帕金森病患者多巴胺能神经元突触和非突触组分生物能量差异的约束建模。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-06-05 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1594330
Xi Luo, Diana C El Assal, Yanjun Liu, Samira Ranjbar, Ronan M T Fleming
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引用次数: 0

摘要

新出现的证据表明,突触末端和躯体之间不同的代谢特征,特别是生物能量差异,可能导致帕金森病患者多巴胺能神经元的选择性易感性。方法:为了研究代谢差异,在对照和PD条件下,我们建立了四个热力学通量一致的代谢模型,代表突触和非突触(体细胞)成分。我们分析了这些模型之间生物能量特征和代谢物交换的差异,以探索多巴胺能神经元选择性易感性的潜在机制。在PD模型中进行生物能量拯救分析,以确定潜在的治疗靶点,以减轻观察到的能量衰竭和代谢功能障碍。结果:所有模型都预测,氧化磷酸化在较低的能量需求下发挥重要作用,而糖酵解在能量需求超过线粒体限制时起主导作用。与非突触PD模型相比,突触PD模型预测线粒体能量贡献更低,对复合物I抑制的敏感性更高。两种PD模型都预测赖氨酸和乳酸的摄取减少,表明这些成分之间的代谢过程协调一致。相比之下,蛋氨酸和尿素摄取减少只在突触性PD模型中预测,而组氨酸和甘油酸摄取减少只在非突触性PD模型中预测。此外,线粒体鸟氨酸转氨酶反应(ORNTArm)的通量增加,将氧戊二酸和鸟氨酸转化为谷氨酸-5-半醛和谷氨酸,预测可以挽救生物能量衰竭并改善突触和非突触PD模型的代谢物交换。讨论:模型之间预测的ATP贡献差异突出了这些神经元成分之间的生物能量差异,从而促成了PD中观察到的选择性脆弱性。观察到的代谢物交换的差异反映了这些神经元成分之间不同的代谢模式。此外,线粒体鸟氨酸转氨酶被预测为突触性和非突触性PD模型的潜在生物能量救援靶点。需要进一步的研究来验证多巴胺能神经元不同组成部分的功能障碍机制,并探索PD患者的靶向治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of dopaminergic neurons in Parkinson's disease.

Introduction: Emerging evidence suggests that different metabolic characteristics, particularly bioenergetic differences, between the synaptic terminal and soma may contribute to the selective vulnerability of dopaminergic neurons in patients with Parkinson's disease (PD).

Method: To investigate the metabolic differences, we generated four thermodynamically flux-consistent metabolic models representing the synaptic and non-synaptic (somatic) components under both control and PD conditions. Differences in bioenergetic features and metabolite exchanges were analyzed between these models to explore potential mechanisms underlying the selective vulnerability of dopaminergic neurons. Bioenergetic rescue analyses were performed to identify potential therapeutic targets for mitigating observed energy failure and metabolic dysfunction in PD models.

Results: All models predicted that oxidative phosphorylation plays a significant role under lower energy demand, while glycolysis predominates when energy demand exceeds mitochondrial constraints. The synaptic PD model predicted a lower mitochondrial energy contribution and higher sensitivity to Complex I inhibition compared to the non-synaptic PD model. Both PD models predicted reduced uptake of lysine and lactate, indicating coordinated metabolic processes between these components. In contrast, decreased methionine and urea uptake was exclusively predicted in the synaptic PD model, while decreased histidine and glyceric acid uptake was exclusive to the non-synaptic PD model. Furthermore, increased flux of the mitochondrial ornithine transaminase reaction (ORNTArm), which converts oxoglutaric acid and ornithine into glutamate-5-semialdehyde and glutamate, was predicted to rescue bioenergetic failure and improve metabolite exchanges for both the synaptic and non-synaptic PD models.

Discussion: The predicted differences in ATP contribution between models highlight the bioenergetic differences between these neuronal components, thereby contributing to the selective vulnerability observed in PD. The observed differences in metabolite exchanges reflect distinct metabolic patterns between these neuronal components. Additionally, mitochondrial ornithine transaminase was predicted to be the potential bioenergetic rescue target for both the synaptic and non-synaptic PD models. Further research is needed to validate these dysfunction mechanisms across different components of dopaminergic neurons and to explore targeted therapeutic strategies for PD patients.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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