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• W1999142147 abstract "Elucidating the function of each gene in a genome is important for understanding the whole organism. We previously constructed 4000 disruptant mutants of Arabidopsis by insertion of Ds transposons. Here, we describe a top-down phenomics approach based on metabolic profiling that uses one-dimensional 1H and two-dimensional 1H,13C NMR analyses and transcriptome analysis of albino mutant lines of Arabidopsis. One-dimensional 1H NMR metabolic fingerprinting revealed global metabolic changes in the albino mutants, notably a decrease in aromatic metabolites and changes in aliphatic metabolites. NMR measurements of plants fed with 13C6-glucose showed that the albino lines had dramatically different 13C-labeling patterns and increased levels of several amino acids, especially Asn and Gln. Microarray analysis of one of the albino lines revealed a unique expression profile and showed that changes in the expression of genes encoding metabolic enzymes did not correspond with changes in the levels of metabolites. Collectively, these results suggest that albino mutants lose the normal carbon/nitrogen balance, presumably mainly through lack of photosynthesis. Our study offers an idea of how much the metabolite network is affected by chloroplast function in plants and shows the effectiveness of NMR-based metabolic analysis for metabolite profiling. On the basis of these findings, we propose that future investigations of plant systems biology combine transcriptomic, metabolomic, and phenomic analyses of gene disruptant lines. Elucidating the function of each gene in a genome is important for understanding the whole organism. We previously constructed 4000 disruptant mutants of Arabidopsis by insertion of Ds transposons. Here, we describe a top-down phenomics approach based on metabolic profiling that uses one-dimensional 1H and two-dimensional 1H,13C NMR analyses and transcriptome analysis of albino mutant lines of Arabidopsis. One-dimensional 1H NMR metabolic fingerprinting revealed global metabolic changes in the albino mutants, notably a decrease in aromatic metabolites and changes in aliphatic metabolites. NMR measurements of plants fed with 13C6-glucose showed that the albino lines had dramatically different 13C-labeling patterns and increased levels of several amino acids, especially Asn and Gln. Microarray analysis of one of the albino lines revealed a unique expression profile and showed that changes in the expression of genes encoding metabolic enzymes did not correspond with changes in the levels of metabolites. Collectively, these results suggest that albino mutants lose the normal carbon/nitrogen balance, presumably mainly through lack of photosynthesis. Our study offers an idea of how much the metabolite network is affected by chloroplast function in plants and shows the effectiveness of NMR-based metabolic analysis for metabolite profiling. On the basis of these findings, we propose that future investigations of plant systems biology combine transcriptomic, metabolomic, and phenomic analyses of gene disruptant lines. Plants produce energy sources, foods, and industrial or medical materials that are necessary and useful for us. It has been expected to handle the metabolic flows so as to increase the amount of interested chemical resources in plants. For that purpose, comprehensive understanding of the metabolite network is required. Presumably, the metabolite network is regulated by both genetic information and cellular conditions. In recent years, to address the key factors regulating the metabolite network, metabolomics, the nontargeting comprehensive metabolite analysis of plants, has been introduced in plant research as well as other organisms. To elucidate the linkage between genetic information and metabolites, attempts to integrate transcriptome and metabolomics data have been made. Hirai et