分子遺伝医学

Enhancing Transcriptomics-Based Model-Driven Performance via Stoichiometric Gene-to-Reaction Associations: Metabolic Reprogramming in Prostate Cancer

Abstract

Igor Marín de Mas

Genome-scale metabolic models (GSMMs) have been widely used to study the molecular mechanisms underlying a variety of diseases with a strong metabolic component such as diabetes or cancer. GSMMs incorporate logical rules associating genes, proteins, and reactions (GPR rules), enabling the integration of either proteomics or transcriptomics. However, current GPR formulation do not account for the necessary stoichiometry to describe the number of transcript copies that are necessary to generate a catalytically active enzyme, which limits our understanding of how gene expression modulates metabolism. Thus, in this short commentary article, we introduce the stoichimetric-GPR (S-GPR) concept, presented in Marin de Mas I et al. The novel S-GPRs associations were implemented to study the metabolic reprogramming in DU145 prostate cancer cells associated to the chronic exposure to the endocrine disruptor Aldrin. The results showed that S-GPRs outperformed previous approaches by significantly improving the GSMM predictions. Thus, the novel S-GPR concept that we have developed enables a more precise integration of transcriptomics data into GSMM-based methods and can be extended to proteomics data, with an important impact in the environmental and the clinical fields.