Department of Science and High Technology, University of Insubria, Busto Arsizio and Como, Italy; Center of Neuroscience, University of Insubria, Busto Arsizio and Como, Italy.
Department of Science and High Technology, University of Insubria, Busto Arsizio and Como, Italy; Center of Neuroscience, University of Insubria, Busto Arsizio and Como, Italy.
Handb Clin Neurol. 2023;192:73-86. doi: 10.1016/B978-0-323-85538-9.00007-9.
Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.
神经退行性疾病是多因素的。这意味着几个遗传、表观遗传和环境因素促成了它们的出现。因此,为了未来对这些高度流行疾病的管理,有必要改变视角。如果采用整体观点,表型(临床病理收敛)是从蛋白质之间功能相互作用的复杂系统的扰动中出现的(系统生物学发散)。系统生物学自顶向下的方法从无偏见地收集通过一种或多种组学技术生成的数据集开始,目的是识别参与表型(疾病)产生的网络和组件,通常没有任何可用的先验知识。自上而下方法的原理是,对实验扰动有类似反应的分子成分在某种程度上是功能相关的。这使得即使在不需要广泛了解研究过程的情况下,也可以研究复杂且相对特征较差的疾病。在本章中,将采用全局方法来理解神经退行性变,特别关注两种最常见的疾病,阿尔茨海默病和帕金森病。最终目的是区分疾病亚型(即使临床表现相似),为这些疾病的患者开展精准医学的未来。