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First published online March 4, 2005; 10.1104/pp.104.054957 Plant Physiology 137:1302-1318 (2005) © 2005 American Society of Plant Biologists
Lotus japonicus Metabolic Profiling. Development of Gas Chromatography-Mass Spectrometry Resources for the Study of Plant-Microbe InteractionsMax Planck Institute of Molecular Plant Physiology, 14476 Golm, Germany
Symbiotic nitrogen fixation (SNF) in legume root nodules requires differentiation and integration of both plant and bacterial metabolism. Classical approaches of biochemistry, molecular biology, and genetics have revealed many aspects of primary metabolism in legume nodules that underpin SNF. Functional genomics approaches, especially transcriptomics and proteomics, are beginning to provide a more holistic picture of the metabolic potential of nodules in model legumes like Medicago truncatula and Lotus japonicus. To extend these approaches, we have established protocols for nonbiased measurement and analysis of hundreds of metabolites from L. japonicus, using gas chromatography coupled with mass spectrometry. Following creation of mass spectral tag libraries, which represent both known and unknown metabolites, we measured and compared relative metabolite levels in nodules, roots, leaves, and flowers of symbiotic plants. Principal component analysis of the data revealed distinct metabolic phenotypes for the different organs and led to the identification of marker metabolites for each. Metabolites that were enriched in nodules included: octadecanoic acid, asparagine, glutamate, homoserine, cysteine, putrescine, mannitol, threonic acid, gluconic acid, glyceric acid-3-P, and glycerol-3-P. Hierarchical cluster analysis enabled discrimination of 10 groups of metabolites, based on distribution patterns in diverse Lotus organs. The resources and tools described here, together with ongoing efforts in the areas of genome sequencing, and transcriptome and proteome analysis of L. japonicus and Mesorhizobium loti, should lead to a better understanding of nodule metabolism that underpins SNF.
The legume family comprises approximately 700 genera with more than 18,000 species, which occupy niches in almost every environment on earth (Polhill et al., 1981  ; Doyle and Luckow, 2003). A key to the success of this family was the evolution of mutualistic symbioses with bacteria of the family Rhizobiaceae, which enabled early legumes to utilize atmospheric N2 as a source of nitrogen, especially when colonizing soils lacking mineral or organic nitrogen. Today, symbiotic nitrogen fixation (SNF) by rhizobia in legumes takes place in specialized plant organs called nodules. Nodules develop from cortical cells of the root or stem after contact with rhizobia in the soil (Brewin, 1991). Mature, nitrogen-fixing nodules consist of several layers of uninfected plant cells surrounding a central zone of infected and noninfected plants cells. Infected plant cells typically contain thousands of differentiated, nitrogen-fixing rhizobia, called bacteroids, which are separated from the cytoplasm, either individually or in small groups, by a unique plant membrane called the peribacteroid or symbiosome membrane (SM; Roth et al., 1988; Udvardi and Day, 1997). Microaerobic conditions within legume nodules result from restricted oxygen influx across the outer cell layers of nodules, binding and transport of oxygen by leghemoglobin in the cytoplasm of plant cells, and high rates of respiration by bacteroids and mitochondria in these cells (Appleby, 1984). Low steady-state oxygen concentrations within nodules (in the nanomolar range) have profound effects on plant and bacterial metabolism in nodules. For instance, microaerobiosis is a prerequisite for activity of the oxygen-labile bacteroid enzyme, nitrogenase (Robson and Postgate, 1980).
SNF involves the mutually beneficial exchange of reduced carbon from the plant for reduced nitrogen from the bacteria (Udvardi and Day, 1997
In the past, most studies on legume metabolites analyzed a few compounds from preselected classes such as sugars, amino and organic acids, thiols, saponins, and phenolics, using a range of instrumentation, including HPLC (Streeter, 1987
GC-MS Chromatograms of L. japonicus Organs and Establishment of MST Libraries Gas chromatograms of nodules, lateral and primary roots, developing and mature leaves, and flowers from L. japonicus plants, harvested 12 weeks after germination and inoculation with Mesorhizobium loti strain R7A, revealed reproducible and organ-specific features (Fig 1). About 40 major polar metabolite derivatives were detectable by eye from the GC traces, together with a multitude of minor constituents, which are barely or not at all visible on the scale shown in Figure 1.
