Natural genetic variation in C. elegans reveals genomic loci controlling metabolite levels

Metabolic homeostasis is sustained by complex biological networks responding to nutrient availability. Disruption of this equilibrium involving intricate interactions between genetic and environmental factors can lead to metabolic disorders, including obesity and type 2 diabetes. To identify the gen...

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Published inbioRxiv
Main Authors Gao, Arwen, Sterken, Mark, Jelmi Uit De Bos, Jelle Van Creij, Kamble, Rashmi, Basten Snoek, Kammenga, Jan, Houtkooper, Riekelt H
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 10.11.2017
Subjects
Amino acid composition
Amino acids
Chromosome 1
Chromosome 4
Correlation analysis
Diabetes mellitus
Diabetes mellitus (non-insulin dependent)
Environmental factors
Gene mapping
Genetic diversity
Genetic factors
Heritability
Homeostasis
Inbreeding
Metabolic disorders
Metabolism
Metabolites
Metabolomics
Nutrient availability
Nutrients
Quantitative genetics
Quantitative trait loci
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Summary:Metabolic homeostasis is sustained by complex biological networks responding to nutrient availability. Disruption of this equilibrium involving intricate interactions between genetic and environmental factors can lead to metabolic disorders, including obesity and type 2 diabetes. To identify the genetic factors controlling metabolism, we applied a quantitative genetic strategy using a Caenorhabditis elegans population consisting of 199 recombinant inbred lines (RILs) originally derived from crossing parental strains Bristol N2 and Hawaii CB4856. We focused on the genetic factors that control metabolite levels and measured fatty acid (FA) and amino acid (AA) composition in the 199 RILs using targeted metabolomics. For both FA and AA profiles, we observed large variation in metabolite levels with 32-82% heritability between the RILs. We performed metabolite-metabolite correlation analysis and detected strongly co-correlated metabolite clusters. To identify natural genetic variants responsible for the observed metabolite variations, we performed QTL mapping and detected 36 significant metabolite QTL (mQTL). We focused on the mQTL that displayed high significant linkage and heritability, including an mQTL for the FA C14:1 on chromosome I, and another mQTL for the FA C18:2 on chromosome IV. Using introgression lines (ILs) we were able to narrow down both mQTL to a 1.4 Mbp and a 3.6 Mbp region, respectively. Overall, this systems approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. It also allows us to investigate additional interventions, such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.
DOI:10.1101/217729