GEMCAT – A new algorithm for gene expression-based prediction of metabolic alterations

Abstract

ABSTRACTThe conclusive interpretation of multi-omics datasets obtained from high throughput approaches is an important prerequisite to understand disease-related physiological changes and to predict biomarkers in body fluids. We here present a Gene Expression-based Metabolite Centrality Analysis Tool, GEMCAT, a new genome scale metabolic modelling algorithm. GEMCAT enables integration of transcriptomics or proteomics data to predict changes in metabolite concentrations which can be verified by targeted metabolomics. In addition, GEMCAT allows to trace measured and predicted metabolic changes back to the underlying alterations in gene expression or proteomics and thus enables functional interpretation and integration of multi-omics data. We demonstrate the predictive capacity of GEMCAT on two datasets, one using RNA sequencing data and metabolomics from an engineered human cell line with a functional deletion of the mitochondrial NAD-transporter and another using proteomics and metabolomics measurements from patients with inflammatory bowel disease.