GC separates complex mixtures of metabolite derivatives into a series of compounds that enter the mass spectrometer and are subsequently ionized, fragmented, and detected. Each metabolite is, therefore, represented by one or more ionic fragments of precise mass, which together can serve as a tag for that metabolite. We have termed these MST, by analogy to expressed sequence tags of genes. Each MST has properties that facilitate unequivocal identification of the parent metabolite, following comparison to the pure reference compound (Wagner et al., 2003 ). The properties of an MST are: (1) gas chromatographic retention, which is best characterized by a retention time index (RI), and (2) a specific composition of fragments, which are each characterized by a mass-to-charge ratio (m/z). A library of MSTs was derived from a set of L. japonicus organs using the automated mass spectral deconvolution and identification system, AMDIS (Stein, 1999). Mass fragments that belong to one MST have the same RI and occur in fixed relative abundance, independent of metabolite concentration. Therefore, any single fragment or set of fragments with identical RI can be used for the quantification of metabolites. As a rule, choice of mass fragments for quantitative purposes must be selective, i.e. only those fragments that are unique to an MST can be used. Mass fragments that are common to coeluting MSTs, i.e. fragments with similar RIs and identical m/z, must be avoided for quantification purposes. In this work, fragments used for metabolite quantification were identified by m/z, RI, and name of MST to which the fragment belongs. If the MST represents a known or identified metabolite, we add the name of the respective metabolite derivative. We used the following nomenclature: m/z of the selected GC-MS fragments followed by RI and MST name both separated by the underline character; for example, mass fragment 292_2014_glucaric acid (6TMS) or 333_2014_glucaric acid (6TMS; e.g. see Fig. 4). MSTs that remain unidentified were classified tentatively by best matching mass spectra from a custom and a commercial NIST02 library (Institute of Standards and Technology, Gaithersburg, MD). A tentative match required a score >600 on a scale of 0 to 1,000. To address fragments that belong to unidentified MSTs, we use the following nomenclature: match value, and substance name of best fit, separated by a semicolon and set into brackets, e.g. 243_1930_[802; Methylcitric acid (4TMS)] (e.g. Fig. 4).
MST-Based Identification of Metabolites in Lotus Comparison of MSTs derived from Lotus organs with those of pure reference compounds enabled the identification of 87 compounds among the hundreds represented on GC-MS chromatograms (Table I). These included most of the common amino acids as well as polyamines; many organic acids, including TCA cycle intermediates; aromatic acids; sugars and sugar phosphates; and polyols (Table I). A number of likely chemical contaminants, from human or laboratory sources, or reagent impurities, including lactic acid, benzoic acid, and oligomethyl-cyclosiloxanes, were also identified.
A small set of MSTs was found to represent more than one metabolite. For example, pyroglutamic acid is formed from Gln, and, to a lesser extent, from Glu during extraction and derivatization of metabolites. However, the classification of Gln, Glu, and pyroglutamic acid into different clusters (see Fig. 6) indicated minimal cross-contamination in this analysis. Arg and citrulline may be converted completely into Orn during chemical derivatization. In our analyses, no specific derivatives of Arg or citrulline were found. Thus, the MST of Orn represented the sum of endogenous Arg, citrulline, and Orn.
