Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

中文翻译：

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对双相情感障碍对前额叶皮层代谢影响的计算机分析确定了 GABA、谷氨酸-谷氨酰胺循环、能量代谢和氨基酸合成途径的改变。

双相情感障碍（BP）是一种终生的精神疾病，常常扰乱患者的日常生活。它的特点是不稳定和周期性的情绪变化，导致患者在情绪、精力、活动水平、注意力和执行日常任务的能力方面表现出不寻常的变化。BP 是一种主要的精神疾病，但仍未得到充分治疗。双相情感障碍的病因和神经机制尚不明确，诊断仍以精神科检查为主，且该障碍的不稳定特性使诊断具有挑战性。确定该疾病的分子机制可能会提高诊断和治疗率。研究了患者的单核苷酸多态性 (SNP) 和转录组谱以及被认为与双相情感障碍相关的信号通路。在这里，我们提出了一种计算方法，该方法使用来自双相情感障碍患者和健康对照者的公开可用的转录组数据。除了统计分析，数据还与基因组规模的代谢模型和蛋白质-蛋白质相互作用网络相结合。健康个体和双相情感障碍患者根据他们的代谢特征进行比较。我们假设双相情感障碍的能量代谢改变与氨基酸代谢和神经元-星形胶质细胞交换反应的扰动有关。由于氨基酸代谢的变化，神经递质及其从神经元的分泌以及神经元和星形胶质细胞之间的代谢交换途径（例如谷氨酰胺-谷氨酸循环）也发生了改变。还观察到带负电荷 (-1) KIV 和 KMV 分子的变化，这表明大脑中的电荷平衡在躁郁症中发生了很大变化。由于这一事实，我们还假设带正电的锂离子除了对神经传递的影响外，还可以稳定神经元中被扰乱的电荷平衡。据我们所知，我们的方法是独一无二的，因为它是第一个在神经精神病学研究中使用基因组规模代谢模型的研究。这表明大脑中的电荷平衡在躁郁症中发生了很大的变化。由于这一事实，我们还假设带正电的锂离子除了对神经传递的影响外，还可以稳定神经元中被扰乱的电荷平衡。据我们所知，我们的方法是独一无二的，因为它是第一个在神经精神病学研究中使用基因组规模代谢模型的研究。这表明大脑中的电荷平衡在躁郁症中发生了很大的变化。由于这一事实，我们还假设带正电的锂离子除了对神经传递的影响外，还可以稳定神经元中被扰乱的电荷平衡。据我们所知，我们的方法是独一无二的，因为它是第一个在神经精神病学研究中使用基因组规模代谢模型的研究。