ena-STUDY-DZNE-18-02-2021-12:48:24:005-973
Central longevity regulators have limited effects on age-dependent phenotypic changes assessed at scale
Most previous research aimed at identifying genetic and environmental factors regulating aging has largely relied on using lifespan as the sole proxy measure for aging or was restricted to the assessment of a small number of aging traits. Here, we introduce a new resource derived from a novel analytical approach to aging that offers the following major improvements compared to prior research. We employed large-scale phenotyping to analyze not only a few, but hundreds of phenotypes and thousands of molecular markers across tissues and organ systems in a single study in aging C57BL/6J mice. For each phenotype, we established lifetime profiles to determine at which age age-dependent phenotypic change is first detectable relative to the young adult baseline. We examined central genetic and environmental lifespan regulators (putative anti-aging interventions; PAAIs; targeting mTOR and growth hormone signaling; dietary restriction) for a possible amelioration of the signs and symptoms of aging. Importantly, in our study design, we included young treated groups of animals, subjected to PAAIs prior to the onset of age-dependent phenotypic change. This new study design allowed us to observe that most PAAI effects, surprisingly, influenced phenotypes long before the onset of age-dependent changes. Accordingly, many PAAI effects do not reflect a targeting of age-dependent phenotypic change, suggesting that the extent to which PAAIs influence the aging process has been overestimated. This dataset contains measurements on hundreds of phenotypes and molecular markers that were acquired across the murine lifespan. Specifically, to map age-dependent trajectories of a broad range of phenotypes we carried out deep phenotyping analyses in 3, 5, 8, 14, 20 and 26 month old C57BL/6J mice, covering phenotypes within the areas of cardiovascular health, neuropsychiatric functions, sensory systems, clinical chemistry, hematology, immunology, metabolism, as well as anatomy and physiology. These data were used to define age-sensitive phenotypes (ASPs) and to determine when age-dependent changes are first detectable.The dataset also contains phenotypic data obtained in young and old mice subjected to PAAIs. For each PAAI, we generated a young as well as an old cohort of experimental animals and controls, all of which were analyzed concurrently. We focused our analyses on two single-gene mutants, both associated with extension of lifespan in mice and each affecting a pathway generally considered to be pivotal in regulating lifespan and aging (hypomorphic mTOR mutant mice; mice with a loss-of-function mutation in Ghrhr). We also applied our analytical approach to a dietary restriction model (intermittent fasting) previously linked to lifespan extension.The datasets presented here are raw data underlying the paper “Central longevity regulators have limited effects on age-dependent phenotypic changes assessed at scale” by Xie et al.
Most previous research aimed at identifying genetic and environmental factors regulating aging has largely relied on using lifespan as the sole proxy measure for aging or was restricted to the assessment of a small number of aging traits. Here, we introduce a new resource derived from a novel analytical approach to aging that offers the following major improvements compared to prior research. We employed large-scale phenotyping to analyze not only a few, but hundreds of phenotypes and thousands of molecular markers across tissues and organ systems in a single study in aging C57BL/6J mice. For each phenotype, we established lifetime profiles to determine at which age age-dependent phenotypic change is first detectable relative to the young adult baseline. We examined central genetic and environmental lifespan regulators (putative anti-aging interventions; PAAIs; targeting mTOR and growth hormone signaling; dietary restriction) for a possible amelioration of the signs and symptoms of aging. Importantly, in our study design, we included young treated groups of animals, subjected to PAAIs prior to the onset of age-dependent phenotypic change. This new study design allowed us to observe that most PAAI effects, surprisingly, influenced phenotypes long before the onset of age-dependent changes. Accordingly, many PAAI effects do not reflect a targeting of age-dependent phenotypic change, suggesting that the extent to which PAAIs influence the aging process has been overestimated. This dataset contains measurements on hundreds of phenotypes and molecular markers that were acquired across the murine lifespan. Specifically, to map age-dependent trajectories of a broad range of phenotypes we carried out deep phenotyping analyses in 3, 5, 8, 14, 20 and 26 month old C57BL/6J mice, covering phenotypes within the areas of cardiovascular health, neuropsychiatric functions, sensory systems, clinical chemistry, hematology, immunology, metabolism, as well as anatomy and physiology. These data were used to define age-sensitive phenotypes (ASPs) and to determine when age-dependent changes are first detectable.The dataset also contains phenotypic data obtained in young and old mice subjected to PAAIs. For each PAAI, we generated a young as well as an old cohort of experimental animals and controls, all of which were analyzed concurrently. We focused our analyses on two single-gene mutants, both associated with extension of lifespan in mice and each affecting a pathway generally considered to be pivotal in regulating lifespan and aging (hypomorphic mTOR mutant mice; mice with a loss-of-function mutation in Ghrhr). We also applied our analytical approach to a dietary restriction model (intermittent fasting) previously linked to lifespan extension.The datasets presented here are raw data underlying the paper “Central longevity regulators have limited effects on age-dependent phenotypic changes assessed at scale” by Xie et al.