Vasilopoulou Catherine G, Margarity Marigoula, Klapa Maria I
Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT)Patras, Greece; Human and Animal Physiology Laboratory, Department of Biology, University of PatrasPatras, Greece.
Human and Animal Physiology Laboratory, Department of Biology, University of Patras Patras, Greece.
Front Physiol. 2016 May 24;7:183. doi: 10.3389/fphys.2016.00183. eCollection 2016.
Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.
新陈代谢是分子生理学的一个基本组成部分,阐明代谢途径的结构和调节对于全面了解细胞功能以及理解其功能障碍的潜在机制至关重要。因此,在各种生理条件下量化准确的代谢网络活动图谱是系统生物学在许多生物学应用背景下的主要目标之一。特别是对于中枢神经系统,代谢网络活动分析可以极大地增进我们对哺乳动物大脑复杂结构和神经疾病机制的了解,从而设计出有效的治疗方法。代谢组学已成为对小分子量代谢物浓度谱的高通量定量分析,这些代谢物在代谢反应中作为反应物和产物,并且作为参与许多生物过程的蛋白质的调节分子。因此,通过同时分析数十到数百种具有病理生理和药理学意义的分子,代谢谱提供了一种代谢活动指纹。一般来说,代谢组学的应用正处于标准化阶段,而在脑研究中,建立标准化程序面临的挑战更为突出。在这篇综述中,我们支持代谢组学在脑研究中的价值。此外,我们展示了设计和开展可靠的脑代谢组学研究的挑战,除其他参数外,该研究必须考虑性别差异以及脑生理学在区域差异中表现出的复杂性。我们最后提出了克服这些挑战的方法,并设计出一项能产生可重复且一致结果的研究。