|Title||Natural genetic variation in C. elegans identified genomic loci controlling metabolite levels|
|Author(s)||Gao, Arwen W.; Sterken, Mark G.; De Bos, Jelmi Uit; Creij, Jelle van; Kamble, Rashmi; Snoek, Basten L.; Kammenga, Jan E.; Houtkooper, Riekelt H.|
|Source||Genome Research 28 (2018)8. - ISSN 1088-9051 - p. 1296 - 1308.|
Laboratory of Nematology
Groep KoornneefGroep Koornneef
|Publication type||Refereed Article in a scientific journal|
|Abstract||Metabolic homeostasis is sustained by complex biological networks that respond to nutrient availability. Genetic and environmental factors may disrupt this equilibrium, leading to metabolic disorders, including obesity and type 2 diabetes. To identify the genetic factors controlling metabolism, we performed quantitative genetic analysis using a population of 199 recombinant inbred lines (RILs) in the nematode Caenorhabditis elegans. We focused on the genomic regions that control metabolite levels by measuring fatty acid (FA) and amino acid (AA) composition in the RILs using targeted metabolomics. The genetically diverse RILs showed a large variation in their FA andAAlevels with a heritability ranging from 32% to 82%. We detected strongly co-correlated metabolite clusters and 36 significant metabolite QTL (mQTL). We focused on mQTL displaying highly 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. RNAi-based screening focusing on the Chromosome I mQTL identified several candidate genes for the C14:1 mQTL, including lagr-1, Y87G2A.2, nhr-265, nhr-276, and nhr-81. Overall, this systems
approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. Furthermore, it allows us to investigate interventions such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.