Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis
- University of Minnesota, United States
- University of Helsinki, Finland
- The University of North Carolina at Chapel Hill, United States
Abstract
How dietary selection impacts genome evolution to define the optimal range of nutrient intake is a poorly understood question with medical relevance. We have addressed this question by analyzing Drosophila simulans and sechellia, recently diverged species with differential diet choice. D. sechellia larvae, specialized to a nutrient scarce diet, did not survive on sugar rich conditions, while the generalist species D. simulans was sugar tolerant. Sugar tolerance in D. simulans was a tradeoff for performance on low energy diet and was associated with global reprogramming of metabolic gene expression. Hybridization and phenotype-based introgression revealed the genomic regions of D. simulans that were sufficient for sugar tolerance. These regions included genes that are involved in mitochondrial ribosome biogenesis and intracellular signaling, such as PPP1R15/Gadd34 and SERCA, which contributed to sugar tolerance. In conclusion, genomic variation affecting genes involved in global metabolic control defines the optimal range for dietary macronutrient composition.
Data availability
Genome sequencing and RNA sequencing datasets have bee placed into NCBI SRA archive, Study # SRP158000. A link is provided for reviewers in the Materials and Methods.
Article and author information
Author details
Funding
Suomen Akatemia (286767)
- Ville Hietakangas
Novo Nordisk Foundation (NNF16OC0021460)
- Ville Hietakangas
Sigrid Juséliuksen Säätiö
- Ville Hietakangas
Finnish Diabetes Foundation
- Ville Hietakangas
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2018, Melvin et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,215
- views
-
- 318
- downloads
-
- 13
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis
eLife 7:e40841.
https://doi.org/10.7554/eLife.40841
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.
-
- Chromosomes and Gene Expression
- Genetics and Genomics
A new method for mapping torsion provides insights into the ways that the genome responds to the torsion generated by RNA polymerase II.
