IDENTIFICATION OF LOCI RESPONSIBLE TO ENZYME ACTIVITY VARIATION IN CENTRAL METABOLISM USING GENOME WIDE ASSOCIATION

FUSARI, CORINA M.; KOOKE, RIK; ENKE, BEATRICE; KROHN, NICOLE; HOEHNE, MELANIE; NORDBORG, MAGNUS; KEURENTJES, JOOST J. B.; SULPICE, RONAN; STITT, MARK

Viena

Congreso; ICAR 2012; 2012

GMI, Universitaet Wien, Univversitaet fuer Bodenkultur Wien

Enzymes constitute the metabolic machinery for primary C and N metabolism, which provides building blocks for growth. In a previous study using Arabidopsis accessions we showed that many enzymes in primary metabolism correlate to each other, suggesting that their levels are under tight common regulatory control. Analysis of the genetic regulation of enzyme activities provides a powerful approach to identify genes in this regulatory network. Using the Ler x Cvi biparental population, we already showed that there is considerable genetic variation in enzyme activities from central C metabolism and carried out coarse mapping of QTL that determine 10 out of 15 enzyme activities. We have now deepened these studies by fine mapping 18 enzyme activities, 12 metabolites and biomass using a core set (326 accessions) of the RegMap panel, and by performing association analysis using the 250K SNP chip. Correlation analyses confirmed that enzymes change in a very strong and coordinated manner. By applying Mixed Linear Models we were able to identify associations between some enzyme activities and key polymorphisms present in the respective structural genes. Some of these co-localized with coarse-mapped QTL in the Ler x Cvi biparental population (e.g. acid Invertase and UGPase). However, many strongly associated SNPs were in trans to enzyme structural genes, suggesting that they represent trans-regulatory QTL, e.g. for neutral invertase and AGPase. Co-localization of QTL for different enzyme activities identified potential regulatory hubs, i.e. gene loci related to transcriptional regulation, cellular trafficking and protein degradation. All loci remained strongly associated when different methods were applied to control for population structure and kinship relationships. These analyses provide the highest defined QTL dataset for primary metabolism enzymes to date and break new ground in understanding the genetic regulation of central metabolism.