Ricostruzione di modelli metabolici su scala genomica specifici degli astrociti utilizzando un nuovo metodo di approssimazione K

Astrocytes play a fundamental role in maintaining brain energy homeostasis and supporting neuronal functions. Metabolic dysfunction in astrocytes has been indicated as the primary cause in different neurological diseases. Despite the growing interest in astrocyte research and technological advancements, the metabolic state of astrocyte in physiological and pathological conditions, as well as their relationship to neurological disorders remains to be elucidated. In this study, we reconstructed sample-specific genome scale metabolic models of human astrocytes using transcriptomic data from individual samples. A novel K-approximation method was applied to construct astrocyte-specific genome scale metabolic models (GEM)s, using Human1 as a base model, with the purpose of identifying all active reactions in astrocyte cell, and generating biological feasible models. To achieve this purpose, we reconstructed seven-astrocyte specific GEMs and assessed their corresponding biological feasibility by comparing key metabolic pathways known to be well established in astrocytic functions. These pathways included major metabolic subsystems such as carbohydrate metabolism, oxidative phosphorylation, neurotransmitter recycling, lipid metabolism and Redox detoxification. Escher maps were manually created for each core metabolic pathway to visualize active reactions and identify pathway completeness. The models captured canonical features of astrocyte metabolism, including robust glycolysis, lactate production, glutamate and GABA metabolism, and ROS scavenging. Interestingly, metabolic differences across samples were observed, especially in aging-related pathways such as the pentose phosphate pathway and neurotransmitter recycling, highlighting inter-individual variability and potential age-associated decline. This study provides a reliable framework for assessing astrocyte metabolic activity and supports the utility of the K-approximation method as a viable reconstruction approach. This work establishes a foundation for future modeling efforts targeting astrocyte function in aging and neurodegenerative diseases, where metabolic reprogramming is known to play a key role.