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The metabolome as a biomarker of aging in Drosophila melanogaster
Authors:Xiaqing Zhao  Forrest T. Golic  Benjamin R. Harrison  Meghna Manoj  Elise V. Hoffman  Neta Simon  Richard Johnson  Michael J. MacCoss  Lauren M. McIntyre  Daniel E. L. Promislow
Affiliation:1. Department of Lab Medicine and Pathology, University of Washington School of Medicine, Seattle US ; 2. Department of Genome Sciences, University of Washington School of Medicine, Seattle US ; 3. Genetics Institute, University of Florida, Gainesville USA ; 4. Department of Molecular Genetics and Microbiology, University of Florida, Gainesville USA ; 5. Department of Biology, University of Washington, Seattle US
Abstract:Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.
Keywords:aging   biomarker   drosophila   metabolomics   mortality
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