Manual inspection of GC-MS chromatograms indicated major similarities in metabolism of developing and mature leaves, as well as similarities between lateral and primary roots (Fig. 1). To analyze similarities and differences numerically, we performed automated peak integration using 1,046 mass spectral fragments, representative of about 500 MSTs. MSTs representing known or unknown metabolites were analyzed, as a rule, using one to four specific mass spectral fragments within the respective retention time window (see "Materials and Methods" section "Generation of a Metabolite Response Matrix"). Choice of fragment mass and retention time window was performed manually and was facilitated by nonsupervised collection of MSTs (Colebatch et al., 2004 The first 5 principal components derived from the above data matrix encompassed 77.3% of the total variance from this data set (Fig. 2). The first component accounted for 37.1% of the variance and allowed distinction of shoot organs from root organs (Fig. 2A). Nodules exhibited more similarity to roots than to shoot organs according to the first component. However, the second component, which comprised 17.8% of the variance, demonstrated that the data set contained metabolite measurements that distinguished between nodule and root profiles (Fig. 2A). Subsequent principal components revealed other differences between the various organs. Thus, the third and fourth components, encompassing 11.2% and 7.3% of the variance, respectively, indicated that general markers for flowers and primary roots exist (Fig. 2B). The fifth component clearly separated developing leaves from other organs (Fig. 2C). No subsequent components allowed a clear discrimination between sample types (e.g. Fig. 2C, component 6). The organ samples described above were harvested at one developmental stage, but at different times of a single day/night cycle. No principal component was found that reflected diurnal changes in metabolism. This finding indicated that diurnal changes resulted in only minor changes in metabolite profiles compared to those resulting from organ development and differentiation. PCA analysis of leaf samples only indicated some diurnal changes in this organ (data not shown). However, the small number of samples from each time point prohibited identification of significant shifts in leaf metabolism during the diurnal cycle. To test the robustness of PCA in distinguishing between different organs, we analyzed GC-MS data from plants harvested over a 10-week period (7–17 weeks after germination), following growth under different culture conditions in different seasons (Fig. 3A). This data set was expected to be more variable than the first. In fact, this appeared to be the case, and PCA analysis proved less successful in distinguishing between samples of different organs (Fig. 3A). Nonetheless, nodule and root samples were mostly separated from shoot organs by component 1 of PCA, which accounted for 22.9% of the total variance. The 2 subsequent components covered a sum of 14.6% of variance but did not yield distinctions between the samples that could be linked to organ age or plant growth conditions. However, the fourth component, which encompassed 5.5% of the variance, separated nodule samples from those of other organs (Fig. 3A).
PCA Analysis Reveals Specific Metabolites That Distinguish Different Organs The contribution of each metabolite to a specific component is reflected by the loading value derived from PCA analysis. Those metabolites with highest loading values are indicated to have the strongest influence on the respective characteristics of a component. We focused on the loading values of components 1 and 2 of experiment 1 (Fig. 2). The 25 most influential fragment masses of each component were analyzed (Fig. 4, A and B). The first component, which described the root to shoot difference, was influenced most by Pro, Gln, erythronic acid, and glucaric acid. The second component, which revealed differences between nodules and other organs, was influenced most by Asn, Gln, Glu, Trp, and octadecanoic acid. Multiple MSTs of unidentified compounds were also found to contribute substantially to components 1 and 2. We selected MST [802; Methylcitric acid (4TMS)] and an identified metabolite, glucaric acid, to demonstrate the possibility of gaining biological insight about a compound, even if its chemical identity is unknown. The choice of these two compounds was made with reference to data from the second experiment described above (Fig. 3). Glucaric acid was found to be important for root and shoot distinction: fragment masses 292, 333, and 373 at RI 2,014 (Fig. 3B). The unknown MST [802; Methylcitric acid (4TMS)] was also found to be a reproducible marker of nodules: fragment 243 at RI 1,929 (Fig. 3B).
MST [802; Methylcitric acid (4TMS)], as the nomenclature indicates, was found to be highly similar to a typical bacterial metabolite, 2-methylcitric acid. This match was found in the commercially available NIST02 mass spectral library (Ausloos et al., 1999
Despite the lack of a specific structure for MST [802; Methylcitric acid (4TMS)], we investigated the distribution pattern of the underlying metabolite in Lotus organs (Fig. 5B). MST [802; Methylcitric acid (4TMS)] was found at high levels in nodule samples, while all other organs contained only traces of this compound. In contrast, DL-2-methylcitric acid was relatively high in nodules and flowers but low in lateral roots (Fig. 5B). These results indicate MST [802; Methylcitric acid (4TMS)] is a good marker substance for nodules, while DL-2-methylcitric acid is more evenly distributed throughout the plant. Furthermore, we found glucaric acid to be low in roots and nodules but high in leaves and flowers. Thus, this compound was confirmed as a good marker for shoot organs.
