P0855
Combined Correlation-Based Network and mQTL Analyses Efficiently Identified Loci for Branched Chain Amino Acid, Serine to Threonine, and Proline Metabolism in Tomato Seeds

Date: Monday, January 12, 2015
Room:
Albert Batushansky , Ben-Gurion University, Midreshet Ben-Gurion, Israel
David Toubiana , Ben-Gurion University, Midreshet Ben-Gurion, Israel
Oren Tzfadia , Weizmann Institute of Science, Rehovot, Israel
Asif Khan , Ben-Gurion University, Midreshet Ben-Gurion, Israel
Simon Barak , Ben-Gurion University, Midreshet Ben-Gurion, Israel
Daniel Zamir , The Hebrew University of Jerusalem, Rehovot, Israel
Zoran Nikoloski , Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
Alisdair R. Fernie , Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
Aaron Fait , Ben-Gurion University, Midreshet Ben Gurion, Israel
Correlation-based network analysis (CNA) of the metabolic profiles of seeds of a tomato Introgression Line mapping population revealed a clique of proteinogenic amino acids:  Gly, Ile, Pro, Ser, Thr, and Val. Correlations between profiles of these amino acids exhibited a statistically significant average correlation coefficient of 0.84 as compared to an average correlation coefficient of 0.39 over the 16,119 other metabolite cliques containing 6 metabolites. QTL analysis revealed co-localization for the six amino acids on chromosome 2, 4 and 10, however the sequence analysis of the selected chromosomes identified a unique set of 10 genes on chromosome 2 only, which were associated with amino acid metabolism and specifically the metabolism of Ser-Gly and their conversion into branched chain amino acids. Metabolite profiling of a set of tomato sublines, with introgressions on chromosome 2, identified a significant change in the abundance of the six amino acids in comparison to M82-control. Expression analysis of the identified amino acid genes matched the observation from the metabolite profiles suggest a coordinated regulation of the amino acids at the transcription level. To explore deeper the transcriptional network related with amino acid regulation and identify potential master gene(s), RNA-seq analysis was implemented on dry and imbibed seeds of the introgression lines.