Understanding the relationship between disease and aging in order to increase longevity in a healthy condition is a major goal in medical research. The search for the genetic components of longevity in humans has faced periods of success and also disappointment due to complicating factors (e.g., population specificity, gender, and birth cohort effects), but also heterogeneity of long lived people, characterized by age related diseases (ARDs). Thus, a great portion of heritability and phenotypic variance of longevity remains unexplained, probably due to the complex interaction among genetic and non-genetic factors, which modulate the individual chance of living longer. In the complex scenario of longevity and aging characterization, a large number of genetic variants with small effect have been associated with higher survival, typically located in key interrelated pathways with few crucial hubs interconnecting the different biological routes and modulated by the environment (e.g., mTorc, insulin, stress response pathways).
Furthermore, a clear distinction between ARDs and longevity does not exist because they overlap. For this reason, the search for candidate genes for human longevity cannot exclude diseases-related variants. In addition, we need to move from single variant to fine analysis of the integrated networks implied in ARDs, looking for the key connectors laying at the crossroad between health and disease, successful and unsuccessful aging. This will help the understanding of the underlying mechanisms of aging as well as the identification of the interventions suitable to achieve a healthy longevity, i.e., to modulate environmental factors interacting with genetic factors.
To gain an understanding of the system as a whole rather than focusing on individual factors, the multi-level integration of omics data should be applied. In recent years, the availability of very large datasets of biological data (genetic variants, epigenomics, RNA expression, and metabolomics) from high-throughput technologies provided valuable tools to investigate the aging process at the molecular level. Thus, after decades of reductionist studies, network and integrated omics data analysis have begun to target the aging process at a system level, promising to revolutionize the identification of aging biomarkers and the development of strategies to improve health in old age.
The aim of this Research Topic is to provide an overview on the application of omics technologies in human aging and longevity research. We invite the submission of Original Research articles, Brief Research Reports, and Reviews dealing with the search for determinants of human longevity and aging. We will consider a variety of topics such as RNA-sequencing, DNA methylation studies, or genetic association studies, integrated with transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics data. Articles focused on the identification of networks related to aging and longevity, the development of new statistical approaches to integrate data (genetics and non-genetics) in the field of aging research, and the identification of biomarkers of aging through systems biology approaches are highly encouraged.
Understanding the relationship between disease and aging in order to increase longevity in a healthy condition is a major goal in medical research. The search for the genetic components of longevity in humans has faced periods of success and also disappointment due to complicating factors (e.g., population specificity, gender, and birth cohort effects), but also heterogeneity of long lived people, characterized by age related diseases (ARDs). Thus, a great portion of heritability and phenotypic variance of longevity remains unexplained, probably due to the complex interaction among genetic and non-genetic factors, which modulate the individual chance of living longer. In the complex scenario of longevity and aging characterization, a large number of genetic variants with small effect have been associated with higher survival, typically located in key interrelated pathways with few crucial hubs interconnecting the different biological routes and modulated by the environment (e.g., mTorc, insulin, stress response pathways).
Furthermore, a clear distinction between ARDs and longevity does not exist because they overlap. For this reason, the search for candidate genes for human longevity cannot exclude diseases-related variants. In addition, we need to move from single variant to fine analysis of the integrated networks implied in ARDs, looking for the key connectors laying at the crossroad between health and disease, successful and unsuccessful aging. This will help the understanding of the underlying mechanisms of aging as well as the identification of the interventions suitable to achieve a healthy longevity, i.e., to modulate environmental factors interacting with genetic factors.
To gain an understanding of the system as a whole rather than focusing on individual factors, the multi-level integration of omics data should be applied. In recent years, the availability of very large datasets of biological data (genetic variants, epigenomics, RNA expression, and metabolomics) from high-throughput technologies provided valuable tools to investigate the aging process at the molecular level. Thus, after decades of reductionist studies, network and integrated omics data analysis have begun to target the aging process at a system level, promising to revolutionize the identification of aging biomarkers and the development of strategies to improve health in old age.
The aim of this Research Topic is to provide an overview on the application of omics technologies in human aging and longevity research. We invite the submission of Original Research articles, Brief Research Reports, and Reviews dealing with the search for determinants of human longevity and aging. We will consider a variety of topics such as RNA-sequencing, DNA methylation studies, or genetic association studies, integrated with transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics data. Articles focused on the identification of networks related to aging and longevity, the development of new statistical approaches to integrate data (genetics and non-genetics) in the field of aging research, and the identification of biomarkers of aging through systems biology approaches are highly encouraged.
