Genomic Approach to Understand the Association of DNA Repair with Longevity and Healthy Aging Using Genomic Databases of Oldest-Old Population

Population dynamics model for aging

The chronological age used in demography describes the linear evolution of the life of a living being. The chronological age cannot give precise information about the exact developmental stage or aging processes an organism has reached. On the contrary, the biological age (or epigenetic age) represents the true evolution of the tissues and organs of the living being. Biological age is not always linear and sometimes proceeds by discontinuous jumps. These jumps can be negative (we then speak of rejuvenation) or positive (in the event of premature aging), and they can be dependent on endogenous events such as pregnancy (negative jump) or stroke (positive jump) or exogenous ones such as surgical treatment (negative jump) or infectious disease (positive jump). The article proposes a mathematical model of the biological age by defining a valid model for the two types of jumps (positive and negative). The existence and uniqueness of the solution are solved, and its temporal dynamic is analyzed using a moments equation. We also provide some individual-based stochastic simulations.

Research on Werner Syndrome: Trends from Past to Present and Future Prospects

A rare and autosomal recessive premature aging disorder, Werner syndrome (WS) is characterized by the early onset of aging-associated diseases, including shortening stature, alopecia, bilateral cataracts, skin ulcers, diabetes, osteoporosis, arteriosclerosis, and chromosomal instability, as well as cancer predisposition. WRN, the gene responsible for WS, encodes DNA helicase with a 3′ to 5′ exonuclease activity, and numerous studies have revealed that WRN helicase is involved in the maintenance of chromosome stability through actions in DNA, e.g., DNA replication, repair, recombination, and epigenetic regulation via interaction with DNA repair factors, telomere-binding proteins, histone modification enzymes, and other DNA metabolic factors. However, although these efforts have elucidated the cellular functions of the helicase in cell lines, they have not been linked to the treatment of the disease. Life expectancy has improved for WS patients over the past three decades, and it is hoped that a fundamental treatment for the disease will be developed. Disease-specific induced pluripotent stem (iPS) cells have been established, and these are expected to be used in drug discovery and regenerative medicine for WS patients. In this article, we review trends in research to date and present some perspectives on WS research with regard to the application of pluripotent stem cells. Furthermore, the elucidation of disease mechanisms and drug discovery utilizing the vast amount of scientific data accumulated to date will be discussed.

How Important Are Genes to Achieve Longevity?

Several studies on the genetics of longevity have been reviewed in this paper. The results show that, despite efforts and new technologies, only two genes, APOE and FOXO3A, involved in the protection of cardiovascular diseases, have been shown to be associated with longevity in nearly all studies. This happens because the genetic determinants of longevity are dynamic and depend on the environmental history of a given population. In fact, population-specific genes are thought to play a greater role in the attainment of longevity than those shared between different populations. Hence, it is not surprising that GWAS replicated associations of common variants with longevity have been few, if any, as these studies pool together different populations. An alternative way might be the study of long-life families. This type of approach is proving to be an ideal resource for uncovering protective alleles and associated biological signatures for healthy aging phenotypes and exceptional longevity.

GSTT1, GSTP1 and XPC genes are associated with longevity in an Italian cohort

Longevity is a complex process controlled by environmental and genetic factors. We evaluated the association of seven drug metabolising and DNA-repair gene polymorphisms with longevity in an Italian cohort. A sample of 756 subjects aged 18-98 was genotyped for CYP1A1 (rs1048943, A>G), GSTM1 (rs 1183423000, presence/absence), GSTT1 (rs1601993659, presence/absence), GSTP1 (rs1695, A>G), XRCC1 (rs1799782, C>T), XRCC1 (rs25489, A>G) and XPC (rs2228001, A>C) gene polymorphisms. The association between the studied gene polymorphisms and longevity was evaluated by dividing the sample into three age groups: 18-50, 51-85, and 86-98. We observed a significant decrease in the frequency of the GSTT1 null, GSTP1 G and XPC C alleles in the oldest group with respect to the youngest one. We also obtained the same results when dividing the sample into 18-85 and 86-98 age groups. The general linear model analyses confirmed a significant decreasing trend with age of the above mentioned alleles. We hypothesised that these minor alleles, being important in the sensitivity against the development of different types of cancer, may reflect a reduced life-expectancy in carrier subjects and may explain their significantly lower frequency observed among subjects belonging to the oldest age group.

Aging Fits the Disease Criteria of the International Classification of Diseases

The disease criteria used by the World Health Organization (WHO) were applied to human biological aging in order to assess whether aging can be classified as a disease. These criteria were developed for the 11th revision of the International Classification of Diseases (ICD) and included disease diagnostics, mechanisms, course and outcomes, known interventions, and linkage to genetic and environmental factors. RESULTS: Biological aging can be diagnosed with frailty indices, functional, blood-based biomarkers. A number of major causal mechanisms of human aging involved in various organs have been described, such as inflammation, replicative cellular senescence, immune senescence, proteostasis failures, mitochondrial dysfunctions, fibrotic propensity, hormonal aging, body composition changes, etc. We identified a number of clinically proven interventions, as well as genetic and environmental factors of aging. Therefore, aging fits the ICD-11 criteria and can be considered a disease. Our proposal was submitted to the ICD-11 Joint Task force, and this led to the inclusion of the extension code for "Ageing-related" (XT9T) into the "Causality" section of the ICD-11. This might lead to greater focus on biological aging in global health policy and might provide for more opportunities for the new therapy developers.

Of Mice, Whales, Jellyfish and Men: In Pursuit of Increased Longevity

The quest for increased human longevity has been a goal of mankind throughout recorded history. Recent molecular studies are now providing potentially useful insights into the aging process which may help to achieve at least some aspects of this quest. This chapter will summarize the main findings of these studies with a focus on long-lived mutant mice and worms, and the longest living natural species including Galapagos giant tortoises, bowhead whales, Greenland sharks, quahog clams and the immortal jellyfish.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.