Linking Metabolic QTL with Network and cis-eQTL Controlling Biosynthetic Pathways

Abstract

Phenotypic variation between individuals of a species is often under quantitative genetic control. Genomic analysis of gene expression polymorphisms between individuals is rapidly gaining popularity as a way to query the underlying mechanistic causes of variation between individuals. However, there is little direct evidence of a linkage between global gene expression polymorphisms and phenotypic consequences. In this report, we have mapped quantitative trait loci (QTLs)-controlling glucosinolate content in a population of 403 Arabidopsis Bay 3 Sha recombinant inbred lines, 211 of which were previously used to identify expression QTLs controlling the transcript levels of biosynthetic genes. In a comparative study, we have directly tested two plant biosynthetic pathways for association between polymorphisms controlling biosynthetic gene transcripts and the resulting metabolites within the Arabidopsis Bay 3 Sha recombinant inbred line population. In this analysis, all loci controlling expression variation also affected the accumulation of the resulting metabolites. In addition, epistasis was detected more frequently for metabolic traits compared to transcript traits, even when both traits showed similar distributions. An analysis of candidate genes for QTL-controlling networks of transcripts and metabolites suggested that the controlling factors are a mix of enzymes and regulatory factors. This analysis showed that regulatory connections can feedback from metabolism to transcripts. Surprisingly, the most likely major regulator of both transcript level for nearly the entire pathway and aliphatic glucosinolate accumulation is variation in the last enzyme in the biosynthetic pathway, AOP2. This suggests that natural variation in transcripts may significantly impact phenotypic variation, but that natural variation in metabolites or their enzymatic loci can feed back to affect the transcripts.

Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-wide Association Mapping

“…For example, the ratio of 3-methylthiopropyl to total C3 aliphatic shows the efficiency of conversion of the 3-methylthiopropyl glucosinolate into its potential products (Table S3). These ratios and summation traits allow us to isolate the effects of variation at individual steps of glucosinolate biosynthesis from variation affecting the rest of the biosynthetic pathway (Wentzell et al 2007). The independence of these derived traits is supported by their low correlation with the raw traits (Table S4).…”

“…Two major classes of GSL are produced by A. thaliana, aliphatic (methionine derived) and indolic (tryptophan derived), both showing extensive intraspecific variation in structure and content. Previous studies using at least five different A. thaliana populations to map GSL accumulation provide an excellent set of known QTL (Kliebenstein et al 2001b(Kliebenstein et al , 2002aKeurentjes et al 2006;Pfalz et al 2007;Wentzell et al 2007). The genetic tools available for A. thaliana have facilitated cloning of several QTL controlling variation of GSL structure and content between natural A. thaliana accessions, most notably the AOP and MAM/ Elong loci.…”

“…(C) Aliphatic GSL side chain modification within the Bay-0 3 Sha RIL population. Sidechain modification is controlled by variation at the GSL.ALK QTL via cis-eQTL at the AOP2 and AOP3 genes (Wentzell et al 2007). The Cvi and Sha accessions express AOP2 to produce alkenyl GSL.…”

Complex Genetics Control Natural Variation inArabidopsis thalianaResistance toBotrytis cinerea

“…Studies of quantitative resistance to B. cinerea in tomato and Arabidopsis found QTL explaining 12-15% (tomato) and 5-10% (Arabidopsis) of phenotypic variance for traits associated with resistance to B. cinerea (Denby et al 2004;Finkers et al 2007). Despite the challenges manifest in uncovering the causal polymorphisms for small-effect QTL, genomics tools available for Arabidopsis thaliana offer promising opportunities to unravel network mechanisms underlying complex quantitative traits (Tonsor et al 2005;Keurentjes et al 2007;Wentzell et al 2007;Ma and Bohnert 2008). To begin elucidating the molecular mechanisms underlying quantitative resistance, we analyzed quantitative A. thaliana responses to B. cinerea infection in a structured mapping population.…”

Epistasis and Genetic Regulation of Variation in the Arabidopsis Metabolome

“…Rowe et al conducted untargeted metabolomic analyses on an Arabidopsis Bayreuth-0 (Bay) 3 Shahdara (Sha) recombinant inbred line (RIL) population described by Loudet et al (2002) and previously used for targeted metabolite QTL and global expression QTL analyses (Loudet et al, 2003;Calenge et al, 2006;Kliebenstein et al, 2006;Wentzell et al, 2007;West et al, 2007). The untargeted metabolomic approach aims to measure as many metabolites as possible, rather than determining a priori what class of compounds or specific compounds will be investigated.…”