From correlation to causation: analysis of metabolomics data using systems biology approaches

IntroductionMetabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.ObjectivesThis review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.MethodsWe review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.ResultsWe offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.ConclusionsSystems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

[1]  Nicola Zamboni,et al.  13C metabolic flux analysis in complex systems. , 2011, Current opinion in biotechnology.

[2]  W. Wiechert,et al.  How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics. , 2015, Current opinion in biotechnology.

[3]  F. Bruggeman,et al.  The nature of systems biology. , 2007, Trends in microbiology.

[4]  Ulrich Mansmann,et al.  GlobalANCOVA: exploration and assessment of gene group effects , 2008, Bioinform..

[5]  Israel Steinfeld,et al.  BMC Bioinformatics BioMed Central , 2008 .

[6]  Age K. Smilde,et al.  Covariances Simultaneous Component Analysis: a new method within a framework for modeling covariances , 2015 .

[7]  Oliver Fiehn,et al.  MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity , 2012, BMC Bioinformatics.

[8]  Jing Gao,et al.  Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks , 2010, Bioinform..

[9]  Jamey D. Young,et al.  INCA: a computational platform for isotopically non-stationary metabolic flux analysis , 2014, Bioinform..

[10]  Silas Granato Villas-Bôas,et al.  Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity , 2010, Bioinform..

[11]  David R. Gilbert,et al.  MetaNetter: inference and visualization of high-resolution metabolomic networks , 2008, Bioinform..

[12]  R. Trethewey,et al.  Metabolic profiling: a Rosetta Stone for genomics? , 1999, Current opinion in plant biology.

[13]  Michael P. Barrett,et al.  MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks , 2010, Nucleic Acids Res..

[14]  Wolfgang Wiechert,et al.  13CFLUX2—high-performance software suite for 13C-metabolic flux analysis , 2012, Bioinform..

[15]  W. Wong,et al.  GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space. , 2004, Applied bioinformatics.

[16]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[17]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[18]  Fabian J Theis,et al.  Computational approaches for systems metabolomics. , 2016, Current opinion in biotechnology.

[19]  L. Quek,et al.  OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis , 2009, Microbial cell factories.

[20]  Steven C. Lawlor,et al.  MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data , 2003, Genome Biology.

[21]  Douglas B. Kell,et al.  Proposed minimum reporting standards for data analysis in metabolomics , 2007, Metabolomics.

[22]  Christoph Steinbeck,et al.  Navigating freely-available software tools for metabolomics analysis , 2017, Metabolomics.

[23]  Wei Zhao,et al.  Weighted Gene Coexpression Network Analysis: State of the Art , 2010, Journal of biopharmaceutical statistics.

[24]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[25]  M. Cascante,et al.  Restrictions in ATP diffusion within sarcomeres can provoke ATP-depleted zones impairing exercise capacity in chronic obstructive pulmonary disease. , 2016, Biochimica et biophysica acta.

[26]  Guang Sun,et al.  Metabolomics Differential Correlation Network Analysis of Osteoarthritis , 2016, PSB.

[27]  Ned S Wingreen,et al.  Enzyme clustering accelerates processing of intermediates through metabolic channeling , 2014, Nature Biotechnology.

[28]  A. Peters,et al.  Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling , 2017, Journal of proteome research.

[29]  J. Selbig,et al.  Parallel analysis of transcript and metabolic profiles: a new approach in systems biology , 2003, EMBO reports.

[30]  Shoshi Kikuchi,et al.  Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling. , 2008, Journal of proteome research.

[31]  Lars K. Nielsen,et al.  Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach , 2016, Scientific Reports.

[32]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[33]  P. Mendes,et al.  Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks , 2013, PloS one.

[34]  E. Ross.  Gαq and Phospholipase C-β: Turn On, Turn Off, and Do It Fast , 2011, Science Signaling.

[35]  O. Fiehn.  Metabolomics – the link between genotypes and phenotypes , 2004, Plant Molecular Biology.

[36]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[37]  H. Soininen,et al.  Metabolome in progression to Alzheimer's disease , 2011, Translational Psychiatry.

[38]  Lei Shi,et al.  SABIO-RK—database for biochemical reaction kinetics , 2011, Nucleic Acids Res..

[39]  David S. Wishart,et al.  Current Progress in computational metabolomics , 2007, Briefings Bioinform..

[40]  A. Lane,et al.  Stable isotope-resolved metabolomics and applications for drug development. , 2012, Pharmacology & therapeutics.

[41]  David S. Wishart,et al.  HMDB 3.0—The Human Metabolome Database in 2013 , 2012, Nucleic Acids Res..

