Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders.

Abstract Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model Recon3D. The analysis of the reconstructed network revealed the flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications.