Buffering Mechanisms in Aging: A systems approach towards uncovering the genetic component of aging

Abstract

An unrealized potential to understand the genetic basis of aging in humans, is to consider the immense survival advantage of the rare individuals who live 100 years or more. The Longevity Gene Study was initiated in 1998 at the Albert Einstein College of Medicine to investigate longevity genes in a selected population: the ''oldest old'' Ashkenazi Jews, 95 years of age and older, and their children. The study proved the principle that some of these subjects are endowed with longevity-promoting genotypes. Here we reason that some of the favorable genotypes act as mechanisms that buffer the deleterious effect of age-related disease genes. As a result, the frequency of deleterious genotypes may increase among individuals with extreme lifespan because their protective genotype allows diseaserelated genes to accumulate. Thus, studies of genotypic frequencies among different age groups can elucidate the genetic determinants and pathways responsible for longevity. Borrowing from evolutionary theory, we present arguments regarding the differential survival via buffering mechanisms and their target age-related disease genes in searching for aging and longevity genes. Using more than 1,200 subjects between the sixth and eleventh decades of life (at least 140 subjects in each group), we corroborate our hypotheses experimentally. We study 66 common allelic site polymorphism in 36 candidate genes on the basis of their phenotype. Among them we have identified a candidatebuffering mechanism and its candidate age-related disease gene target. Previously, the beneficial effect of an advantageous cholesteryl ester transfer protein (CETP-VV) genotype on lipoprotein particle size in association with decreased metabolic and cardiovascular diseases, as well as with better cognitive function, have been demonstrated. We report an additional advantageous effect of the CETP-VV (favorable) genotype in neutralizing the deleterious effects of the lipoprotein(a) (LPA) gene. Finally, using literature-based interaction discovery methods, we use the set of longevity genes, buffering genes, and their age-related target disease genes to construct the underlying subnetwork of interacting genes that is expected to be responsible for longevity. Genome wide, high-throughput hypothesis-free analyses are currently being utilized to elucidate unknown genetic pathways in many model organisms, linking observed phenotypes to their underlying genetic mechanisms. The longevity phenotype and its genetic mechanisms, such as our buffering hypothesis, are similar; thus, the experimental corroboration of our hypothesis provides a proof of concept for the utility of high-throughput methods for elucidating such mechanisms. It also provides a framework for developing strategies to prevent some age-related diseases by intervention at the appropriate level.

A generalized measurement equation and van Cittert-Zernike theorem for wide-field radio astronomical interferometry

“…One can also include antennae that sample the magnetic field, and an arrangement of electric and magnetic antennae can be constructed so as to sample the full electromagnetic field at a point. Such antennae are called electromagnetic vector-sensors (Nehorai & Paldi 1991;Bergman et al 2005). Interferometers based on combinations of electric or magnetic antennae elements can be described using the electromagnetic vC-Z relations given in appendix A.…”

Genetic Determinants of Human Health Span and Life Span: Progress and New Opportunities

“…Studies performed at the Albert Einstein College of Medicine were based upon populations of Ashkenazi Jews and the following considerations [84]. First, exceptional longevity is obviously a rare phenotype (∼1/10,000 individuals live to the age of 94–110).…”

“…As a result, the frequencies of deleterious genotypes may, paradoxically, be increased among individuals with extreme life spans. This may explain why the cholesteryl ester transfer protein (CETP-VV) genotype appears to exhibit an additional advantageous effect—the neutralization of the deleterious effects of the lipoprotein(a) (Lp(a)) gene [84]. Such buffering effects cannot be ascribed to genetic linkage.…”

Natural Variation and Neuromechanical Systems

“…On the other hand, switching between two environments in a rapid and patterned manner may encourage the expression of previously hidden variation [14]. Such results have been found in animal and bacterial models in response to environmental fluctuations, and may also apply in a limited sense to human neuromuscular systems.…”

“…The take-home message is that physiological regulation forced by a specific neuromechanical technology can yield differential results in different individuals. Overall, the interaction between transcriptional regulation, metabolic networks, and environmental stimuli can trigger the additional expression of variation in situ [14].…”