[42]  O. Mamer,et al.  The identification of urinary acids by coupled gas chromatography-mass spectrometry. , 1971, Clinica chimica acta; international journal of clinical chemistry.

[43]  Peter Donnelly,et al.  Human metabolic profiles are stably controlled by genetic and environmental variation , 2011, Molecular systems biology.

[44]  Avi Ma’ayan.  Introduction to Network Analysis in Systems Biology , 2011, Science Signaling.

[45]  Christoph Steinbeck,et al.  BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology , 2015, BMC Bioinformatics.

[46]  I. Hulsegge,et al.  Globaltest and GOEAST: two different approaches for Gene Ontology analysis , 2009, BMC proceedings.

[47]  G. Stephanopoulos,et al.  Review of metabolic pathways activated in cancer cells as determined through isotopic labeling and network analysis. , 2017, Metabolic engineering.

[48]  Alexander Goesmann,et al.  Visualizing post genomics data-sets on customized pathway maps by ProMeTra – aeration-dependent gene expression and metabolism of Corynebacterium glutamicum as an example , 2009, BMC Systems Biology.

[49]  Giovanni Scardoni,et al.  Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data , 2012, Bioinform..

[50]  Krin A. Kay,et al.  The implications of human metabolic network topology for disease comorbidity , 2008, Proceedings of the National Academy of Sciences.

[51]  Bart C. Weimer,et al.  Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction , 2015, BMC Bioinformatics.

[52]  Age K Smilde,et al.  Global test for metabolic pathway differences between conditions. , 2012, Analytica chimica acta.

[53]  Minoru Kanehisa,et al.  KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..

[54]  Nancy E. Witowski,et al.  Urinary metabolic network analysis in trauma, hemorrhagic shock, and resuscitation , 2012, Metabolomics.

[55]  H. Meuzelaar,et al.  A technique for fast and reproducible fingerprinting of bacteria by pyrolysis mass spectrometry. , 1973, Analytical chemistry.

[56]  Edda Klipp,et al.  Inferring dynamic properties of biochemical reaction networks from structural knowledge. , 2004, Genome informatics. International Conference on Genome Informatics.

[57]  Joaquín Dopazo,et al.  Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data , 2010, Bioinform..

[58]  Susan C. Connor,et al.  Assignment of MS-based metabolomic datasets via compound interaction pair mapping , 2008, Metabolomics.

[59]  Tasneem Hameed,et al.  Change. , 2018, The Journal of the Oklahoma State Medical Association.

[60]  J. Kleinjans,et al.  Development of novel tools for the in vitro investigation of drug-induced liver injury , 2015, Expert opinion on drug metabolism & toxicology.

[61]  Jean-Charles Portais,et al.  Influx_s: Increasing Numerical Stability and Precision for Metabolic Flux Analysis in Isotope Labelling Experiments , 2012, Bioinform..

[62]  Yangyang Zhao,et al.  BioModels: ten-year anniversary , 2014, Nucleic Acids Res..

[63]  R. D'ari.  Systematic functional analysis of the yeast genome , 1998 .

[64]  Masanori Arita,et al.  Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach , 2011, BMC Systems Biology.

[65]  W. Lee.  Characterizing phenotype with tracer based metabolomics , 2006, Metabolomics.

[66]  Fabien Jourdan,et al.  Computational methods to identify metabolic sub‐networks based on metabolomic profiles , 2017, Briefings Bioinform..

[67]  Gerald C. Chu,et al.  Compensatory metabolic networks in pancreatic cancers upon perturbation of glutamine metabolism , 2017, Nature Communications.

[68]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[69]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[70]  O. Hoekenga,et al.  Weighted Correlation Network Analysis (WGCNA) Applied to the Tomato Fruit Metabolome , 2011, PloS one.

[71]  Hunter N B Moseley,et al.  Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS. , 2014, Methods in molecular biology.

[72]  Johan Trygg,et al.  Chemometrics in metabonomics. , 2007, Journal of proteome research.

[73]  Antje Chang,et al.  BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA , 2012, Nucleic Acids Res..

[74]  Fabian J. Theis,et al.  Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data , 2011, BMC Systems Biology.

[75]  Erwin P. Gianchandani,et al.  Correction: Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Computational Biology.

[76]  Ivano Bertini,et al.  Evidence of different metabolic phenotypes in humans , 2008, Proceedings of the National Academy of Sciences.

[77]  L. Tenori,et al.  Entropy-Based Network Representation of the Individual Metabolic Phenotype. , 2016, Journal of proteome research.

