Data-driven 13C-fluxomics towards ab initio reconstruction of metabolic networks

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Butin, Noémie | Millard, Pierre | Frainay, Clément | Le Grégam, Loïc | Jourdan, Fabien | Schmitt, Uwe | Bellvert, Floriant | Kiefer, Patrick | Portais, Jean-Charles

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International audience. The reconstruction of metabolic networks is a valuable tool for understanding the operation of biological systems (cells, tissues, organisms). It provides an in-depth understanding of the behavior of a particular organism, by identifying the link between the structure of the network, its organization and its functional properties. The reconstruction process is usually based on the genome, providing a comprehensive model that represents an organism’s entire set of biochemical reactions. Alternative reconstruction approaches, such as experimental approaches, aim at reconstructing the active metabolic network of an organism - i.e. the part of the network which is truly active in a given condition – based on direct observation of the metabolome, in a way that is orthogonal and complementary to in silico reconstruction approaches. In this work, we have developed a new data-driven approach for ab initio metabolic reconstruction based on isotopic tracing experiments. For many years, isotopic labeling with stable isotope tracers, typically a 13C-labeled carbon source, has been used to study the structure and the activity of metabolic pathways and networks. The approach we developed, named IsoMet for Isotopic driven Metabolic reconstruction, is based on the interpretation of untargeted metabolomics data collected in 13C-labeling dynamic experiments. This approach aims at providing direct access to the active metabolic network of an organism, specific to a cell type in a given context, without prior considerations. IsoMet shares concepts with non-stationary 13C-fluxomics approaches, such as label propagation simulation and flux calculation. The basic principle of IsoMet consists to construct and test the ability of different network topologies to explain observed labeling dynamic data, leading to the construction of active metabolic subnetworks. The proposed approach is generic and can be applied to various stable isotope tracers (13C, 15N, etc) used to investigate metabolism and different type of isotopic measurement (MS, MS/MS, NMR, etc). It is intended to be applicable to various biological models, ranging from poorly known organisms to complex systems (tissues, mammalian cells, …).

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