RNA-seq profiling of human non-tumorigenic and cancer breast cell lines

This study has allowed us to develop INTEGRATE (Model-based multi-omics data INTEGRAtion to characterize mulTi-level mEtabolic regulation) pipeline in order to accurately characterize the landscape of metabolic regulation in different biological samples, starting from metabolomics data and transcriptomic data. INTEGRATE first computes differential expression of reactions from transcriptomics data and, then, it exploits constraint-based modelling to predict how the global relative differences in expression are expected to translate into consistent differences in metabolic fluxes. To improve model predictions, this pipeline optionally sets constraints also on selected extracellular fluxes, according to exo-metabolomic data. In parallel, INTEGRATE uses intracellular metabolomic datasets and the mass action law formulation to predict how differences in substrate availability translate into differences in metabolic fluxes (metabolic regulation only), neglecting enzymatic activity. The intersection of the two output datasets discriminates fluxes regulated at the metabolic and/or gene expression level. The effectiveness of this pipeline was demonstrated 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.