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

摘要 尽管衰老被理解为全系统的细胞和组织功能的恶化，但实际上 与特定标志相关，最先由L等人介绍。2013年。因为这些标志有 传统上是孤立地研究它们之间的关系，它们之间的关系在很大程度上仍然未知，包括 标志是否在不同的时间或以等级方式出现，或者某些标志(或基因)是否 其中)是老龄化的关键驱动因素。我们之前量化了全系统细胞功能的恶化 使用统计物理方法，显示在转录调控中交流的信息 网络随着年龄的增长而减少。在这里，我们建议调查这种信息损失是如何由 利用全系统分析和机械干预，在标志之间相互作用。具体来说，我们 目的(1)绘制遗传信息流的轨迹、层次和影响 随着时间的推移和性别的不同而老化；以及(2)机械地验证年龄对迁移的影响 遗传信息。我们将使用单细胞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；建立在 来自统计物理和信息论的工具；使用局部有针对性的基因抑制/过度表达 ShRNA方法；以及肌肉功能完整性的活体评估。这种创新的方法将是 由一个由干细胞生物学家(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量化和机械化的 理解标志相关基因和过程(De)如何稳定调控网络相互作用 在生物体的生命周期内。最终，我们预计这个框架可以扩展到预测一个 并制定策略对基因网络进行重新编程，目的是保存 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.