[78]  David S. Wishart,et al.  MetaboAnalyst 3.0—making metabolomics more meaningful , 2015, Nucleic Acids Res..

[79]  D. Kell,et al.  A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations , 2001, Nature Biotechnology.

[80]  O. Fiehn,et al.  Differential metabolic networks unravel the effects of silent plant phenotypes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[81]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[82]  E. Saccenti.  Correlation Patterns in Experimental Data Are Affected by Normalization Procedures: Consequences for Data Analysis and Network Inference. , 2017, Journal of proteome research.

[83]  Lin Song,et al.  Comparison of co-expression measures: mutual information, correlation, and model based indices , 2012, BMC Bioinformatics.

[84]  Ernest Fraenkel,et al.  Revealing disease-associated pathways by network integration of untargeted metabolomics , 2016, Nature Methods.

[85]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[86]  Chris T. A. Evelo,et al.  WikiPathways: building research communities on biological pathways , 2011, Nucleic Acids Res..

[87]  Edward M. Marcotte,et al.  The path not taken , 2001, Nature Biotechnology.

[88]  Joost T. van Dongen,et al.  Combined Transcript and Metabolite Profiling of Arabidopsis Leaves Reveals Fundamental Effects of the Thiol-Disulfide Status on Plant Metabolism1[W][OA] , 2006, Plant Physiology.

[89]  Age K Smilde,et al.  Reverse engineering of metabolic networks, a critical assessment. , 2011, Molecular bioSystems.

[90]  Michael Sjöström,et al.  Chemometrics, present and future success , 1998 .

[91]  P. Mendes,et al.  The origin of correlations in metabolomics data , 2005, Metabolomics.

[92]  W. Wiechert,et al.  Isotopically non-stationary metabolic flux analysis: complex yet highly informative. , 2013, Current opinion in biotechnology.

[93]  Age K. Smilde,et al.  Metabolic network discovery through reverse engineering of metabolome data , 2009, Metabolomics.

[94]  Jelle J. Goeman,et al.  A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..

[95]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[96]  Vitaly A. Selivanov,et al.  Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells , 2005, Bioinform..

[97]  Matej Oresic,et al.  Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions , 2011, BMC Systems Biology.

[98]  J. Griffin.  The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball? , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[99]  A. Smilde,et al.  Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. , 2006, Analytical chemistry.

[100]  A. B. Robinson,et al.  Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[101]  S. Schnell,et al.  Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. , 2004, Progress in biophysics and molecular biology.

[102]  Gender-specific pathway differences in the human serum metabolome , 2015, Metabolomics.

[103]  Karsten Suhre,et al.  MassTRIX: mass translator into pathways , 2008, Nucleic Acids Res..

[104]  J Ovádi,et al.  Physiological significance of metabolic channelling. , 1991, Journal of theoretical biology.

[105]  Svante Wold,et al.  Chemometrics; what do we mean with it, and what do we want from it? , 1995 .

[106]  Fátima Sánchez-Cabo,et al.  Mitochondrial and nuclear DNA matching shapes metabolism and healthy ageing , 2016, Nature.

[107]  Yury Tikunov,et al.  A correlation network approach to metabolic data analysis for tomato fruits , 2008, Euphytica.

[108]  Erwin P. Gianchandani,et al.  Dynamic Analysis of Integrated Signaling, Metabolic, and Regulatory Networks , 2008, PLoS Comput. Biol..

[109]  Paul Weiss.  The Nature of Systems (I.) , 1929 .

[110]  Thomas Linke,et al.  Visualizing plant metabolomic correlation networks using clique-metabolite matrices , 2001, Bioinform..

[111]  Hideyuki Suzuki,et al.  KaPPA-View. A Web-Based Analysis Tool for Integration of Transcript and Metabolite Data on Plant Metabolic Pathway Maps1[w] , 2005, Plant Physiology.

[112]  O. Fiehn,et al.  Can we discover novel pathways using metabolomic analysis? , 2002, Current opinion in biotechnology.

[113]  Diana M. Hendrickx.  Network inference from time-resolved metabolomics data , 2013 .

[114]  ZhangBin,et al.  Defining clusters from a hierarchical cluster tree , 2008 .

[115]  Ljubisa Miskovic,et al.  iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks. , 2016, Metabolic engineering.

[116]  L. Tenori,et al.  Allostasis and Resilience of the Human Individual Metabolic Phenotype. , 2015, Journal of proteome research.

[117]  Antje Chang,et al.  BRENDA, the enzyme information system in 2011 , 2010, Nucleic Acids Res..

