai 200235, China
- State Key Laboratory of Bioreactor Engineering, East China University of Science & Technology, Shanghai 200237, China
| | - Wenlian Chen
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Rui Jin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yongyu Zhang
- Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhiwei Cao
- School of Life Science and Technology, Tongji University, Shanghai 200092, China
- Shanghai Center for Bioinformation Technology, Shanghai 200235, China
- Key Laboratory of Liver and Kidney Diseases (Shanghai University of Traditional Chinese Medicine), Ministry of Education, Shanghai 200021, China
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94
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Wurtele ES, Chappell J, Jones AD, Celiz MD, Ransom N, Hur M, Rizshsky L, Crispin M, Dixon P, Liu J, P Widrlechner M, Nikolau BJ. Medicinal plants: a public resource for metabolomics and hypothesis development. Metabolites 2012; 2:1031-59. [PMID: 24957774 PMCID: PMC3901233 DOI: 10.3390/metabo2041031] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Revised: 10/30/2012] [Accepted: 10/31/2012] [Indexed: 11/16/2022] Open
Abstract
Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range ofantioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology.
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Affiliation(s)
- Eve Syrkin Wurtele
- Department of Genetics, Cell and Developmental Biology, Iowa State University, Ames, IA 50011, USA.
| | - Joe Chappell
- Department of Cellular and Molecular Biochemistry, University of Kentucky, Lexington, KY, 40536, USA
| | - A Daniel Jones
- Department of Biochemistry & Molecular Biology and Deptment of Chemistry, Michigan State University, East Lansing, MI 48824, USA
| | - Mary Dawn Celiz
- Department of Biochemistry & Molecular Biology and Deptment of Chemistry, Michigan State University, East Lansing, MI 48824, USA
| | - Nick Ransom
- Department of Genetics, Cell and Developmental Biology, Iowa State University, Ames, IA 50011, USA
| | - Manhoi Hur
- Department of Genetics, Cell and Developmental Biology, Iowa State University, Ames, IA 50011, USA
| | - Ludmila Rizshsky
- Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA50011, USA
| | - Matthew Crispin
- Department of Genetics, Cell and Developmental Biology, Iowa State University, Ames, IA 50011, USA
| | - Philip Dixon
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Jia Liu
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Mark P Widrlechner
- Department of Ecology, Evolution, and Organismal Biology and Department of Horticulture, Iowa State University, Ames, IA 50011, USA
| | - Basil J Nikolau
- Center for Metabolic Biology, The Plant Science Institute, Iowa State University, Ames, IA 50011, USA
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95
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Llorach R, Garcia-Aloy M, Tulipani S, Vazquez-Fresno R, Andres-Lacueva C. Nutrimetabolomic strategies to develop new biomarkers of intake and health effects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2012; 60:8797-8808. [PMID: 22594919 DOI: 10.1021/jf301142b] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Correctly assessing the metabolic status of subjects after consumption of specific diets is an important challenge for modern nutrition. Recently, metabolomics has been proposed as a powerful tool for exploring the complex relationship between nutrition and health. Nutritional metabolomics, through investigating the role that dietary components play in the maintenance of health and development of risk disease, aims to identify new biomarkers that allow the intake of these compounds to be monitored and related to their expected biological effects. This review offers an overview of the application of nutrimetabolomic strategies in the discovery of new biomarkers in human nutritional research, suggesting three main categories: (1) assessment of nutritional and dietary interventions; (2) diet exposure and food consumption monitoring; and (3) health phenotype and metabolic impact of diet. For this purpose, several examples of these applications will be used to provide evidence and to discuss the advantages and drawbacks of these nutrimetabolomic strategies.
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Affiliation(s)
- Rafael Llorach
- Nutrition and Food Science Department, XaRTA, INSA, Pharmacy Faculty, University of Barcelona , Avinguda Joan XXIII s/n, 08028 Barcelona, Spain
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Klein CC, Cottret L, Kielbassa J, Charles H, Gautier C, Ribeiro de Vasconcelos AT, Lacroix V, Sagot MF. Exploration of the core metabolism of symbiotic bacteria. BMC Genomics 2012; 13:438. [PMID: 22938206 PMCID: PMC3543179 DOI: 10.1186/1471-2164-13-438] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Accepted: 08/18/2012] [Indexed: 12/01/2022] Open
Abstract
Background A large number of genome-scale metabolic networks is now available for many organisms, mostly bacteria. Previous works on minimal gene sets, when analysing host-dependent bacteria, found small common sets of metabolic genes. When such analyses are restricted to bacteria with similar lifestyles, larger portions of metabolism are expected to be shared and their composition is worth investigating. Here we report a comparative analysis of the small molecule metabolism of symbiotic bacteria, exploring common and variable portions as well as the contribution of different lifestyle groups to the reduction of a common set of metabolic capabilities. Results We found no reaction shared by all the bacteria analysed. Disregarding those with the smallest genomes, we still do not find a reaction core, however we did find a core of biochemical capabilities. While obligate intracellular symbionts have no core of reactions within their group, extracellular and cell-associated symbionts do have a small core composed of disconnected fragments. In agreement with previous findings in Escherichia coli, their cores are enriched in biosynthetic processes whereas the variable metabolisms have similar ratios of biosynthetic and degradation reactions. Conversely, the variable metabolism of obligate intracellular symbionts is enriched in anabolism. Conclusion Even when removing the symbionts with the most reduced genomes, there is no core of reactions common to the analysed symbiotic bacteria. The main reason is the very high specialisation of obligate intracellular symbionts, however, host-dependence alone is not an explanation for such absence. The composition of the metabolism of cell-associated and extracellular bacteria shows that while they have similar needs in terms of the building blocks of their cells, they have to adapt to very distinct environments. On the other hand, in obligate intracellular bacteria, catabolism has largely disappeared, whereas synthetic routes appear to have been selected for depending on the nature of the symbiosis. As more genomes are added, we expect, based on our simulations, that the core of cell-associated and extracellular bacteria continues to diminish, converging to approximately 60 reactions.
