Researchers here outline how it is possible to use what is known of gene regulatory networks in order to design better approaches to slow aging. Proteins interact with one another, and feedback loops involving interactions and changes in expression among many proteins determine each aspect of cell behavior. The key realization is that in such a complex system, one has to think about these networks rather than any one individual protein in order to maximize the chance of producing a useful approach to altering cell behavior.
Earlier aging studies focused on individual genes or pathways in isolation and measured lifespan as a static endpoint. As a result, how aging-related genes interact with one another and how these gene regulatory networks (GRNs) operate dynamically to drive aging remain significant unanswered challenges. GRNs consist of nodes, that symbolize genes or regulatory elements, and edges, that depict the interactions or regulatory connections between these nodes. Highly connected nodes at the center of a GRN are the major orchestrators of the response of a cell to stimuli.
The dynamics of these nodes can often be explained by focusing on a few key local interactions, namely subgraphs. Network motifs are recurrent sub-GRNs, typically including up to four nodes, that have characterized behaviors. Network motifs can be as simple as positive autoregulation which ensures the sustained activity of a node. By contrast, mutual inhibition between two nodes can lead to two distinct cell fates where the system stabilizes in one of two states based on initial conditions. The negative feedback loop is a motif that is especially crucial for ensuring homeostasis, and is activated by deviations from a set point that trigger mechanisms to counteract those changes. These motifs are observed in many GRNs and are reinforced by redundant and compensatory pathways to increase the resilience of the system to perturbations.
Decoding the emergent behavior of aging-related GRNs sets the stage for rational design of new interventional strategies to mitigate age-related diseases and promote healthy longevity. However, the intricate nature of aging-related processes cannot be fully understood through traditional reductionist methods. Instead, systems-level approaches designed to analyze the nonlinear dynamics of gene circuits are required. In addition, such network-based approaches can be naturally integrated with synthetic biology to reveal the design principles of prolongevity strategies.
Link: https://doi.org/10.1...tcb.2025.02.006
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