Moolamalla S T R, Vinod P K
Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
Comput Biol Med. 2020 Oct;125:103994. doi: 10.1016/j.compbiomed.2020.103994. Epub 2020 Sep 17.
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.
由于症状和遗传风险因素存在重叠，鉴别神经精神疾病具有挑战性。患有这些疾病的人面临着个人和职业方面的挑战。了解疾病状态下大脑代谢的失调情况有助于进行有效的诊断，并基于代谢情况制定治疗策略。在本研究中，我们利用转录组数据和基于约束的建模方法，重建了三种主要神经精神疾病——精神分裂症（SCZ）、双相情感障碍（BD）和重度抑郁症（MDD）的代谢网络。我们将来自六项独立研究的大脑转录组数据与最近一个全面的全基因组规模代谢模型Recon3D进行了整合。对重建网络的分析揭示了神经精神疾病中过氧化物酶体 - 线粒体 - 高尔基体轴上的通量水平变化。我们还提取了可区分这三种神经精神疾病的报告代谢物和代谢途径。我们发现了脂肪酸氧化、芳香族和支链氨基酸代谢、胆汁酸合成、糖胺聚糖合成与修饰以及磷脂代谢方面的差异。此外，我们预测了针对每种疾病可将疾病代谢状态转变为健康代谢状态的网络扰动。这些分析提供了精神分裂症、双相情感障碍和重度抑郁症代谢变化的局部和全局视图，可能具有临床意义。