al. (1Hirai M.Y. Yano M. Goodenowe D.B. Kanaya S. Kimura T. Awazuhara M. Arita M. Fujiwara T. Saito K. Proc. Natl. Acad. Sci. U. S. A. 2004; 101: 10205-10210Crossref PubMed Scopus (627) Google Scholar) described the linkage between the levels of some secondary metabolites, such as glucosinolates and the transcripts for genes encoding corresponding metabolic enzymes. However, such approaches for the primary metabolic pathway did not give us clear information on the regulatory systems, presumably due to higher complexity (1Hirai M.Y. Yano M. Goodenowe D.B. Kanaya S. Kimura T. Awazuhara M. Arita M. Fujiwara T. Saito K. 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Bacteriol. 2005; 187: 5818-5830Crossref PubMed Scopus (186) Google Scholar). Completion of the Arabidopsis and rice genome sequences (14Arabidopsis Genome Initiative.Nature. 2000; 408: 796-815Crossref PubMed Scopus (7211) Google Scholar, 15Goff S.A. Ricke D. Lan T.H. Presting G. Wang R. Dunn M. Glazebrook J. Sessions A. Oeller P. Varma H. Science. 2002; 296: 92-100Crossref PubMed Scopus (2700) Google Scholar, 16Yu J. Hu S. Wang J. Wong G.K. Li S. Liu B. Deng Y. Dai L. Zhou Y. Zhang X. Cao M. Liu J. Sun J. Tang J. Chen Y. Huang X. Lin W. Ye C. Tong W. Cong L. Geng J. Han Y. Li L. Li W. Hu G. Huang X. Li W. Li J. Liu Z. Li L. Liu J. Qi Q. Liu J. Li L. Li T. Wang X. Lu H. Wu T. Zhu M. Ni P. Han H. Dong W. Ren X. Feng X. Cui P. Li X. Wang H. Xu X. Zhai W. Xu Z. Zhang J. He S. Zhang J. Xu J. Zhang K. Zheng X. Dong J. Zeng W. Tao L. Ye J. Tan J. Ren X. Chen X. He J. Liu D. Tian W. Tian C. Xia H. Bao Q. Li G. Gao H. Cao T. Wang J. Zhao W. Li P. Chen W. Wang X. Zhang Y. Hu J. Wang J. Liu S. Yang J. Zhang G. Xiong Y. Li Z. Mao L. Zhou C. Zhu Z. Chen R. Hao B. Zheng W. Chen S. Guo W. Li G. Liu S. Tao M. Wang J. Zhu L. Yuan L. Yang H. Science. 2002; 296: 79-92Crossref PubMed Scopus (2584) Google Scholar, 17Sasaki T. Matsumoto T. Yamamoto K. Sakata K. Baba T. Katayose Y. Wu J. Niimura Y. Cheng Z. Nagamura Y. Nature. 2002; 420: 312-316Crossref PubMed Scopus (458) Google Scholar) has allowed the application of systems biology to plants (18Weckwerth W. Annu. Rev. Plant Biol. 2003; 54: 669-689Crossref PubMed Scopus (537) Google Scholar, 19Sweetlove L.J. Last R.L. Fernie A.R. Plant Physiol. 2003; 132: 420-425Crossref PubMed Scopus (124) Google Scholar). Furthermore, a large number of gene disruption mutants have been constructed in various plant species, especially Arabidopsis and rice. By taking advantage of these resources, functional genomic approaches can be used to elucidate the function of plant genes (20Alonso J.M. Ecker J.R. Nat. Rev. Genet. 2006; 7: 524-536Crossref PubMed Scopus (200) Google Scholar). In a previous report, we described a phenomic approach analyzing the effects of 4000 gene disruptions in Arabidopsis (21Kuromori T. Wada T. Kamiya A. Yuguchi M. Yokouchi T. Imura Y. Takabe H. Sakurai T. Akiyama K. Hirayama T. Okada K. Shinozaki K. Plant J. 2006; 47: 640-651Crossref PubMed Scopus (95) Google Scholar). We found that fewer than 5% of the gene disruptions caused a visible phenotype in the aerial parts, suggesting that a more detailed and practical methodology is needed to determine the effects of losses in gene function. For the purpose of establishing systems biology in plants, we propose the comprehensive analysis of gene disruptant lines by a combination of metabolic fingerprinting using one-dimensional 1H NMR spectroscopy and multidimensional NMR spectroscopy with stable isotope labeling, along with transcriptomic and phenomic analyses. In the current study, we analyzed three Ds transposon insertion mutants of Arabidopsis that display albino phenotype. The albino phenotype is often due to retarded or disrupted chloroplast development. The effects of the loss of chloroplast function on the metabolic pathways, however, have not been previously investigated. Analysis of extracts from albino plants by one-dimensional 1H NMR revealed a unique metabolite profile, and detailed analysis by two-dimensional 13C heteronuclear single quantum coherence (HSQC) 3The abbreviations used are: HSQC, heteronuclear single quantum coherence; PLS-DA, partial least-squares discriminant analysis; pTAC, plastid active chromosomal protein; UHR, ultrahigh resolution. spectroscopy allowed the detection of dozens of metabolites, including amino acids. Our results indicate that the albino mutations substantially altered the metabolite profile. Microarray analysis of one of the albino lines revealed a unique pattern of gene expression. We discuss the mechanism of changes in metabolites on the basis of this microarray data and the usefulness of metabolic analysis of disruptant mutants in determining gene functions in plants. Plant Materials and Sample Preparation—Three albino mutants of Arabidopsis were obtained from the RIKEN Biological Resource Center (available on the World Wide Web at rarge.gsc.riken.jp/) (21Kuromori T. Wada T. Kamiya A. Yuguchi M. Yokouchi T. Imura Y. Takabe H. Sakurai T. Akiyama K. Hirayama T. Okada K. Shinozaki K. Plant J. 2006; 47: 640-651Crossref PubMed Scopus (95) Google Scholar, 22Kuromori T. Hirayama T. Kiyosue Y. Takabe H. Mizukado S. Sakurai T. Akiyama K. Kamiya A. Ito T. Shinozaki K. Plant J. 2004; 37: 897-905Crossref PubMed Scopus (181) Google Scholar). One of the parents of the Ds insertion lines, Ds13, was used as the control line (22Kuromori T. Hirayama T. Kiyosue Y. Takabe H. Mizukado S. Sakurai T. Akiyama K. Kamiya A. Ito T. Shinozaki K. Plant J. 2004; 37: 897-905Crossref PubMed Scopus (181) Google Scholar). The seeds were sterilized in 2.5% NaClO containing 0.1% Triton X-100 for 5 min and then washed five times with water. The sterilized seeds were kept for 4 days at 4 °C and then germinated and grown on agar plates containing half-strength Murashige and Skoog medium containing 0.5% glucose or 13C6-glucose (Cambridge Isotope Laboratories, Inc., Andover, MA). The plates were kept at 23 °C under a 16-h light/8-h dark cycle. After 3 weeks, ∼8 h into the photoperiod, the aerial parts of 20 plants were harvested randomly and immediately plunged into liquid nitrogen before freeze-drying. NMR samples were prepared essentially as described previously (23Kikuchi J. Hirayama T. Saito K. Dixon R.A. Willmitzer L. Biotechnology in Agriculture and Forestry, Plant Metabolomics. 57. Springer-Verlag, Berlin2006: 93-101Google Scholar, 24Kikuchi J. Hirayama T. Weckwerth W. Metabolomics: Methods and Protocols. Humana Press Inc., Totowa, NJ2006: 273-286Google Scholar). Briefly, 5 mg of the freeze-dried material was extracted with 600 μl of hexafluoro-acetone trideuterate at 50 °C for 5 min with gentle vortexing. After centrifugation, the extracted supernatant was transferred into a 5-mm Ø NMR tube for NMR measurements. One- and Two-dimensional NMR Measurements—One-dimensional 1H NMR spectra of unlabeled samples were acquired at 298 K on a Bruker DRX-500 NMR spectrometer equipped with a 1H inverse probe and a triple-axis gradient. 13C-Labeled plants were compared by both 13C-decoupled and coupled Watergate 1H NMR pulse sequences to assess their incorporation of 13C. Two-dimensional HSQC experiments were performed at 298 K with a Bruker DRU-700 NMR spectrometer equipped with a 1H inverse cryogenically cooled probe with a z axis gradient. A total of 200 complex f1 (13C) and 2048 complex f2 (1H) points were recorded with 36 scans per f1 increment. The spectral widths were 13,381 Hz for f1 and 11,161 Hz for f2. To quantify the signal intensities, a Lorentzian-to-Gaussian window with a Lorentzian line width of 5 Hz and a Gaussian line width of 10 Hz were applied in both dimensions before Fourier transformation. A fifth order polynomial base-line correction was subsequently applied in the f1 dimension. The indirect dimension was zero-filled to 1024 points in the final data matrix. For the 13C-13C coupling analysis, a total of 400 complex f1 points over a spectral width of 7000 Hz was acquired by ultrahigh resolution (UHR) HSQC. NMR spectra were processed using NMRPipe software (25Delaglio F. Grzesiek S. Vuister G.W. Zhu G. Pfeifer J. Bax A. J. Biomol. NMR. 1995; 6: 277-293Crossref PubMed Scopus (11638) Google Scholar, 26Kikuchi J. Shinozaki K. Hirayama T. Plant Cell Physiol. 2004; 45: 1099-1104Crossref PubMed Scopus (113) Google Scholar). The 1H and 13C chemical shifts were determined using sodium 2,2′-di-methyl 2-silapentane 5-sulfoxide as a reference. The NMR measurements were repeated three times for each sample. Quantitative Multivariate Analysis of One- and Two-dimensional NMR Spectra—The one-dimensional NMR spectra were integrated between 0.5 and 10.5 ppm over a series of 0.04-ppm integral regions using our custom integration software. After exclusion of the water resonance, each integral region was normalized to the total integral region. Two-dimensional spectral signal assignments were made using our custom software. 4E. Chikayama and J. Kikuchi, unpublished data. These well separated two-dimensional spectral signals, aided by HSQC and our data base of standards, 4E. Chikayama and J. Kikuchi, unpublished data. allowed the accurate identification of major metabolites. The data were analyzed by partial least-squares projection based on the spectral bins obtained from one- and two-dimensional spectral analyses using the pls package (version 2.0) with the “simpls” method running on R software. Microarray Analysis—Total RNA was isolated from 3-week-old plants of the control line and albino line 2198 at 6 h after the start of the light period. Total RNA was isolated using TRIzol extraction reagent (Invitrogen) and purified with an RNase MinElute Cleanup Kit (Qiagen). First strand cDNA was synthesized from 16 μg of total RNA using a SuperScript double-stranded cDNA synthesis kit (Invitrogen). The labeled cRNA was synthesized using a BioArray RNA transcript labeling kit (T7) (ENZO Life Sciences, Inc., Farmingdale, NY). After labeling, the cRNA was partially digested by incubating it for 35 min at 94 °C in 40 mm Tris acetate (pH 8.0), 100 mm potassium acetate, and 30 mm magnesium acetate. Microarray hybridization was carried out using 30 μg of cRNA. The hybridization and data detection were performed according to the manufacturer's recommendations (Affymetrix). The data were normalized by robust multiarray normalization and analyzed using affylmGUI running on R software (27Wettenhall J.M. Simpson K.M. Satterley K. Smyth G.K. Bioinformatics. 2006; 22: 897-899Crossref PubMed Scopus (178) Google Scholar). The normalized data were also analyzed by MapMan (28Thimm O. Blasing O. Gibon Y. Nagel A. Meyer S. Kruger P. Selbig J. Muller L.A. Rhee S.Y. Stitt M. Plant J. 2004; 37: 914-939Crossref PubMed Scopus (2588) Google Scholar) and Mev version 4.0 (29Saeed A.I. Sharov V. White J. Li J. Liang W. Bhagabati N. Braisted J. Klapa M. Currier T. Thiagarajan M. Sturn A. Snuffin M. Rezantsev A. Popov D. Ryltsov A. Kostukovich E. Borisovsky I. Liu Z. Vinsavich A. Trush V. Quackenbush J. BioTechniques. 2003; 34: 374-378Crossref PubMed Scopus (4021) Google Scholar) software. Primary array data sets were published in the gene expression and hybridization array data repository of the National Center for Biotechnology Information (Gene Expression Omnibus) (available on the World Wide Web at www.ncbi.nlm.nih.gov/geo/) (accession number GSE6788). Metabolite Fingerprinting Based on 1H One-dimensional NMR Spectral Analysis—In addition to T-DNA-tagged and multielement transposon-tagged lines, gene disruption lines generated using transposon-tagging systems based on the maize Activator (Ac)/Dissociation (Ds) system are very useful for genetic studies (30Smith D. Yanai Y. Liu Y.G. Ishiguro S. Okada K. Shibata D. Whittier R.F. Fedoroff N.V. Plant J. 1996; 10: 721-732Crossref PubMed Scopus (74) Google Scholar, 31Muskett P.R. Clissold L. Marocco A. Springer P.S. Martienssen R. Dean C. Plant Physiol. 2003; 132: 506-516Crossref PubMed Scopus (24) Google Scholar). To help establish systems biology in plants, we propose the systematic analysis of gene-disrupted lines by metabolic fingerprinting using one-dimensional 1H NMR spectroscopy and metabolic profiling by multidimensional NMR spectroscopy with stable isotope labeling, combined with transcriptomic and phenomic analyses (Fig. 1A). We selected three albino mutants (12-2658-1, 13-2198-1, and 15-1824-1) that have single gene disruptions from RIKEN Ds transposon stocks (21Kuromori T. Wada T. Kamiya A. Yuguchi M. Yokouchi T. Imura Y. Takabe H. Sakurai T. Akiyama K. Hirayama T. Okada K. Shinozaki K. Plant J. 2006; 47: 640-651Crossref PubMed Scopus (95) Google Scholar, 22Kuromori T. Hirayama T. Kiyosue Y. Takabe H. Mizukado S. Sakurai T. Akiyama K. Kamiya A. Ito T. Shinozaki K. Plant J. 2004; 37: 897-905Crossref PubMed Scopus (181) Google Scholar). We refer to these lines as 2658, 2198, and 1824, respectively. All three mutants had an obvious albino phenotype. Line 2658 was slightly bluish, but the three lines were otherwise nearly indistinguishable (Fig. 1B). The disrupted gene in line 2658 is At1g63970, which encodes 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase, an enzyme that catalyzes the fifth step of the nonmevalonate pathway of isoprenoid biosynthesis (32Hsieh M.H. Goodman H.M. Planta. 2006; 223: 779-784Crossref PubMed Scopus (44) Google Scholar). The biosynthesis of plastid isoprenoids is essential for chloroplast development and plant growth (32Hsieh M.H. Goodman H.M. Planta. 2006; 223: 779-784Crossref PubMed Scopus (44) Google Scholar, 33Fellermeier M. Raschke M. Sagner S. Wungsintaweekul J. Schuhr C.A. Hecht S. Kis K. Radykewicz T. Adam P. Rohdich F. Eisenreich W. Bacher A. Arigoni D. Zenk M.H. Eur. J. Biochem. 2001; 268: 6302-6310Crossref PubMed Scopus (38) Google Scholar, 34Hsieh M.H. Goodman H.M. Plant Physiol. 2005; 138: 641-653Crossref PubMed Scopus (146) Google Scholar). The gene disrupted in line 2198 was At3g04260, which encodes one of the plastid active chromosomal proteins, pTAC3 (35Pfalz J. Liere K. Kandlbinder A. Dietz K.J. Oelmuller R. Plant Cell. 2006; 18: 176-197Crossref PubMed Scopus (344) Google Scholar). pTACs are required for transcriptional or posttranscriptional processing of mRNA in plastids. T-DNA insertion mutants of other pTACs, namely pTAC2, pTAC6, and pTAC12, have albino or pale green phenotype (35Pfalz J. Liere K. Kandlbinder A. Dietz K.J. Oelmuller R. Plant Cell. 2006; 18: 176-197Crossref PubMed Scopus (344) Google Scholar), which is consistent with the albino phenotype of line 2198. In line 1824, At5g08400 was disrupted. Although the function of this gene is still unknown, its gene product is predicted to localize in chloroplasts (36Emanuelsson O. Nielsen H. Brunak S. von Heijne G. J. Mol. Biol. 2000; 300: 1005-1016Crossref PubMed Scopus (3638) Google Scholar). The obvious albino phenotype of line 1824 suggests that this gene is important for chloroplast function. We next examined the effect of the albino mutations on the cellular metabolite profile by one-dimensional 1H NMR analysis of the three albino lines and the control line. After plants were grown on plates for 3 weeks, extracts were prepared and analyzed by NMR. We did three biologically replication for each line in one experiment set. There were some clear differences in the one-dimensional 1H NMR spectra of the albino lines and the control line (Fig. 2). For example, several signals around 1.0 and 7.0 ppm, which correspond to lipids and aromatic compounds, respectively, were stronger in the spectrum of the control line. On the other hand, many signals between 2.5 and 4.0 ppm, corresponding to organic acids and sugars, were stronger in the spectra of the albino lines. To gain further insight into the metabolic changes caused by the albino mutations, we performed partial least-squares discriminant analysis (PLS-DA). In the PLS-DA, each one-dimensional 1H spectrum between 0.5 and 10.5 ppm was sub-divided into 250 segments of sequential 0.04-ppm regions (segments for the signal from water were removed). Each segment was normalized by the total intensity of the one-dimensional 1H spectrum. PLS-DA allowed differentiation of the different lines (Fig. 2). Components 1 and 2 were clearly different between the albino and control lines. A loading plot showed that chemical shifts around 2.96 and 3.72 ppm and the regions around them made strong positive contributions, and those around 0.96 and 1.32 ppm made negative contributions to component 1 (Fig. 2). These two regions correspond mainly to organic acids and lipids, respectively. For component 2, the regions around 2.96 and 3.72 ppm made positive contributions and that around 2.52, 2.72, and 4.16 ppm made a negative contribution. These results indicate that the albino mutants had a common metabolic phenotype and that the gene malfunctions had different metabolic effects in each mutant although their visible phenotypes were very similar. Metabolite Profiling and Comparison based on 1H,13C HSQC—To obtain more detailed metabolite profiles, we conducted 1H,13C HSQC measurements. For these experiments, plants were labeled with 13C6-glucose according to our previously described methods (23Kikuchi J. Hirayama T. Saito K. Dixon R.A. Willmitzer L. Biotechnology in Agriculture and Forestry, Plant Metabolomics. 57. Springer-Verlag, Berlin2006: 93-101Google Scholar, 24Kikuchi J. Hirayama T. Weckwerth W. Metabolomics: Methods and Protocols. Humana Press Inc., Totowa, NJ2006: 273-286Google Scholar). Briefly, each line was grown on medium containing 13C6-glucose. After labeling with 13C6-glucose for 3 weeks, the greening portions of each line were collected and analyzed by NMR. First, we performed 13C decoupling 1H NMR to estimate the labeling efficiency. Photosynthesis dilutes 13C incorporated from the roots with 12C incorporated from CO2. Therefore, we expected to detect the difference of labeling efficiencies between the control and albino lines. The differences between decoupled and coupled spectra were greater in albino lines than in the control line (Fig. S1A). PLS-DA showed a clear separation between the decoupled and coupled data of the albino lines (Fig. S1B). This suggests that the albino lines were labeled more efficiently, as expected. Loading plots identified the peaks that contributed to the discrimination of these samples. Peaks at 2.72, 2.56, and 4.16 ppm and at 2.64, 3.08, 4.08, and 2.24 ppm were prominent in the coupled and decoupled spectra, respectively (Fig. S1C). These peaks correspond to organic acids, such as amino acids. Although it is difficult to assign these peaks, those at 2.64 and 3.08 ppm may originate from Gln and Asn, respectively. These results support the idea that the albino mutations cause drastic changes in the metabolite profiles. We detected many 1H,13C HSQC signals in the samples (Fig. 3). These signals originated from 13C atoms bound to 1Hinthe metabolites. Because there were many peaks with the same 1H chemical shifts, two-dimensional NMR provided better discrimination of the different metabolites. To determine the molecular basis for the effects of mutations or physiological stimuli, it is necessary to know which metabolites change and by how much. Using these clearly separated signals (Fig. 3), our custom program, and data base of chemical shifts for standard metabolites, 4E. Chikayama and J. Kikuchi, unpublished data. we were able to assign 80 peaks from 22 metabolites, including amino acids and sugars (Fig. 4A and Table S1). The assignments were supported by the presence of peaks corresponding to multiple carbon atoms in each metabolite (Table S1).FIGURE 4Summary of two-dimensional HSQC