[back to the DMCM’24 Programme]

Title:
Systems biological insight into insulin resistance-associated metabolic alterations

Authors:
Alise Zagare, Janis Kurlovics, Egils Stalidzans, Giuseppe Arena, Laura Neises, Johannes Meiser, Rejko Krüger and Jens Christian Schwamborn

Abstract:
Systems biology’s role in complex disease modelling has lately increased. Data integration and computational modelling approaches as a part of systems biology enable more comprehensive insight into complex disease pathogenic molecular mechanisms. Moreover, computational modelling serves as a valuable tool for generating hypotheses and providing guidance for subsequent experimental strategies. In our study, we focus on insulin resistance, which is an important risk factor for many disease development, including neurodegenerative diseases. By integrating transcriptomic data into the Recon3 human metabolic network, our objective is to predict the metabolic pathways most affected by insulin resistance in the human midbrain-specific organoid model. Identifying metabolic processes primarily influenced by insulin resistance could enhance our understanding of the mechanisms through which insulin resistance may contribute to the development of Parkinson’s disease, which is one of the most common progressive neurodegenerative diseases. The predictions from metabolic modelling indicated notable changes in lipid metabolism and impaired glycolysis in midbrain organoids with insulin resistance. Importantly, experimental validation of these modeling-derived predictions affirmed the accuracy and effectiveness of our computational predictions.