GeneticsGeneticsPeer-reviewed journal article
Genetic variation for ontogenetic shifts in metabolism underlies physiological homeostasis in Drosophila
Abstract
Organismal physiology emerges from metabolic pathways and structures that can vary across development and among individuals. Here we tested whether genetic variation at one level of physiology can be buffered at higher levels during development by the inherent capacity for homeostasis in physiological systems. We found that the fundamental scaling relationship between mass and metabolic rate, as well as the oxidative capacity per mitochondria, differed significantly across development in the fruit fly Drosophila. However, mitochondrial respiration rate was maintained across development at similar levels. Furthermore, genotypes clustered into two types--those that switched to aerobic, mitochondrial ATP production before the second instar and those that relied on anaerobic production of ATP via glycolysis through the second instar. Despite genetic variation for the timing of this metabolic shift, second-instar metabolic rate was more robust to genetic variation than was the metabolic rate of other instars. We also found that a mitochondrial-nuclear genotype with disrupted mitochondrial function both increased aerobic capacity more through development and relied more heavily on anaerobic ATP production relative to wildtype genotypes. By taking advantage of both ways of making ATP, this genotype maintained mitochondrial respiratory capacity, but also generated more free radicals and had decreased mitochondrial membrane potential, potentially as a physiological-defense mechanism. Taken together, the data revealed that genetic defects in core physiology can be buffered at the organismal level via physiological compensation and that natural populations likely harbor genetic variation for distinct metabolic strategies in development that generate similar organismal outcomes.
- organism: Drosophila
- assay: metabolomics
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: experimental assay
- Citations
- 32
- FWCI
- 3.33
- Field percentile
- top 8%
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