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

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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.