SE161:/MS1

Sample Set Information

ID	TSE1320
Title	Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis.
Description	A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki x Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.
Authors	Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R, Kikuchi J, Yonemaru J, Ebana K, Yano M, Saito K.
Reference	Plant J. 2012 May;70(4):624-36. doi: 10.1111/j.1365-313X.2012.04903.x. Epub 2012 Feb 10.
Comment

PubMed

Analytical Method Details Information

ID	MS1
Title	LC‐ESI‐Q‐TOF‐MS
Instrument	LC, Waters Acquity UPLC system; Q-TOF-MS, Waters Q-Tof Premier
Instrument Type	UPLC-QTOF-MS
Ionization	ESI
Ion Mode	Positive
Description	Sample metabolomic data were acquired using four time-of-flight mass spectrometers (TOF-MS), including CE-TOF-MS for analysis of polar metabolites, GC-TOF-MS for analysis of primary metabolites, LC-Q-TOF-MS for analysis of secondary metabolites and LC-IT-TOF-MS for analysis of lipids. Analysis was performed in triplicate, and the samples were extracted from each pipeline using optimized methods. For the LC-Q-TOF-MS pipeline, 100 mg of rice seed powder was homogenized in 10 volumes of 5% aqueous methanol containing 0.1% acetic acid (Tokyo Kasei, http://www.tciamerica.com/) using an MM300 mixer mill (Retsch, http://www.retsch.com/) with a zirconia bead for 10 min at 20 Hz. Next, the samples were centrifuged at 15000 g for 10 min. The supernatant (500 ll) was diluted to 5.0 ml using water containing 0.1% acetic acid, and was applied to a 3 cc OASIS HLB cartridge (Waters, http://www.waters.com/) that had been equilibrated with 0.5% aqueous methanol containing 0.1% acetic acid. After pre-equilibrium with 5 ml of 0.5% aqueous methanol containing 0.1% acetic acid, metabolites were eluted with 3 ml of 90% aqueous methanol containing 0.1% acetic acid. The eluate was dried under vacuum and then suspended in 100 ll water containing the internal standard (0.5 mg/ml lidocaine). Insoluble residue was removed by filtration using an Ultrafree-MC filter with 0.2 lm pore size (Millipore, http://www.millipore.com/). The samples (3µl) were subsequently subjected to metabolome analysis by liquid chromatography coupled with electrospray quadrupole time-of-flight tandem mass spectrometry (LC-ESI-Q-TOF-MS) using an Acquity BEH ODS column (LC, Waters Acquity UPLC system; Q-TOF-MS, Waters Q-Tof Premier).
Comment_of_details