Resilience integrates concepts in aging research

Abstract

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

Keywords: Biological sciences; Cell biology; Evolutionary biology; Health sciences; Molecular biology.

Graphical abstract

Box 1 Figure. Simple schemas illustrating the ability of a phenotypic state to return to stable state following a perturbation. States could include, for example, immune reactions, balance, cognitive decision making, molecular network structure, etc. (A) Above: a deeper valley indicates greater resilience, which is associated with faster recovery and a return to the prestressed or perturbed state. Below: lower resilience is marked by a shallower valley, which indicates a slower recovery and increased possibility that the system may not return to the prestressed state. (B) Above: If multiple systems are resilient and recover quickly, we expect to observe less correlation across systems. Below: A decline in resilience will lead to slower return rates to equilibrium upon small perturbations. Delay within one element (e.g., glucose level) may impinge on other elements (e.g., gait), leading to a rising cross-correlation between competent and compromised states.

Figure 1

A resilience-centered framework for understanding aging and its evolution. Variation in aging among individuals is shaped by variation in genotype, environment, and the interaction between the two. Typically missing from this relationship (Equation 1, main text) are the mechanisms by which genes and environment shape phenotype (Molecular & Cellular Biology (MCB), Resilience and Aging & Age-Related Disease (AARD)). Here we propose linking resilience with evolutionary selection and molecular domains directly in a framework that incorporates intrinsic molecular, cellular, and physiological processes – the systems biology of an organism. Briefly, resilience affects and is affected by the intrinsic environment (MCB and AARD) in the form of change in the state of the endophenotypes. At the same time, through its dependence on resilience, homeostasis of the internal environment is vulnerable to aging and disease. Together with aging and disease themselves, resilience influences how selection shapes endophenotypic traits. These states and processes are developed in more detail in Figures 2, 3, and 4 and explanations in the main text.

Figure 2

Evolution, Biology of Aging, and Environmental research disciplines are instrumental in the framework of resilience. Each discipline engages a breadth of approaches to uncover causal factors involved in regulating the pace and the consequence of organismal aging. Each discipline connects to resilience (Figure 1), such that measuring resilience and its upstream causes and downstream consequences can ultimately provide a more comprehensive, interdisciplinary understanding of the forces that shape aging at the individual and population levels. Causal relations in each discipline are discussed in detail in the main text.

Figure 3

Tools and measurements will help understand interactions over scales (A) and underlying regulatory mechanisms (B), linking aging and resilience. (A) Above: Knowledge of how networks connect across scales is uneven. Larger circles reflect greater biological detail and conceptual understanding. Below: Layers of networks confer essential traits for whole body homeostasis and plasticity. Integrity of the system relies on the correct response being executed at each level. Circles and rectangles are color-coded to link methods and disciplines (above) to the underlying biology (below). (B) Interactions among molecules within distinct regulatory mechanisms create a complex set of interdependent outcomes when a system is perturbed. Time course studies of highly defined stimulus and response (i.e., resilience) could delineate the series of causes and effects and how these programs might be sensitive to age.

Figure 4

Uncovering aging biology through the study of resilience. Step I: A nonlethal, low intensity challenge is introduced. Step II: The response to stimulus is quantified at high resolution using multiple platforms, from pre-stimulus to peak response to eventual resolution. Step III: Data integration defines the multidimensional cellular response, effectors in stress, and resilience pathways that are identified using bioinformatics approaches. Step IV: Longevity-associated pathways are superimposed on the resilience framework to identify multidimensional mechanistic links between longevity-associated pathways and resilience.