1 Department of Dermatology, Boston University School of Medicine , Boston, Massachusetts.
2 Department of Ecology and Life Safety, Samara National Research University , Samara, Russia .
Antioxid Redox Signal. 2018 Oct 1;29(10):985-1002. doi: 10.1089/ars.2017.7163. Epub 2017 Oct 10.
Aging is a complex trait that is influenced by a combination of genetic and environmental factors. Although many cellular and physiological changes have been described to occur with aging, the precise molecular causes of aging remain unknown. Given the biological complexity and heterogeneity of the aging process, understanding the mechanisms that underlie aging requires integration of data about age-dependent changes that occur at the molecular, cellular, tissue, and organismal levels. Recent Advances: The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, and automated imaging techniques provides researchers with new opportunities to understand the mechanisms of aging. Using these methods, millions of biological molecules can be simultaneously monitored during the aging process with high accuracy and specificity.
Although the ability to produce big data has drastically increased over the years, integration and interpreting of high-throughput data to infer regulatory relationships between biological factors and identify causes of aging remain the major challenges. In this review, we describe recent advances and survey emerging omics approaches in aging research. We then discuss their limitations and emphasize the need for the further development of methods for the integration of different types of data.
Combining omics approaches and novel methods for single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed with aging and disease states. Together, these studies are expected to provide a better understanding of the aging process and could provide insights into the pathophysiology of many age-associated human diseases. Antioxid. Redox Signal. 29, 985-1002.
衰老是一种复杂的特征,受遗传和环境因素的综合影响。尽管已经描述了许多与衰老相关的细胞和生理变化,但衰老的确切分子原因仍不清楚。鉴于衰老过程的生物学复杂性和异质性,理解衰老的机制需要整合关于分子、细胞、组织和机体水平上与年龄相关的变化的数据。
高通量技术的发展,如下一代测序、蛋白质组学、代谢组学和自动化成像技术,为研究人员提供了新的机会来理解衰老的机制。使用这些方法,可以在衰老过程中以高精度和特异性同时监测数百万种生物分子。
尽管多年来产生大数据的能力有了显著提高,但整合和解释高通量数据以推断生物因素之间的调控关系并确定衰老的原因仍然是主要挑战。在这篇综述中,我们描述了衰老研究中最近的进展和新兴的组学方法。然后,我们讨论了它们的局限性,并强调需要进一步开发整合不同类型数据的方法。
将组学方法和单细胞分析的新方法与系统生物学工具相结合,将允许构建相互作用网络,并研究这些网络如何随着衰老和疾病状态而受到干扰。这些研究有望更好地理解衰老过程,并为许多与年龄相关的人类疾病的病理生理学提供见解。抗氧化。氧化还原信号。29,985-1002。