Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. First, metabolic regulation controls metabolic fluxes (i.e., the rate of individual metabolic reactions) through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. A second regulatory layer sets the maximal theoretical level for each enzyme-controlled reaction by controlling the expression level of the catalyzing enzyme. In isolation, high-throughput data, such as metabolomics and transcriptomics data do not allow for accurate characterization of the hierarchical regulation of metabolism outlined above. Hence, they must be integrated in order to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we proposes INTEGRATE, a computational pipeline that integrates metabolomics (intracellular and optionally extracellular) and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation
