SE139:/S1/M1/D1

Sample Set Information

ID	TSE1240
Title	Comparative metabolomics charts the impact of genotype-dependent methionine accumulation in Arabidopsis thaliana.
Description	Methionine (Met) is an essential amino acid for all organisms. In plants, Met also functions as a precursor of plant hormones, polyamines, and defense metabolites. The regulatory mechanism of Met biosynthesis is highly complex and, despite its great importance, remains unclear. To investigate how accumulation of Met influences metabolism as a whole in Arabidopsis, three methionine over-accumulation (mto) mutants were examined using a gas chromatography–mass spectrometry-based metabolomics approach. Multivariate statistical analyses of the three mto mutants (mto1, mto2, and mto3) revealed distinct metabolomic phenotypes. Orthogonal projection to latent structures–discriminant analysis highlighted discriminative metabolites contributing to the separation of each mutant and the corresponding control samples. Though Met accumulation in mto1 had no dramatic effect on other metabolic pathways except for the aspartate family, metabolite profiles of mto2 and mto3 indicated that several extensive pathways were affected in addition to over-accumulation of Met. The pronounced changes in metabolic pathways in both mto2 and mto3 were associated with polyamines. The findings suggest that our metabolomics approach not only can reveal the impact of Met over-accumulation on metabolism, but also may provide clues to identify crucial pathways for regulation of metabolism in plants.
Authors	Kusano M, Fukushima A, Redestig H, Kobayashi M, Otsuki H, Onouchi H, Naito S, Hirai MY, Saito K.
Reference	Amino Acids. 2010 Oct;39(4):1013-21. doi: 10.1007/s00726-010-0562-y. Epub 2010 Mar 31.
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PubMed

Sample Information

ID	S1
Title	Arabidopsis mto (Arabidopsis thaliana methionine over-accumulation)
Organism - Scientific Name	Arabidopsis thaliana
Organism - ID	NCBI taxonomy 3702
Compound - ID
Compound - Source
Preparation	The Arabidopsis mto mutants, mto1-1 (in ecotype Columbia), mto1-4 (in Columbia), mto2 [in ecotype Wassilewskija (WS)], mto3-1 (in WS), and mto3-2 (in Columbia), have been described previously (Goto et al. 2005). Seedlings were grown on Murashige and Skoog medium (Wako, Osaka, Japan, #392-00591) with 0.8% agar, 1% sucrose at pH 5.8 under 16 h light/8 h dark cycles at 22°C for 18 days. Aerial parts were harvested from 4 to 10 biological replicates of each line (see Supplementary Data 1), 6 h after the onset of the light phase. The mto mutants were backcrossed ten times for mto1-1 and mto1-4, seven times for mto2, four times for mto3-1, and seven times for mto3-2 to the WT accession (Col-0) before phenotypic and metabolite profiling analysis. Two independent alleles (mto1-1 and mto1-4) for mto1, one (mto2-1) for mto2, and two (mto3-1 and mto3-2) for mto3 were used in this study. To minimize effects derived from the different harvesting periods, we compared mutants and the control plants that were harvested in the same period [hereafter called Col0_H1 (mto1), Col0_H2 (mto2), and Col0_H3 (mto3), Supplementary Fig. 1]. For metabolite profiling, these mutants and the corresponding WT were grown under strictly controlled conditions (Kusano et al. 2007). The mto2 plants exhibit dwarf phenotypes, whereas there are no visible phenotypic changes in the mto1 and mto3 mutants when compared with the WT (Fig. 1b).
Sample Preparation Details ID
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Analytical Method Information

ID	M1
Title	GC-TOF MS
Method Details ID	MS1
Sample Amount	55.6 μg fresh weight of plant material
Comment

