From Spectrometric Data to Metabolic Networks: An Integrated Quantitative View of Cell Metabolism

Oscar Yanes (1,2)
(1) Universitat Rovira i Virgili & IISPV, Spain (2) Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Spain

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Oscar Yanes (Presenter) CIBER & URV & IISPV

Presenter Bio: Oscar Yanes received his Ph.D. degree in Biochemistry (2006) from the Universitat Autonoma de Barcelona. In 2007 he became Research Associate in Gary Siuzdak’s lab at The Scripps Research Institute. Since 2011 he coordinates the Metabolomics Platform of the Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), he is affiliated member at the IRB Barcelona and assistant professor at the Universitat Rovira i Virgili where he also leads his own research group (www.yaneslab.com).
He has long experience in developing new technologies and methods, computational tools and applications in LC-MS, GC-MS and NMR-based metabolomics. With >60 publications and >4.000 citations, his lab now focuses on understanding metabolic dysregulations in disease through integrating MS and NMR-based metabolomics with other omic platforms.

Relevant Financial Disclosures (within past 24 months) Committee/Board/Advisory Board Spanish Society for Mass Spectrometry Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

INTRODUCTION: Metabolite profiling – or metabolomics – presents a powerful global approach to measure shifts in metabolites as functional readouts of cellular state. Metabolites can complement upstream biochemical information obtained from genes, transcripts, and proteins and advance our understanding of how cells are altered in health and disease. Unfortunately, the great success in the characterization of genes, transcripts and proteins has currently no parallel in metabolites. Metabolomic studies are revealing large numbers of naturally occurring metabolites that cannot be characterized because their chemical structures and spectrometric data are not available. This is preventing metabolomics from evolving as fast as other omic sciences, and thus it is restricting the integration of multiple layers of omic data to gain more insights into the emergence of observed phenotypes.
OBJECTIVES: To fill this gap, new experimental approaches based on mass spectrometry (MS), and innovations in bioinformatics to enable a comprehensive analysis of cellular metabolites are needed.
RESULTS: Here I will present novel computational tools for: 1) identifying and quantifying metabolites from reconstructed GC-MS, LC-MS and MALDI-MS spectral profiles; 2) the structural characterisation of unknown metabolites; and 3) the use of isotopically labeled metabolites to study the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell. Finally, I will show that the integrated analysis of proteomics and metabolomics data through metabolic networks provides a new conceptual structure for an alternative quantitative and predictive description of cell metabolism.