The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster

Abstract

Background Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). Results We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight ‘high resistance’ lines and eight ‘low resistance’ lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. Conclusions This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype

“…Metabolites represent end products of biochemical pathways; hence, they are downstream to other -omics data and therefore closest to the phenotype. Hence, metabolomics is widely recognized now as an important stepping stone to relate genotype to phenotype (Fiehn, 2002;Patti et al, 2012;Bhattacharyya et al, 2016;Johnson et al, 2016;Bhattacharyya et al, 2018;Handakumbura et al, 2019;Rodrigues & Shakhnovich, 2019;Harrison et al, 2020). In the recent past, high-throughput studies have been dedicated to understanding how genetic variations lead to changes in metabolic profile of the cell (Mulleder et al, 2016;Fuhrer et al, 2017).…”

Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype

“…Metabolites represent end products of biochemical pathways; hence they are downstream to other -omics data, and therefore closest to the phenotype. Hence metabolomics is widely recognized now as an important stepping-stone to relate genotype to phenotype (Bhattacharyya et al, 2016;Bhattacharyya et al, 2018;Fiehn, 2002;Handakumbura et al, 2019;Harrison et al, 2020;Johnson et al, 2016;Patti et al, 2012;Rodrigues & Shakhnovich, 2019;. In the recent past, high-throughput studies have been dedicated to understanding how genetic variations lead to changes in metabolic profile of the cell (Fuhrer et al, 2017;Mulleder et al, 2016).…”

Evolution of Natural Lifespan Variation and Molecular Strategies of Extended Lifespan

“…Evolution of gene expression is known to exhibit selective constraints [77, 78], thereby supporting specific phenotypic outcomes such as changes in morphology [79] and lifespan [80]. Similarly, comparative studies on molecule abundance by metabolite profiling have been utilized to describe the genotype to phenotype relations in model organisms [81, 82]. Accordingly, we aimed to explain lifespan differences among these natural populations of closely related yeast isolates by analyzing their endophenotype differences that include gene expression variation (transcriptomics) and differences in their metabolite levels (metabolomics).…”