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Abstract
The decoding of the Tritryp reference genomes nearly 7 years ago provided a first peek into the biology of pathogenic trypanosomatids and a blueprint that has paved the way for genome-wide studies. Although 60-70% of the predicted protein coding genes in Trypanosoma brucei, Trypanosoma cruzi and Leishmania major remain unannotated, the functional genomics landscape is rapidly changing. Facilitated by the advent of next-generation sequencing technologies, improved structural and functional annotation and genes and their products are emerging. Information is also growing for the interactions between cellular components as transcriptomes, regulatory networks and metabolomes are characterized, ushering in a new era of systems biology. Simultaneously, the launch of comparative sequencing of multiple strains of kinetoplastids will finally lead to the investigation of a vast, yet to be explored, evolutionary and pathogenomic space.
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Affiliation(s)
- J Choi
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
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98
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Barupal DK, Haldiya PK, Wohlgemuth G, Kind T, Kothari SL, Pinkerton KE, Fiehn O. MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity. BMC Bioinformatics 2012; 13:99. [PMID: 22591066 PMCID: PMC3495401 DOI: 10.1186/1471-2105-13-99] [Citation(s) in RCA: 179] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2011] [Accepted: 04/25/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites. RESULTS We present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development. CONCLUSIONS MetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed at http://metamapp.fiehnlab.ucdavis.edu.
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99
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Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M. Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis. Curr Bioinform 2012; 7:96-108. [PMID: 22438836 PMCID: PMC3299976 DOI: 10.2174/157489312799304431] [Citation(s) in RCA: 189] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2011] [Revised: 10/25/2011] [Accepted: 12/07/2011] [Indexed: 01/04/2023]
Abstract
Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.
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Affiliation(s)
- Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa 252-8520, Japan
- Graduate School of Medicine and Faculty of Medicine Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Masato Kawakami
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
- Department of Environment and Information Studies, Keio University, Fujisawa, Kanagawa 252-8520, Japan
| | - Martin Robert
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa 252-8520, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
- Department of Environment and Information Studies, Keio University, Fujisawa, Kanagawa 252-8520, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan
- Department of Environment and Information Studies, Keio University, Fujisawa, Kanagawa 252-8520, Japan
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100
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A genomic reappraisal of symbiotic function in the aphid/Buchnera symbiosis: reduced transporter sets and variable membrane organisations. PLoS One 2011; 6:e29096. [PMID: 22229056 PMCID: PMC3246468 DOI: 10.1371/journal.pone.0029096] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2011] [Accepted: 11/21/2011] [Indexed: 11/19/2022] Open
Abstract
Buchnera aphidicola is an obligate symbiotic bacterium that sustains the physiology of aphids by complementing their exclusive phloem sap diet. In this study, we reappraised the transport function of different Buchnera strains, from the aphids Acyrthosiphon pisum, Schizaphis graminum, Baizongia pistaciae and Cinara cedri, using the re-annotation of their transmembrane proteins coupled with an exploration of their metabolic networks. Although metabolic analyses revealed high interdependencies between the host and the bacteria, we demonstrate here that transport in Buchnera is assured by low transporter diversity, when compared to free-living bacteria, being mostly based on a few general transporters, some of which probably have lost their substrate specificity. Moreover, in the four strains studied, an astonishing lack of inner-membrane importers was observed. In Buchnera, the transport function has been shaped by the distinct selective constraints occurring in the Aphididae lineages. Buchnera from A. pisum and S. graminum have a three-membraned system and similar sets of transporters corresponding to most compound classes. Transmission electronic microscopic observations and confocal microscopic analysis of intracellular pH fields revealed that Buchnera does not show any of the typical structures and properties observed in integrated organelles. Buchnera from B. pistaciae seem to possess a unique double membrane system and has, accordingly, lost all of its outer-membrane integral proteins. Lastly, Buchnera from C. cedri revealed an extremely poor repertoire of transporters, with almost no ATP-driven active transport left, despite the clear persistence of the ancestral three-membraned system.
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