Analytical Method Details Information

ID	MS1
Title	GC-TOF MS
Instrument	GC:Agilent 6890N MS:LECO Pegasus III TOF
Instrument Type
Ionization	EI
Ion Mode	Positive
Description	Plants may show differences in terms of metabolite profiles, so-called ‘metabotypes’, even when no visible phenotypic changes can be observed (Hall 2006). To achieve a broad coverage of primary metabolites, GC-TOF/MS-based metabolite profiling was used in this study. As described previously (Kusano et al. 2007), each sample was extracted, derivatized, and analyzed using GC-TOF/MS. Five milligrams fresh weight of plant tissues was used for GC-TOF/MS analysis. The derivatized extracts, equivalent to 55.6 μg fresh weight of plant material, were injected into the GC-TOF/MS instrument. All raw data were pre-processed using a MATLAB script (version 7.0.4, The MathWorks, Natick, MA) implemented by Jonsson et al. (2005, 2006) to perform baseline correction, alignment, and peak deconvolution. Metabolite identifications were performed by comparing their mass spectra and retention time indices to those generated for authentic compounds analyzed on our instrumentation as well as those in the mass spectra and retention index libraries in the Golm Metabolome Database (Kopka et al. 2005; Schauer et al. 2005). To correct the interference, or cross-contribution, between the internal standards and native metabolites because of problems such as insufficient chromatographic resolution, the data were normalized using cross-contribution compensating multiple standard normalization (CCMN) (Redestig et al. 2009).
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PubMed

Data Analysis Information

ID	D1
Title	Statistical data analysis
Data Analysis Details ID	DS1
Recommended decimal places of m/z
Comment

Data Analysis Details Information

ID	DS1
Title	Statistical data analysis
Description	Multivariate statistical analyses, comprising principal component analysis (PCA) and orthogonal projections to latent structures–discriminant analysis (OPLS-DA) (Bylesjo et al. 2006; Trygg et al. 2007), were performed using SIMCA-P 12.0 software (Umetrics AB, Umea, Sweden) with log10 transformation and unit variance scaling. The models were used to visualize the high-dimensional data and determine the metabolomic variation between the control WT and mto mutants. PCA captures the main sources of variation in an unsupervised manner, whereas OPLS-DA extracts as much of the class-separating (mutant vs. WT) variation as possible. With OPLS-DA, the variance in X (the MS profiles) is decomposed into three parts: Y-predictive, Y-orthogonal and the unmodeled residual. The Y-predictive components capture the class-separating variation (mutant vs. WT) and can be used to interpret the differences between the genotypes. The Y-orthogonal variation on the other hand models variation that is strictly unrelated to genotypic differences. With this distinction, OPLS-DA provides a convenient way to inspect the genotype-related differences without confusing them with other systematic variance coming from residual analytical bias, for example. All OPLS-DA models were validated using leave-one-out cross-validation and diagnosed using the prediction error-rate defined as the number of inaccurate predictions of left-out samples divided by the total number of samples, N. Since N was fairly low, we also computed an empirical p value, p CV, by randomizing the class labels 100 times and counting the number of times we obtained a lower error-rate than with the original labels. Statistical tests were performed using the R statistical environment (http://www.r-project.org/) on the basis of log10-transformed data. The resulting p values were adjusted for multiple testing using the false discovery rate (FDR) procedure (Benjamini and Hochberg 1995). To facilitate biological interpretation of the metabolite profiles in mto mutants, we performed metabolite set enrichment analysis (MSEA, Redestig et al. unpublished method), which is similar to gene set enrichment analysis (GSEA) (Mootha et al. 2003). The procedure of this approach was as follows: first, unification of metabolite identifiers was done using MetMask (http://metmask.sourceforge.net, Redestig et al. submitted), which is a tool for chemical identifier linking. Metabolites were then classified into 47 groups based on the classification scheme provided by the PlantCyc compound classes database version 3.0 (Zhang et al. 2005). Detected metabolites that were not mentioned by PlantCyc were classified manually. The groups of metabolites consist of metabolites that share biological function and/or biochemical pathways. Because of the rather crude grouping, we refer to the groups as “metabolite bins”, analogous to the widely used MapMan gene bins (Thimm et al. 2004). Genotype differences were estimated using the t-statistics obtained when comparing mutants and the WT. For each metabolite class the t-statistic distribution was compared with those of all other metabolites using the Kolmogorov–Smirnov test. The analysis identified classes of metabolites that were particularly affected in the corresponding comparison. The p values were FDR corrected to adjust for multiple testing.
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