Identification of enzymatic and regulatory genes of plant metabolism through QTL analysis in Arabidopsis

Abstract

The biochemical diversity in the plant kingdom is estimated to well exceed 100,000 distinct compounds (Weckwerth, 2003) and 4000 to 20,000 metabolites per species seem likely (Fernie et al., 2004). In recent years extensive progress has been made towards the identification of enzymes and regulatory genes working in a complex network to generate this large arsenal of metabolites. Genetic loci influencing quantitative traits, e.g. metabolites or biomass, may be mapped to associated molecular markers, a method called quantitative trait locus mapping (QTL mapping), which may facilitate the identification of novel genes in biochemical pathways. Arabidopsis thaliana, as a model organism for seed plants, is a suitable target for metabolic QTL (mQTL) studies due to the availability of highly developed molecular and genetic tools, and the extensive knowledge accumulated on the metabolite profile. While intensely studied, in particular since the availability of its complete sequence, the genome of Arabidopsis still comprises a large proportion of genes with only tentative function based on sequence homology. From a total number of 33,518 genes currently listed (TAIR 9, http://www.arabidopsis.org), only about 25% have direct experimental evidence for their molecular function and biological process, while for more than 30% no biological data are available. Modern metabolomics approaches together with continually extended genomic resources will facilitate the task of assigning functions to those genes. In our previous study we reported on the identification of mQTL (Lisec et al., 2008). In this paper, we summarize the current status of mQTL analyses and causal gene identification in Arabidopsis and present evidence that a candidate gene located within the confidence interval of a fumarate mQTL (AT5G50950) encoding a putative fumarase is likely to be the causal gene of this QTL. The total number of genes molecularly identified based on mQTL studies is still limited, but the advent of multi-parallel analysis techniques for measurement of gene expression, as well as protein and metabolite abundances and for rapid gene identification will assist in the important task of assigning enzymes and regulatory genes to the growing network of known metabolic reactions.