Combining Metabolomics and Experimental Evolution Reveals Key Mechanisms Underlying Longevity Differences in Laboratory Evolved Drosophila melanogaster Populations

Abstract

Experimental evolution with Drosophila melanogaster has been used extensively for decades to study aging and longevity. In recent years, the addition of DNA and RNA sequencing to this framework has allowed researchers to leverage the statistical power inherent to experimental evolution to study the genetic basis of longevity itself. Here, we incorporated metabolomic data into to this framework to generate even deeper insights into the physiological and genetic mechanisms underlying longevity differences in three groups of experimentally evolved D. melanogaster populations with different aging and longevity patterns. Our metabolomic analysis found that aging alters mitochondrial metabolism through increased consumption of NAD+ and increased usage of the TCA cycle. Combining our genomic and metabolomic data produced a list of biologically relevant candidate genes. Among these candidates, we found significant enrichment for genes and pathways associated with neurological development and function, and carbohydrate metabolism. While we do not explicitly find enrichment for aging canonical genes, neurological dysregulation and carbohydrate metabolism are both known to be associated with accelerated aging and reduced longevity. Taken together, our results provide plausible genetic mechanisms for what might be driving longevity differences in this experimental system. More broadly, our findings demonstrate the value of combining multiple types of omic data with experimental evolution when attempting to dissect mechanisms underlying complex and highly polygenic traits such as aging.

3D Reconstruction of Murine Mitochondria Exhibits Changes in Structure Across Aging Linked to the MICOS Complex

“…Higher degrees of statistical significance (**, ***, ****) were defined as p < 0.01, p < 0.001, and p < 0.0001, respectively. Gas Chromatography-Mass Spectrometry (GC-MS) Samples were extracted for metabolites and prepared as previously designed 76 . The profiling of the metabolites was performed using TraceFinder 4.1 with standard verified peaks and retention times.…”

“…The data was then normalized to an internal standard to control for extraction, derivatization, and/or loading effects. Liquid Chromatography-Mass Spectrometry (LC-MS) LC-MS was performed for myotubes as previously described 76 . TraceFinder 4.1 software was used for analysis and metabolites were identified based on an in-house library.…”

“…Metabolomic analysis was performed as described previously 80 using a web service called MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml, last accessed on 8 February 2022) that combines machine learning methods and statistics to group data using PCA, heat mapping, metabolite set enrichment analysis, and statistical analysis. One-way ANOVA and Fisher’s LSD multiple comparison test were also used.…”

“…LC-MS was performed for myotubes as previously described 76 . TraceFinder 4.1 software was used for analysis and metabolites were identified based on an in-house library.…”

Mitochondrial Bioenergetics in Different Pathophysiological Conditions 2.0

“…The comparative study of Phillips et al incorporated metabolomic data into DNA and RNA sequencing framework to gain deeper insights into the physiological and genetic mechanisms underlying longevity differences in three groups of experimentally evolved Drosophila melanogaster populations with different aging and longevity patterns [ 20 ]. Combining genomic and metabolomic data, the authors provided a list of biologically relevant candidate genes, among which was found significant enrichment for genes and pathways associated with neurological development and function and carbohydrate metabolism [ 20 ]. Neurological dysregulation and carbohydrate metabolism are known to be associated with accelerated aging and reduced longevity.…”