Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA.
Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA; Mathematical, Computational and Systems Biology (MCSB) Program, University of California, Irvine, CA 92697, USA.
Neurobiol Dis. 2021 Dec;160:105530. doi: 10.1016/j.nbd.2021.105530. Epub 2021 Oct 9.
Over the years, genetic studies have identified multiple genetic risk variants associated with neurodegenerative disorders and helped reveal new biological pathways and genes of interest. However, genetic risk variants commonly reside in non-coding regions and may regulate distant genes rather than the nearest gene, as well as a gene's interaction partners in biological networks. Systems biology and functional genomics approaches provide the framework to unravel the functional significance of genetic risk variants in disease. In this review, we summarize the genetic and transcriptomic studies of Alzheimer's disease and related tauopathies and focus on the advantages of performing systems-level analyses to interrogate the biological pathways underlying neurodegeneration. Finally, we highlight new avenues of multi-omics analysis with single-cell approaches, which provide unparalleled opportunities to systematically explore cellular heterogeneity, and present an example of how to integrate publicly available single-cell datasets. Systems-level analysis has illuminated the function of many disease risk genes, but much work remains to study tauopathies and to understand spatiotemporal gene expression changes of specific cell types.
多年来，遗传研究已经确定了多个与神经退行性疾病相关的遗传风险变异体，这有助于揭示新的生物学途径和感兴趣的基因。然而，遗传风险变异体通常位于非编码区域，可能调节远距离基因，而不是最近的基因，以及基因在生物网络中的相互作用伙伴。系统生物学和功能基因组学方法为揭示遗传风险变异体在疾病中的功能意义提供了框架。在这篇综述中，我们总结了阿尔茨海默病和相关tau 病的遗传和转录组研究，并重点介绍了进行系统水平分析以探究神经退行性变背后的生物学途径的优势。最后，我们强调了单细胞方法的多组学分析的新途径，这为系统地探索细胞异质性提供了无与伦比的机会，并提供了一个如何整合公开可用的单细胞数据集的示例。系统水平分析已经阐明了许多疾病风险基因的功能，但仍有许多工作要做，以研究 tau 病，并了解特定细胞类型的时空基因表达变化。
