From an evolutionary selection perspective, the reasons why a regulated terminal program leading to an organism's demise was chosen over a program of immortality are puzzling. An evolutionary conjecture for the selection bias toward a terminal program might be that in the postreproductive phase of an organism's life cycle, the selection pressure is lessened or no longer present.
The lessening of selection pressure is the result of mortality caused by extrinsic harm in early life; in the wild, most organisms do not live long enough to reach old age. The absence of selection pressure leads to the formation of an organ system that permits increasing entropic deregulation of cellular mechanism and default death. However, to reach the reproductive phase, it is likely that cellular mechanisms evolved to slow or regulate aging, thus increasing the chances of an organism attaining reproductive phase at the opportune moment. These age-regulating processes would counter harmful internal and environmental effects and contribute to a selection bias.
Therefore, the extent to which these life-sustaining processes can repair and maintain an organism from increasing entropy and environmental harm constitutes the lifespan of an organism. Under this light, aging is a process that can be described at the cellular level. Aging and death are intimately linked. Aging can be defined as an equation with the probability of death as one of its variables.
A second variable is changes in the phenotype, which occur because of degradation of age-regulating cellular processes.
The phenotypic changes of aging, however, are distinct from the diseases of aging in that phenotypic changes affect all individuals, whereas diseases of aging affect only a subpopulation. This is highlighted, for example, by the fact that the maximum human lifespan has not been extended substantially despite the quantum improvements in medical sciences that have been made in this century.
The medical advances have affected the diseases of aging, but not the aging process itself. Recent advances in human genetics and molecular biology have greatly increased our understanding of aging. These advances include identification of genes important in aging, genetic studies 1 , 2 of germline diseases that cause the appearance of premature aging, an improved molecular understanding of the diseases of aging, 3 and others. In this review, a few selected models of aging that illustrate the molecular methods and techniques used in current research in aging are discussed.
These models include cellular damages caused by reactive oxygen species ROS , the genetic program, and genomic instability. The idea of cellular oxidative damage caused by ROS, free radicals generated during metabolic processes, contributing to the aging process has been a popular model, first proposed in the s. The organelle damages caused by ROS are indiscriminate, but constant, over an organism's lifetime.
The model proposes that the cumulative damaging effects of ROS lead to eventual cellular breakdown and death. The extent to which an organism may survive depends on the countermeasures programmed within a cell to decrease the damaging effects of ROS. The cellular solutions to ROS are the genes that encode for antioxidants. Advances in molecular biology have allowed the creation of transgenic animals, in which targeted genes can be either overexpressed or deleted.
Such animals allow analysis of targeted genes at the molecular level in a milieu of living macroscopic environment. Direct support for the free radical hypothesis of aging was substantiated with the creation of transgenic Drosophila flies small 2-winged flies used in genetic research overexpressing the antioxidants copper-zinc superoxide dismutase SOD and catalase. Superoxide dismutases are enzymes that regulate intracellular superoxide anion levels. These transgenic flies lived one third longer and displayed a delayed loss in physical performance compared with wild-type control flies.
The remarkable results obtained from these flies have raised the suggestion that regulation of aging may be a relatively simple process, at least for these flies. Similar experiments using mice, a more complex animal model, were performed by other molecular biologists; transgenic knockout mice target genes are deleted for SOD1 , SOD3 , and glutathione peroxidase were created.
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Interestingly, no rapid aging process was found in the knockout mice, although the knockout mice were more susceptible to motor neuron loss after an injury. Despite the disappointing results obtained from SOD transgenic knockout mice, studies of other molecular biologists further strengthened the notion that genomic DNA is liable to free radical attack, resulting in cumulative genetic defects with aging.
Mutant mice with a single targeted mutation in the gene encoding the p66 shc protein lived one-third longer than wild-type animals. The p66 shc protein is a cytoplasmic signal transducer that mediates signals from mitogenic cell surface receptors to the Ras protein. Mutant mice had increased cellular resistance to agents that cause oxidative damage, consistent with the results of in vitro experiments, suggesting that increased resistance to oxidative damage was responsible for their longevity.
