Interdisciplinary topics in gerontology最新文献

There is not one systems biology of aging, but two. Though aging can evolve in either sexual or asexual species when there is asymmetric reproduction, the evolutionary genetics of aging in species with frequent sexual recombination are quite different from those arising when sex is rare or absent. When recombination is rare, selection is expected to act chiefly on rare large-effect mutations, which purge genetic variation due to genome-wide hitchhiking. In such species, the systems biology of aging can focus on the effects of large-effect mutants, transgenics, and combinations of such genetic manipulations. By contrast, sexually outbreeding species maintain abundant genetic polymorphism within populations. In such species, the systems biology of aging can examine the genome-wide effects of selection and genetic drift on the numerous polymorphic loci that respond to laboratory selection for different patterns of aging. An important question of medical relevance is to what extent insights derived from the systems biology of aging in model species can be applied to human aging.

Animals and plants have biological clocks that help to regulate circadian cycles, seasonal rhythms, growth, development and sexual maturity. If aging is not a stochastic process of attrition but is centrally orchestrated, it is reasonable to suspect that the timing of senescence is also influenced by one or more biological clocks. Evolutionary reasoning first articulated by G. Williams suggests that multiple, redundant clocks might influence organismal aging. Some aging clocks that have been proposed include the suprachiasmatic nucleus, the hypothalamus, involution of the thymus, and cellular senescence. Cellular senescence, mediated by telomere attrition, is in a class by itself, having recently been validated as a primary regulator of aging. Gene expression is known to change in characteristic ways with age, and in particular DNA methylation changes in age-related ways. Herein, I propose a new candidate for an aging clock, based on epigenetics and the state of chromosome methylation, particularly in stem cells. If validated, this mechanism would present a challenging but not impossible target for medical intervention.

Aging is a consequence of an organism's evolution, where specific traits that lead to the organism's development eventually promote aged phenotypes or could lead to age-related diseases. In this sense, one theory that broadly explored development and its association to aging is the developmental aging theory (DevAge), which also encompasses most known age-associated theories. Thus, we employed different systems biology tools to prospect developmental and aging-associated networks for human and murine models for evolutionary comparison. The gathered data suggest a model where proteins related to inflammation, development, epigenetic mechanisms and oxygen homeostasis coordinate the interplay between development and aging. Moreover, the mechanism also appears to be evolutionary conserved in both mammalian models, further corroborating the DevAge molecular model.

Aging is associated with immunosenescence and accompanied by a chronic inflammatory state which contributes to metabolic syndrome, diabetes and their cardiovascular consequences. Risk factors for cardiovascular diseases (CVDs) and diabetes overlap, leading to the hypothesis that both share an inflammatory basis. Obesity is increased in the elderly population, and adipose tissue induces a state of systemic inflammation partially induced by adipokines. The liver plays a pivotal role in the metabolism of nutrients and exhibits alterations in the expression of genes associated with inflammation, cellular stress and fibrosis. Hepatic steatosis and its related inflammatory state (steatohepatitis) are the main hepatic complications of obesity and metabolic diseases. Aging-linked declines in expression and activity of endoplasmic reticulum molecular chaperones and folding enzymes compromise proper protein folding and the adaptive response of the unfolded protein response. These changes predispose aged individuals to CVDs. CVDs and endothelial dysfunction are characterized by a chronic alteration of inflammatory function and markers of inflammation and the innate immune response, including C-reactive protein, interleukin-6, TNF-α, and several cell adhesion molecules are linked to the occurrence of myocardial infarction and stroke in healthy elderly populations and patients with metabolic diseases.

