Systems Biology and the Physical Foundations of Aging - Bullets
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|Systems Biology and Aging|
Control of mammalian aging: growth hormone vs IGF-1.
It is now well established that the conserved insulin/insulin-like growth factor signaling (IIS) pathway controls aging and longevity in organisms aging from C. elegans to mice and likely from yeast to humans. In mammals, interpretation of the available data is complicated by the fact that the insulin-like growth factor 1 (IGF-1) is a part of a highly complex somatotropic axis consisting of growth hormone (GH), IGF-1, IGF binding proteins and proteases that degrade these proteins. Although IGF-1 mediates many of the effects of GH, some of the IGF-1 and GH actions do not overlap. Importantly, mutations affecting GH signaling have, in general, greater and more consistent effects on longevity than mutations affecting IGF-l or downstream IIS signaling. We propose that this is due to at least two factors: (i) reduced GH signaling produces selective (organ-specific) rather than global reduction of IGF-1 signaling, and (ii) reduced GH , but not IGF-1 signaling reduces insulin resistance.
Time permitting, I would also be glad to bring up some intriguing and controversial questions related to body size vs longevity. These questions were and continue to be raised by Tom Samaras and are related to various sets of experimental, epidemiological and demographic data: Would we be better of if we were smaller ? Would future of planet Earth and human populations benefit from reduced height of people ?
Computational Systems Biology in Aging - A Search for Consistencies across Scales
Systems biology can address complexities at different levels of biological organization, which should provide an overall consistent picture, specifically when applied to the biology of aging. At current, the ubiquitous vicious cycle model, related to the mitochondrial theory of aging, does not fit observations on higher levels of the aging physiome, questioning its validity. Instead, we propose an adaptive-response model, based on a computational integration of the complex interplay between positive feed-forward, accelerating mechanisms during cellular aging and feed-back, stress-adaptive mechanism. We will discuss opportunities arising from of the new model but also address aging paradigms challenged by the approach.
Questions: What are the mechanisms, predictors, and preventive interventions for the development of frailty in old age.
Aging is often associated with the emergence of syndromes that cannot be attributed to any one organ system or disease, but are attributable to multiple pathologic processes, external stressors, and the interactions among them. Frailty is one example of a morbid syndrome that results from multiple structural and functional abnormalities in the body that impair one’s ability to adapt to the stresses of everyday life. The degradation of physiologic systems that underlies frailty is manifest by a loss of complexity in the dynamic behavior of these systems. For example, the normal beat-to-beat fluctuations of heart rate in a healthy, young individual are highly complex, with a scale invariant, fractal-like structure that can be quantified with a number of non-linear statistics. The complexity of the heart rate time series declines with aging and frailty, presumably due to the loss of various physiologic inputs that operate over different time scales (e.g., the autonomic nervous system, respiration, hormonal influences, circadian rhythms, external temperatures, activity levels, etc.). Similarly, the complexity of blood pressure fluctuations, respiratory cycles, center-of-pressure displacements, hormonal rhythms, and electroencephalographic waves declines with aging and may predispose elderly people to the onset of frailty. In response to these observations, I have proposed that the loss of complexity in various systems with aging is due to the degradation and disconnection of anatomic structures and physiologic inputs, ultimately impairing an organism’s ability to adapt to stress.
In support of this notion, recent work in our laboratory has demonstrated that the complexity of balance dynamics (center of pressure movements on a balance platform) declines linearly with the loss of vision, peripheral sensation, and both. Furthermore, this loss of balance complexity is associated with greater postural sway during the stress of a simultaneous cognitive task. Finally, this loss of complexity may be partially reversible with interventions such as Tai Chi, which can improve the integrated function of multiple systems that affect balance (e.g. muscle strength, motor coordination, sensation, and respiration).
In answer to the questions posed above, frailty in old age may result from the degradation of highly integrated physiologic networks. Nonlinear measures that can quantify the complexity of these networks may serve as biomarkers for reduced adaptive capacity and predictors of frailty. Multicomponent interventions that can improve the function of interacting physiologic systems may be able to prevent or reverse frailty in vulnerable elderly patients.
Possible topics (will choose one):
- Phylogenetic conservation of aging mechanisms
A variety of laboratory studies suggest that a few well-studied mechanisms of aging have been preserved over evolutionary time. This claim might be true. Or it might be a lab artifact. Are we underestimating the number of genes that influence senescence in nature? Are we overestimating the magnitude of effect of individual genes that influence senescence in nature? Can systems biology approaches help us to identify new pathways that influence senescence?
- A network perspective on natural selection and senescence
Evolutionary theory suggests that senescence occurs because of the decline in the strength of selection with age. But the strength and nature of selection is likely to differ not only among age classes, but also among traits and among genes. I am interested in the possibility that we can use a network framework to think about how the strength of selection might vary among biological components.
- Networks and epistasis
Molecular genetic studies of aging often focus on epistatic interactions as a means of understanding pathways. Do network interactions translate to functional epistatic interactions?
- Senescence as network failure
A great deal of work in systems biology has focused on the issue of robustness-what it is, and how it evolves or is maintained. If we think of senescence as the age-related decline in robustness, can we bring new theoretical insights to this old problem.
One of the hallmarks of aging is increased vulnerability where the apparent homeostatic point is closer to critical thresholds with increasing age because of decreased available physiologic reserves. That is a smaller challenge or perturbation can cause an older person to decompensate. Frailty is when the reserves are so depleted that trivial challenges produce decompensation. One reason why the available reserves are decreased with age is that many are already being invoked to compensate for age-related changes just to maintain homeostasis. In the heart, reserves used to increase cardiac output for exercise or overload in the young are in use at rest in the old. The development of a challenge has a dynamic in itself, interacting with the organism to potentially nip them in the bud. The “decompensation” is also dynamic from individual to individual, many individuals decompensate in the same way (ie., become confused or fall) no matter what the challenge. The apparent homeostasis is also dynamic with small fluctuations occurring at all times and possible abilities to decrease vulnerability by increase the available physiologic reserves.
How and When Does Aging Start? By MicroRNA Up-regulation, in Mid-life.
In general, aging is marked by symptomatic decline and deterioration, manifested in the last period of any living organism’s life span. Thus, the diagnostic and therapeutic approaches to the debilities of the elderly have been dominated by retrospective correction of abnormalities, the “iceberg” culmination of pre- symptomatic changes at the molecular level long before the onset of old age. We recently identified in mouse brain and liver the up-regulation of key microRNAs appearing as early as mid-life. MicroRNAs are a major subset of noncoding RNAs, which do not code any genes but rather control gene expression at the post- transcriptional level. These small RNAs of 18-22 nucleotide bases long functionally suppress gene expression by binding either at the coding region of the message and thus degrading them, or at the 3’- untranslated region (UTR) to inhibit translation. In general, a single microRNA may target multiple genes, and any individual gene may be silenced by several microRNAs. Moreover, the silencing action often operates as “dimmer switch” with partial suppression to control the balance of expression between the miRNA and its targets. This operational modality presents a dream scenario for a system biology approach to investigating the following issues:
1. The temporal and spatial regulation of the docking for a given microRNA to its multiple targets, thus
allowing the versatile suppression of gene expression to occur;
2. The universal versus tissue-specific miRNA up-regulation during mid-life, contributing to both systemic and organ-specific age-dependent decline;
3. The hierarchical order among lead miRNAs for a given signaling network; and
4. Genetic versus epigenetic control of the miRNA-directed regulation for health span.