Other evidence supporting the connection between ROS and aging was found in genetic manipulation experiments using Caenorhabditis elegans C elegans is a 1-mm-long soil nematode found in temperate regions. For example, a mutation of the age-1 gene, a negative regulator of SOD, caused the mutant C elegans to live 2 times longer than the wild-type controls.
Taken together with the SOD and p66 shc protein knockout mice findings, it is probable that genes responsible for reducing ROS are only part of counterbalancing mechanisms that forestall aging to a limited extent. It is also probable that ROS would have the most harmful age-promoting effect on postmitotic cells ie, cells that do not regenerate, such as neurons and cardiomyocytes because the harmful effects are cumulative. However, for cells that replicate rapidly, such as enterocytes that endure the reduced effects of ROS during their short lifetime, regulation of aging by genes encoding for antioxidants may play a minimal role.
For the premitotic cells, a second model proposes that genetic programs that are responsible for an organism's development are more important in regulating aging. Many experiments involving genetic programs and pathways relevant to aging have been performed on C elegans. Caenorhabditis elegans proceed through 4 larval stages before reaching adulthood, the reproductive stage. Under adverse environmental conditions, such as starvation, they enter a dormant state called dauer larva stage.
With improvement in environmental conditions, adult development is resumed. As an adult, C elegans live for a few weeks, compared with 6 months in the dauer diapause stage.source url
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To understand the genetic developmental control of dauer transition, hence regulation of aging that leads to longevity in the dauer stage, the genes that determine the transition into the dauer stage have been studied extensively. The dauer formation daf gene and age-1 are examples of genes involved in the transition into the dauer state. Initial data on animal and humans shows changes in methylation with aging that are tissue-specific Greally, Atzmon , and extend to animal and human models who develop age-related disease after intrauterine growth retardation Einstein , and in brains of both sick and healthy elderly subjects Zukin.
Education Research Health. The Paul F. Holcomb, V. Ku80 deletion suppresses spontaneous tumors and induces a pmediatedDNA damage response. CAN Park, J. After constructing the gene coexpression network, we extracted the subnetworks consisting of protein coding genes.
And only the largest component of the subnetwork was used in our analysis Supplementary Material, Table S11A. Based on the suggestion given by the authors of COXPRESdb personal communication , we used both , a normal threshold, and 30, a stringent one, for the mutual rank to construct the coexpression network. Here, we only show the results using as the network threshold, since different thresholds would not change our conclusions. We studied the tissue specificity of gene expression among the four sets of genes that we analyzed, using a gene expression data set generated with tissue samples of human individuals representing 32 different tissues Based on the annotation of these categories, we systematically compared the differences among different gene sets.
The significance of the observed values of a network characteristic for a group of genes can be assessed by comparing their central tendency—i. We used degree-preserved resampling to evaluate the dependency of a network characteristic of a group of genes on their degree distribution.
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To generate the degree-preserved null distribution, we first randomly select from the network a group of genes, which are of the same number and have a similar degree distribution as the analyzed genes. We then calculate the central tendency of the characteristic for the random genes.
Many iterations of this resampling procedure generate the degree-preserved null distribution of the characteristic for resampled genes. We randomize the background network by randomly rewiring interactions among genes in it Each time, we arbitrarily select two interactions e. In this study, to generate such random networks we rewired the original network as many times as the number of interactions in it. To evaluate the robustness of the networks, we measured the number of disjoint components and also the size of the components after randomly removing a certain number of genes from the networks A smaller number of components and a bigger component size indicate the network is more robust against removal of genes from the network.
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Using this approach, we evaluated the robustness of the aging subnetwork, which is enriched with interactions among hubs, against removal of genes from the network. In comparison, we also evaluated the robustness of simulated networks that contain the same number of nodes and edges and have much less interactions among hubs but instead more interactions between hubs and non-hubs—i.
To generate such networks, we first randomly selected an interaction between aging hubs e.
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The lower the degrees of a pair of interacting non-hubs the higher chance to select the interaction between them. By repeating this procedure a large number of times, we obtained a random network that resembles the network background. When compared with the original network, it has fewer interactions among hubs and more interactions between hubs and non-hubs.
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