metabolite analysis. A, metabolite changes in the albino mutants. The signal intensities of metabolites in albino lines were normalized by those of the control line. The signal intensities of peaks assigned to Ala β, Arg δ, Asn β, Asp β, Cys α, GABA β, Gln γ, Glu β, Gly α, His β, Ile γ2, Leu δ, Lys β2, Phe β, Pro β, Ser β1, Thr β, Trp α, Val γ1, raffinose-2, succinate, and sucrose-1 were used. The values shown are the means of three independent tests with S.D. Black box, 2658/control; white box, 2198/control; gray box, 1824/control. No mark, p < 0.01; *, 0.01 < p < 0.05; ○, p > 0.05 (Student's t test). B, a biplot representation of the PLS-DA for the assigned metabolites based on 1H,13C HSQC spectra. The signal intensities of the metabolites used in A were applied for PLS-DA. Black or white symbols and gray squares indicate PLS factors of samples and loadings for each metabolite, respectively. For loadings, only clearly separated spots were assigned.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The assignment of metabolites from PLS-DA based on the 1H,13C HSQC spectra is shown in Fig. 4B. The control and albino samples could again be separated according to component 1, suggesting a unique profile of metabolites in the albino mutants. Asn and Gln were the main contributors to the differences between the albino and control lines. Component 2 separated the albino samples. These results agreed well with those obtained by one-dimensional NMR, and confirmed that the metabolite profiles of the albino and control plants were different. The results also indicated some differences in metabolite profiles among the different albino mutants. We examined the 13C-13C coupling status using UHR HSQC. A typical example of the Asp β and Asn β peaks is shown in Fig. 5. The 13C-13C coupled signals due to Cα and Cγ in both Asp and Asn were stronger in the albino lines than in the control line. All three albino mutants exhibited clear dd couplings, whereas control signals showed a mixture of s, d+1, and d-1 couplings. The Asp β signals, however, appeared to be weaker in the albino lines than in the control line, suggesting that the weak peak strengths for Asp β were caused not by a low labeling efficiency, but rather by a low amount of this compound. Although it is difficult to estimate the precise labeling efficiency from these data, the labeling efficiency in the control line was at least half of those in the albino mutants. Similar results were found for other amino and organic acids. Therefore, the relative amounts of metabolites shown in Fig. 4A are a reasonable reflection of the actual differences in their absolute quantities. It should be noted that the absolute signal strengths were different among experiments. Presumably, the subtle difference in growth conditions, such as humidity, caused such differences (Fig. S2). However, albino samples always showed distinct metabolite profiles. We also prepared plants growing modified media that had only KNO3 for a nitrogen source. The metabolite profiles of albino mutants were different from those described above (Fig. S2). Using these data obtained from different experiment sets, the correlations between detected metabolites were analyzed (Fig. 6). Interestingly, despite the differences in the metabolite levels, high positive correlations among amide amino acids were o" @default.
• W1999142147 created "2016-06-24" @default.
• W1999142147 creator A5013716534 @default.
• W1999142147 creator A5014945517 @default.
• W1999142147 creator A5027983969 @default.
• W1999142147 creator A5033331114 @default.
• W1999142147 creator A5035950294 @default.
• W1999142147 creator A5037119242 @default.
• W1999142147 creator A5050836747 @default.
• W1999142147 date "2007-06-01" @default.
• W1999142147 modified "2023-09-27" @default.
• W1999142147 title "Top-down Phenomics of Arabidopsis thaliana" @default.
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