Front End Innovation, Beiersdorf AG, Hamburg, Germany.
Institute for Bioinformatics, University Medicine Greifswald, Greifswald, Germany.
Aging (Albany NY). 2020 Jun 18;12(12):12393-12409. doi: 10.18632/aging.103499.
In recent years, reports of non-linear regulations in age- and longevity-associated biological processes have been accumulating. Inspired by methodological advances in precision medicine involving the integrative analysis of multi-omics data, we sought to investigate the potential of multi-omics integration to identify distinct stages in the aging progression from human skin tissue. For this we generated transcriptome and methylome profiling data from suction blister lesions of female subjects between 21 and 76 years, which were integrated using a network fusion approach. Unsupervised cluster analysis on the combined network identified four distinct subgroupings exhibiting a significant age-association. As indicated by DNAm age analysis and Hallmark of Aging enrichment signals, the stages captured the biological aging state more clearly than a mere grouping by chronological age and could further be recovered in a longitudinal validation cohort with high stability. Characterization of the biological processes driving the phases using machine learning enabled a data-driven reconstruction of the order of Hallmark of Aging manifestation. Finally, we investigated non-linearities in the mid-life aging progression captured by the aging phases and identified a far-reaching non-linear increase in transcriptional noise in the pathway landscape in the transition from mid- to late-life.
近年来，关于年龄和寿命相关生物过程中非线性调控的报道越来越多。受精准医学中涉及多组学数据综合分析的方法学进展的启发，我们试图研究多组学整合在鉴定人类皮肤组织衰老进展中不同阶段的潜力。为此，我们从 21 至 76 岁女性受试者的水疱抽吸损伤中生成了转录组和甲基化组谱数据，并使用网络融合方法进行了整合。对联合网络进行无监督聚类分析，确定了四个具有显著年龄相关性的不同亚群。如 DNAm 年龄分析和衰老特征信号富集所示，这些阶段比单纯按年龄分组更能清晰地捕捉到生物学衰老状态，并且在具有高稳定性的纵向验证队列中可以很好地恢复。使用机器学习对驱动这些阶段的生物学过程进行特征分析，可以实现对衰老特征表现顺序的基于数据的重构。最后，我们研究了衰老阶段所捕获的中年期衰老进展中的非线性，并在从中年期到老年期的转变过程中，在通路图谱中发现了转录噪声的深远的非线性增加。
