Natural genetic variation inC. elegansreveals genomic loci controlling metabolite levels

Abstract

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.

Dissecting the eQTL micro-architecture inCaenorhabditis elegans

“…Furthermore, it shows how ILs can be used to narrow-down these eQTL hotspots (S noek et al 2012), using correlation analysis previously used to link trans -bands to genes and biological processes (A ndersen et al 2014; S terken et al 2017). These findings show on a large scale that QTL mapped using a single marker model in a moderately sized RIL population are reliably replicable in a population with a different genetic structure, which confirms findings for single traits reported in C. elegans and beyond (for example, see (S noek et al 2012; A ndersen et al 2014; G ao et al 2018)).…”

“…For example, a study on bacterial preference of C. elegans noted a relatively high heritability (0.46) for Serratia marcescens over E. coli , yet uncovered only a single QTL in the RILs used for mapping whereas multiple were expected (G later et al 2014). Furthermore, recent work from our group, studying metabolite abundances showed high heritability (from 0.32 up to 0.82) corresponding to a few uncovered QTL (G ao et al 2018). Intriguingly, several studies imply trait architectures consisting of closely linked QTL that are balanced in the parental strains (G aertner et al 2012; G later et al 2014).…”

“…Typically, introgression lines are used to verify the existence of QTL, or to narrow down the location of a QTL in order to find the underlying causal polymorphism ( e.g. see (G ao et al 2018)). The RIL and IL populations are exposed to three environments: heat-shock, recovery from heat-shock, and a control environment at a standard rearing temperature.…”

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

“…Lately, the diversity of molecular phenotypes for which natural variation can be found and used to map QTLs has been expanded to proteins (80, 93) and metabolites (81). The associations of these molecular phenotypes with variation in gene expression, eQTLs and classical phenotypes and QTLs have yet to be explored.…”

Experimental evolution of independent genetic pathways for resistance toPseudomonas aeruginosapathogenicity within the nematodeCaenorhabditis remanei

“…This is in stark contrast to the typical trajectory of novel mutations in natural populations where natural selection and genetic drift lead to an accumulation of mutations with small to moderate effects (Rockman, 2012;Noble, et al, 2017;Teotónio, et al, 2017). Models utilizing natural variation in genetic pathways can reveal physiological mechanisms that modify pathogen interactions and disease which would likely be missed using a mutagenesis approach and evolution itself can be used to select for alleles conferring a desired phenotype (Kammenga, et al, 2008;Teotónio et al, 2017;Gao, et al, 2018;Hahnel, et al, 2018).…”