Aging is a systemic process which progressively manifests itself at multiple levels of structural and functional organization from molecular reactions and cell-cell interactions in tissues to the physiology of an entire organ. There is ever increasing data on biomedical relevant network interactions for the aging process at different scales of time and space. To connect the aging process at different structural, temporal and spatial scales, extensive systems biological approaches need to be deployed. Systems biological approaches can not only systematically handle the large-scale datasets (like high-throughput data) and the complexity of interactions (feedback loops, cross talk), but also can delve into nonlinear behaviors exhibited by several biological processes which are beyond intuitive reasoning. Several public-funded agencies have identified the synergistic role of systems biology in aging research. Using one of the notable public-funded programs (GERONTOSYS), we discuss how systems biological approaches are helping the scientists to find new frontiers in aging research. We elaborate on some systems biological approaches deployed in one of the projects of the consortium (ROSage). The systems biology field in aging research is at its infancy. It is open to adapt existing systems biological methodologies from other research fields and devise new aging-specific systems biological methodologies.

This chapter will briefly address the history of systems biology and complexity theory and its use in understanding the dynamics of aging at the 'omic' level of biological organization. Using the idea of treating a biological organism like a network, we will examine how network mathematics, particularly graph theory, can provide deeper insight and can even predict potential genes and proteins that are related to the control of organismal life span. We will begin with a review of the history of network analysis at the cellular level and follow that by an introduction to the various commonly used network analysis variables. We will then demonstrate how these variables can be used to predict potential targets for experimental analysis. Lastly, we will close with some of the challenges that network methods face.

With modern medicine and an awareness of healthy lifestyle practices, people are living longer and generally healthier lives than their ancestors. These successes of modern medicine have resulted in an increasing proportion of elderly in society. Research groups around the world have investigated the contribution of gut microbial communities to human health and well-being. It was established that the microbiota composition of the human gut is modulated by lifestyle factors, especially diet. The microbiota composition and function, acting in concert with direct and indirect effects of habitual diet, is of great importance in remaining healthy and active. This is not a new concept, but until now the scale of the potential microbiota contribution was not appreciated. There are an estimated ten times more bacteria in an individual than human cells. The bacterial population is relatively stable in adults, but the age-related changes that occur later in life can have a negative impact on host health. This loss of the adult-associated microbiota correlates with measures of markers of inflammation, frailty, co-morbidity and nutritional status. This effect may be greater than that of diet or in some cases genetics alone. Collectively, the recent studies show the importance of the microbiota and associated metabolites in healthy aging and the importance of diet in its modulation.

From the perspective of systems biology, melatonin is relevant to aging in multiple ways. As a highly pleiotropic agent, it acts as a modulator and protectant of mitochondrial electron flux, a potent antioxidant that supports the redox balance and prevents excessive free radical formation, a coregulator of metabolic sensing and antagonist of insulin resistance, an immune modulator, a physiological hypnotic and, importantly, an orchestrating chronobiotic. It entrains central and peripheral circadian clocks and is required for some high-amplitude rhythms. The circadian system, which controls countless functions, is composed of many cellular oscillators that involve various accessory clock proteins, some of which are modulated by melatonin, e.g. sirtuin 1, AMP-dependent protein kinase, and protein kinase Cα. Aging and age-related diseases are associated with losses in melatonin secretion and rhythm amplitudes. The dynamic properties of aging processes deserve particular attention. This concerns especially two vicious cycles, one of peroxynitrite formation driven by inflammation or overexcitation, another one of inflammaging driven by the senescence-associated secretory phenotype, and additionally the loss of dynamics in a deteriorating circadian multioscillator system.

Computational modelling is a key component of systems biology and integrates with the other techniques discussed thus far in this book by utilizing a myriad of data that are being generated to quantitatively represent and simulate biological systems. This chapter will describe what computational modelling involves; the rationale for using it, and the appropriateness of modelling for investigating the aging process. How a model is assembled and the different theoretical frameworks that can be used to build a model are also discussed. In addition, the chapter will describe several models which demonstrate the effectiveness of each computational approach for investigating the constituents of a healthy aging trajectory. Specifically, a number of models will be showcased which focus on the complex age-related disorders associated with unhealthy aging. To conclude, we discuss the future applications of computational systems modelling to aging research.