Bioinformatics Lab, National Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, Brazil.
School of Medicine and Surgery, Federal University of the State of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.
PLoS Comput Biol. 2021 May 10;17(5):e1008956. doi: 10.1371/journal.pcbi.1008956. eCollection 2021 May.
A major factor contributing to the etiology of depression is a neurochemical imbalance of the dopaminergic and serotonergic systems, which is caused by persistently high levels of circulating stress hormones. Here, a computational model is proposed to investigate the interplay between dopaminergic and serotonergic-kynurenine metabolism under cortisolemia and its consequences for the onset of depression. The model was formulated as a set of nonlinear ordinary differential equations represented with power-law functions. Parameter values were obtained from experimental data reported in the literature, biological databases, and other general information, and subsequently fine-tuned through optimization. Model simulations predict that changes in the kynurenine pathway, caused by elevated levels of cortisol, can increase the risk of neurotoxicity and lead to increased levels of 3,4-dihydroxyphenylaceltahyde (DOPAL) and 5-hydroxyindoleacetaldehyde (5-HIAL). These aldehydes contribute to alpha-synuclein aggregation and may cause mitochondrial fragmentation. Further model analysis demonstrated that the inhibition of both serotonin transport and kynurenine-3-monooxygenase decreased the levels of DOPAL and 5-HIAL and the neurotoxic risk often associated with depression. The mathematical model was also able to predict a novel role of the dopamine and serotonin metabolites DOPAL and 5-HIAL in the ethiology of depression, which is facilitated through increased cortisol levels. Finally, the model analysis suggests treatment with a combination of inhibitors of serotonin transport and kynurenine-3-monooxygenase as a potentially effective pharmacological strategy to revert the slow-down in monoamine neurotransmission that is often triggered by inflammation.
导致抑郁症病因的一个主要因素是多巴胺能和血清素能系统的神经化学失衡,这是由循环应激激素水平持续升高引起的。在这里,提出了一个计算模型来研究皮质醇血症下多巴胺能和血清素-犬尿氨酸代谢之间的相互作用及其对抑郁症发作的影响。该模型被构造成一组具有幂律函数的非线性常微分方程。参数值是从文献中报道的实验数据、生物数据库和其他一般信息中获得的,并通过优化进行了微调。模型模拟预测,皮质醇水平升高引起的犬尿氨酸途径的变化会增加神经毒性的风险,并导致 3,4-二羟基苯乙酸(DOPAL)和 5-羟基吲哚乙醛(5-HIAL)水平升高。这些醛有助于α-突触核蛋白聚集,并可能导致线粒体碎片化。进一步的模型分析表明,同时抑制 5-羟色胺转运体和犬尿氨酸-3-单加氧酶可以降低 DOPAL 和 5-HIAL 的水平,并降低与抑郁症相关的神经毒性风险。该数学模型还能够预测多巴胺和血清素代谢物 DOPAL 和 5-HIAL 在抑郁症病因中的新作用,这是通过增加皮质醇水平来实现的。最后,模型分析表明,联合使用 5-羟色胺转运体和犬尿氨酸-3-单加氧酶抑制剂作为一种潜在有效的药理学策略,可能逆转炎症常引发的单胺神经递质传递的减缓。