Metabolomics & Protein Interactomics

Computational Metabolomics & Protein Interactomics

Participating Partners: ΙFORTH/ICE-HT (Coordinating Partners, PIs: M.Klapa & N. Moschonas), BSRC "Alexander Fleming" (PI: Martin Reczko), NHRF (PI: A. Chatziioannou) & University of Patras (ΕΥ: G. Patrinos)

While metabolites have been mainly analyzed in the context of their role as reactants and products of metabolic networks, most of them act also as regulatory molecules of proteins participating in many biological processes, contributing significantly to the parameters that define the active protein interaction network under particular physiological conditions. In this context, standardized combination of metabolomic and proteomic data for a combined reconstruction and analysis of metabolic and protein interaction networks of biological systems could enhance the perspective that we currently have of important biological processes and disease underlying pathophysiological mechanisms.
To this end, the tasks to be pursued in this pilot study will be:

Task 1: Standardized repositories of metabolomic data from various biological systems (PI: M. Klapa, FORTH/ICE-HT)

For the educated analysis of metabolomic data from various contexts and biological systems and their consistent use in meta-analyses regarding either solely of metabolic activity studies or mainly in combination with transcriptomic and proteomic data, standardized repositories of metabolomic data are required, e.g. [1-4]. FORTH/ICE-HT has developed a standardized repository of gas chromatography-mass spectrometry (GC-MS) metabolomic data, currently as part of the M-IOLITE software ( [2], generated by the FORTH/ICE-HT Metabolic Engineering and Systems Biology Laboratory (MESBL) from various species and biological problems (e.g. brain tissue/various brain regions, heart tissue, liver tissue, human cell lines, blood plasma samples, plant leaves, [5-11]), based on a validated metabolite peak library [2-3]. Furthering this repository with data from other partners of ELIXIR-GR (see for example the Marine pilot study) and available in the literature along with capabilities to add and combine experimental protocol and design information in a standardized way would assist in developing a useful resource for metabolic network activity analysis and for addressing a set of metabolomic data standardization issues between laboratories, different experiments and different systems. Effort will be made to integrate this resource and expertise in standardized metabolomic data analysis repositories that are under development at the European and international level.

Task 2: Standardized integration of metabolomic with proteomic and transcriptomic data repositories (PIs: M. Klapa, FORTH/ICE-HT; M. Reczko, BSRC “Alexander Fleming”)

In this task, we propose the development of an interface that will enable the integrated use of the metabolomic data repository with proteomic and transcriptomic data provided by other partners of ELIXIR-GR and the literature, which could be linked to various tools of meta-analysis and network analysis provided by partners of ELIXIR-GR (e.g. BSRC Al. Fleming) or having being established in metabolic network analysis in the bioinformatics community.

Task 3: Combined reconstruction and analysis of metabolic and protein interaction networks (PIs: M. Klapa & N. Moschonas, FORTH/ICE-HT; M. Reczko, BSRC “Alexander Fleming”)

In this task, we propose the development of combined reconstruction of the metabolic and protein interaction network of a human cell model, a mouse brain and heart model based on the protein interaction meta-database for human, PICKLE, ( that has been developed at U. Patras & FORTH/ICE-HT [12-13], the available literature and the combined metabolomic and proteomic repositories (see also Task 2), to investigate the physiology of these systems in the context of genetic mutations and specific pathophysiologies. Superpathway models (of cell signalling pathways and metabolism) will be generated, based on specific input sets, integrating the significant pathways derived through statistical enrichment analysis on Reactome metabolic and signalling networks [14] and KEGG Pathways databases [15], via utilisation of the API service of the KENeV web application.

Task 4: Target identification and validation via a systems-level multi-layer strategy (PI: G. Patrinos, U. Patras)

In this task, we propose target identification and/or validation by data integration and combined analysis on data generated by next-generation proteomics (such as sequential window acquisition of all theoretical mass spectra) and metabolomics techniques. Findings will be used to model extended pathways or functional networks with dozens of proteins and metabolites acting in tandem. Instead of any single omics approach, we propose an integrated (transomic) analysis that will provide more insights into the emergence of the phenotypes in question than any layer can by itself, highlighting the complementarity of a multilayered strategy. In this context, we have two layers of systems-scale molecular measurements; the (pharmaco)-metabolome (layer 1) and the proteome (layer 2). Layers 1 and 2 are coupled to information technologies. We propose the use of chemoinformatic tools through network-based analysis, an approach that integrates different levels of information in xenobiotics-protein and protein-disease networks, for both identification and validation [16-20]. Outcome will be greatly empowered by the coupling of 3D culture systems to LC-MS/MS or NMR platforms.


