Santiago Jose A, Bottero Virginie, Potashkin Judith A
Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States.
Front Aging Neurosci. 2017 May 29;9:166. doi: 10.3389/fnagi.2017.00166. eCollection 2017.
Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer's (AD), Parkinson's (PD) and Huntington's diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.
神经退行性疾病很少由单个基因突变引起，而是受遗传、表观遗传和环境因素的综合影响。诸如RNA测序等新兴的高通量技术在解读神经退行性疾病的分子格局方面发挥了重要作用，然而，对如此大量数据的解读仍然是一项挑战。网络生物学已成为一个强大的平台，可整合多个组学数据，以便在健康和疾病背景下全面探索分子网络。在这篇综述文章中，我们重点介绍网络生物学方法的最新进展，尤其侧重于脑网络，这些进展为导致包括阿尔茨海默病（AD）、帕金森病（PD）和亨廷顿病（HD）在内的最常见神经退行性疾病的分子机制提供了见解。我们讨论了使用来自不同组织的多组学数据的整合方法如何在识别生物标志物和治疗靶点方面具有价值。此外，我们还讨论了网络医学领域在将基于网络的研究结果转化为用于个性化医学应用的临床可行工具方面所面临的挑战。