[118]  Franco Moritz,et al.  Characterization of poplar metabotypes via mass difference enrichment analysis. , 2017, Plant, cell & environment.

[119]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[120]  Jinyan Li,et al.  B-cell epitope prediction through a graph model , 2012, BMC Bioinformatics.

[121]  Joachim Selbig,et al.  Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach , 2009, PloS one.

[122]  Yixin Chen,et al.  WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism , 2016, BMC Bioinformatics.

[123]  Nicola Zamboni,et al.  FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments , 2005, BMC Bioinformatics.

[124]  Sayed-Amir Marashi,et al.  Biomedical applications of cell- and tissue-specific metabolic network models , 2017, J. Biomed. Informatics.

[125]  L. Tenori,et al.  Probabilistic networks of blood metabolites in healthy subjects as indicators of latent cardiovascular risk. , 2015, Journal of proteome research.

[126]  Philip Miller,et al.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models , 2015, Nucleic Acids Res..

[127]  Lothar Willmitzer,et al.  Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulphur stress response. , 2005, Journal of experimental botany.

[128]  L. Tenori,et al.  Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects. , 2018, Journal of proteome research.

[129]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[130]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[131]  Neil Swainston,et al.  Recon 2.2: from reconstruction to model of human metabolism , 2016, Metabolomics.

[132]  Joe Wandy,et al.  PiMP my metabolome: an integrated, web-based tool for LC-MS metabolomics data , 2017, Bioinform..

[133]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[134]  An-Ping Zeng,et al.  Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation , 2014, BMC Systems Biology.

[135]  Edoardo Saccenti,et al.  Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics. , 2015, Journal of proteome research.

[136]  Andreas Zell,et al.  Path2Models: large-scale generation of computational models from biochemical pathway maps , 2013, BMC Systems Biology.

[137]  Jürgen Kurths,et al.  Observing and Interpreting Correlations in Metabolic Networks , 2003, Bioinform..

[138]  P. Karp,et al.  Computational prediction of human metabolic pathways from the complete human genome , 2004, Genome Biology.

[139]  W. Windig,et al.  Factor analysis of the influence of changes in experimental conditions in pyrolysis—mass spectrometry , 1980 .

[140]  Ian J. Brown,et al.  Human metabolic phenotype diversity and its association with diet and blood pressure , 2008, Nature.

[141]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[142]  Gregory Stephanopoulos,et al.  Quantifying Reductive Carboxylation Flux of Glutamine to Lipid in a Brown Adipocyte Cell Line* , 2008, Journal of Biological Chemistry.

[143]  Scott B. Crown,et al.  Parallel labeling experiments and metabolic flux analysis: Past, present and future methodologies. , 2013, Metabolic engineering.

[144]  E. Horning,et al.  Metabolic profiles: gas-phase methods for analysis of metabolites. , 1971, Clinical chemistry.

[145]  Enrico Giampieri,et al.  Multiscale characterization of ageing and cancer progression by a novel network entropy measure. , 2015, Molecular bioSystems.

[146]  Joerg M. Buescher,et al.  A roadmap for interpreting (13)C metabolite labeling patterns from cells. , 2015, Current opinion in biotechnology.

[147]  N. Kruger,et al.  Insights into plant metabolic networks from steady-state metabolic flux analysis. , 2009, Biochimie.

[148]  Avi Ma ' ayan,et al.  Introduction to Network Analysis in Systems Biology , 2011 .

[149]  J. Lindon,et al.  Systems biology: Metabonomics , 2008, Nature.

[150]  Bernhard O. Palsson,et al.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions , 2010, BMC Bioinformatics.

[151]  S. Rhee,et al.  MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. , 2004, The Plant journal : for cell and molecular biology.

[152]  Hao Ye,et al.  Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data , 2012, BMC Systems Biology.

[153]  Matej Oresic,et al.  MPEA - metabolite pathway enrichment analysis , 2011, Bioinform..

[154]  U. Mansmann,et al.  Testing Differential Gene Expression in Functional Groups , 2005, Methods of Information in Medicine.

[155]  F Baganz,et al.  Systematic functional analysis of the yeast genome. , 1998, Trends in biotechnology.

[156]  Calyampudi R. Rao.  Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation , 1948, Mathematical Proceedings of the Cambridge Philosophical Society.

[157]  J. Stelling,et al.  Robustness of Cellular Functions , 2004, Cell.

[158]  Jan van der Greef,et al.  Symbiosis of chemometrics and metabolomics: past, present, and future , 2005 .

[159]  U. Sauer,et al.  Article number: 62 REVIEW Metabolic networks in motion: 13 C-based flux analysis , 2022 .

[160]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..