Genetic information flow in the Hallmarks of Aging: from system-level analytics to mechanistic interventions

摘要 虽然衰老被理解为细胞和组织功能的系统性恶化，但它仍然是一个复杂的问题。 与特定标志相关，由López-Otín等人于2013年首次提出。因为这些特征 传统上被孤立地研究，它们之间的关系在很大程度上仍然未知，包括 标志是否出现在不同的时间或以分层的方式，或者是否某些标志（或基因） 其中，“以人为本”是关键。我们之前量化了细胞功能的系统性恶化 使用统计物理方法，表明转录调控中传达的信息 网络随着年龄的增长而减少。在这里，我们建议调查如何确定这种信息损失是由 利用全系统分析和机械干预措施，评估各种标志之间的相互作用。我们特别 目的（1）绘制遗传信息流的轨迹，层次结构和影响，以适应每一个特征， 随着时间的推移和根据性别老化;（2）机械验证年龄对转移的影响， 遗传信息。我们将使用来自以下组织的单细胞RNA-seq数据的组合来实现这些目标： 年轻、中年和老年雄性和雌性小鼠的骨骼肌细胞;计算分析建立在 统计物理学和信息论的工具;使用局部基因抑制/过度表达 shRNA方法;以及肌肉功能完整性的体内评估。这种创新的方法将 由一个跨学科的团队进行，包括干细胞生物学家（PI），理论物理学家（co-PI）， 计算生物学家（co-I）和分子生物学家（co-I），其广度增强了可行性， 影响力和成功的可能性。拟议工作的结果将是一个定量和机械的 了解标志相关基因和过程如何稳定调控网络相互作用 在一个有机体的生命周期中。最终，我们预计，这一框架可以扩展到预测 个人的生物学年龄，并制定战略，以重新编程基因网络的目标， 更年轻的表型。

ABSTRACT Although aging is understood as a system-wide deterioration of cell and tissue function, it is nevertheless associated with specific hallmarks, first introduced by López-Otín et al. in 2013. Because these hallmarks have traditionally been studied in isolation, the relationships between them remain largely unknown, including whether hallmarks emerge at different times or in a hierarchical manner, or whether certain hallmarks (or genes therein) are the critical drivers of aging. We previously quantified the system-wide deterioration of cell function using a statistical physics approach, showing that the information communicated in transcriptional regulatory networks decreases with age. Here, we propose to investigate how this information loss is determined by the interplay among hallmarks, using both system-wide analysis and mechanistic interventions. Specifically, we aim (1) to map the trajectory, hierarchy, and impact of genetic information flow for each of the hallmarks of aging over time and according to sex; and (2) to mechanistically validate the effect of age on the transfer of genetic information. We will accomplish these aims using a combination of single cell RNA-seq data from skeletal muscle cells in young, middle-aged, and old male and female mice; computational analysis built on tools from statistical physics and information theory; targeted genetic inhibition/overexpression using local shRNA approaches; and in vivo assessment of muscle functional integrity. This innovative approach will be carried out by an interdisciplinary team comprising a stem cell biologist (PI), a theoretical physicist (co-PI), a computational biologist (co-I), and a molecular biologist (co-I), the breadth of which enhances feasibility, impact, and likelihood for success. The outcome of the proposed work will be a quantitative and mechanistic understanding of how hallmark-associated genes and processes (de)stabilize regulatory network interactions over an organism’s lifespan. Ultimately, we anticipate that this framework can be extended to predict an individual’s biological age and to develop strategies to reprogram gene networks with the goal of preserving a more youthful phenotype.