  1. van Rijswijk M, Beirnaert C, Caron C, …, Klapa MI, …,Ν. Μoschonas, …, M. Reczko, …, Zanetti G, Steinbeck C. 2017. The future of metabolomics in ELIXIR [version 2; referees: 3 approved]. F1000Research 2017, 6(ELIXIR):1649.
  2. Maga-Nteve C., Klapa MI. 2016. Streamlining GC-MS metabolomic analysis using the M-IOLITE software suite. IFAC-PapersOnLine 49:286-288.
  3. H. Kanani, P. K. Chrysanthopoulos and M.I. Klapa. 2008. Standardizing GC-MS metabolomics. J. Chromatogr. B 871: 191-201 (in special issue “Hyphenated Techniques for Global Metabolite Profiling”)
  4. Papadimitropoulos M-E., Vasilopoulou C., Maga-Nteve Ch. & Klapa MI. 2018. Untargeted GC-MS metabolomics. Methods Mol. Biol., Vol. 1738, G Theodoridis et al. (Eds): Metabolic Profiling, SpringerNature
  5. Maga-Nteve C, Vasilopoulou CG, Constantinou C, Margarity M, Klapa MI. 2017. Sex-comparative study of mouse cerebellum physiology under adult-onset hypothyroidism: The significance of GC–MS metabolomic data normalization in meta-analysis. J Chromatography B 1041:158-166
  6. Tooulakou G, Giannopoulos A, Nikolopoulos D, Bresta P, Dotsika E, Orkoula MG, Kontoyannis CG, Fasseas C, Liakopoulos G, Klapa MI, Karabourniotis G. Alarm Photosynthesis: Calcium Oxalate Crystals as an Internal CO2 Source in Plants. Plant Physiol. 171: 2577-85.
  7. Vasilopoulou CG, Margarity M, Klapa MI. 2016. Metabolomic Analysis in Brain Research: Opportunities and Challenges. Front Physiol. 7:183.
  8. Gkourogianni A., Kosteria I., Telonis A., Margeli, A., Mantzou E., Konsta, M., Loutradis D., Mastorakos G., Papassotiriou, I., Klapa M.I., Kanaka-Gantenbein C., Chroussos G.P. 2014. Plasma Metabolomic Profiling Suggests Early Indications for Predisposition to Latent Insulin Resistance in Children Conceived by ICSI PLOS One 9: e94001
  9. S I. Vernardis, C.T. Goudar, M.I. Klapa. 2013. Metabolic profiling reveals that time related physiological changes in mammalian cell perfusion cultures are bioreactor scale independent. Metabolic Engineering 19:1-9
  10. K. Spagou, G.Theodoridis, I. Wilson, N. Raikos, P. Greaves, R.Edwards, B. Nolan & M.I. Klapa. 2011. A GC-MS metabolomic profiling study of plasma samples from mice on low- and high- fat diets. J Chromatogr B Analyt Technol Biomed Life Sci.879:1467-75 (in Special Issue “Derivatization Techniques in Analysis”)
  11. C. Constantinou, PK Chrysanthopoulos, M Margarity, MI Klapa. 2011. GC-MS metabolic analysis reveals significant alterations in cerebellar metabolic physiology in a mouse model of adult onset hypothyroidism. J. Proteome Res.
  12. Gioutlakis A, Klapa MI, Moschonas NK. 2017. PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS ONE 12(10): e0186039.
  13. M.I. Klapa, K. Tsafou, E. Theodoridis, A. Tsakalides and N.K. Moschonas. 2013. Reconstruction of the experimentally supported human protein interactome: what can we learn? BMC Systems Biology 7:96
  14. Pilalis E, Koutsandreas T, Valavanis I, Athanasiadis E, Spyrou G, Chatziioannou A. KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments. Computational and Structural Biotechnology Journal, 2015, 13, 248–255
  15. Moutselos K, Kanaris I, Chatziioannou A, Maglogiannis I, Kolisis FN: KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database. BMC Bioinformatics 2009, 10:324
  16. Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal. 2016 Dec 31;14:177-84.
  17. Katsila T, Konstantinou E, Lavda I, Malakis H, Papantoni I, Skondra L, Patrinos GP. Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease. EBioMedicine. 2016 Mar 31;5:40-5.
  18. Pirhaji L, Milani P, Leidl M, Curran T, Avila-Pacheco J, Clish CB, White FM, Saghatelian A, Fraenkel E. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nature Methods. 2016 Sep 1;13(9):770-6.
  19. Tullis P. The man who can map the chemicals all over your body. Nature. 2016 Jun;534(7606):170-2.
  20. Williams EG, Wu Y, Jha P, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N, Aebersold R. Systems proteomics of liver mitochondria function. Science. 2016 Jun 10;352(6291): aad0189.