bioRxivEvolutionary Biology
Combining metabolomics and experimental evolution reveals key mechanisms underlying longevity differences in laboratory evolved Drosophila melanogaster populations
M. Phillips, K. R. Arnold, Z. Vue, H. Beasley, E. Garza Lopez, A. Marshall, D. Morton, M. R. McReynolds, T. T. Barter, A. Hinton
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 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 like aging.
- organism: Drosophila
- assay: metabolomics
- assay: DNA methylation
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: experimental assay
- approach: statistical model
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