As illustrated in Figure 5B, metabolites were found to exhibit specific distribution patterns in the organs of Lotus. We applied HCA to the MST distribution of all 87 identified and 49 unidentified compounds that were among the top 25 most discriminatory from PCA analysis (Figs. 1 and 4). Only one mass fragment was used for each MST in this analysis. Following HCA, we grouped the MSTs into 12 classes (Fig. 6). Two of the classes, 1 and 4, were occupied by known laboratory contaminants and were excluded from further analysis. The properties of the remaining 10 classes were investigated further (Fig. 7). Class 2 contained metabolites that were present at relatively high levels in nodules and at low or intermediate levels in other organs. MST [802; Methylcitric acid (4TMS)], like Glu, Asn, putrescine, and mannitol, were characteristic members of this class. Class 3 compounds, with relatively high levels in nodules and leaves, had only 2 members, raffinose and 2-ketoglutaric acid. Class 5, which had high levels in nodules and flowers, comprised DL-2-methylcitric acid and 10 other compounds, including Glc-6-P and 2-aminoadipic acid. Class 6 was the dominant metabolite class and comprised metabolites with high levels in flowers only, for example Pro, Val, Trp, ononitol, and Gln. Classes 7 and 8 were similar and contained metabolites enriched in shoot organs, such as glucaric acid. However, class 8 contained metabolites that had high levels in all above-ground organs, while class 7 metabolites had reduced levels in mature leaves. Class 9 metabolites exhibited relatively high levels in shoot organs and in primary roots. Class 10 contained metabolites with low levels in nodules only. Class 11 and 12 metabolites exhibited high levels in mature leaves. While metabolites of class 12 were also present at high levels in developing leaves, class 11 metabolites had only low or medium levels in other organs. Detailed information on the class membership of each MST, together with short descriptions of each class, is included in Table I.
PCA analysis pointed to metabolites that may be important for organ differentiation. Metabolite clustering by HCA resulted in a rough overview of general metabolite distribution patterns. As an extension to these analyses, ANOVA was used to assess the statistical significance of differences in the distribution of each metabolite. Seven comparisons of organs and groups of organs were performed. These comparisons were motivated by sample classifications made evident by PCA analysis: (1) comparison of nodule with all other plant samples; (2) comparison of nodule with root samples; (3) comparison of below-ground with above-ground samples; (4) comparison of root samples with shoot, including flower samples; (5) comparison of flower samples with all other samples; (6) comparison of lateral and primary roots; and (7) comparison of developing and mature leaves. Differences in metabolite levels were calculated as ratios and compiled in Table I. Differences in metabolite levels that were significant at P Amino acids exhibited two major sites of accumulation: nodules and flowers. Asn, homoSer, Glu, and Cys levels were significantly higher in nodules than in other organs (Table I). In contrast, most other amino acids, especially Trp, Pro, Leu, Val and Gln, were enriched in flowers. Most amino acids were present at higher levels in leaves than in roots. Developing leaves contained higher concentrations of most amino acids than mature leaves, although most differences were not significant. Only 4 identified organic acids accumulated significantly in nodules compared to all other organs: octadecanoic acid, threonic acid, gluconic acid, and 2-methylcitric acid. Like amino acids, most organic acids were present at significantly lower levels in roots than in leaves. Some organic acids, for example GlcUA and quinic acid, were highly enriched in flowers. Massive accumulation of the N-containing compounds, uric acid, allantoin, and urea was also found in flowers. In contrast, putrescine levels were higher in nodules and roots than in other organs (Table I). Sugars exhibited variable organ distribution patterns. Most striking was the accumulation of Xyl, Ara, and Gal in flowers. Nodules exhibited significant accumulation of Rib and raffinose compared to other organs. Whereas most sugars were present at similar levels in roots and leaves, Glc and Fru were significantly higher in roots and especially in lateral roots. Sugar alcohols and sugar phosphates were distributed more evenly throughout the plant but exhibited a tendency to be low in roots. Only mannitol, glyceric acid-3-P, and glycerol-3-P exhibited significant accumulation in nodules. Ononitol, pinitol, and galactitol were relatively high in flowers. Developing leaves accumulated a range of phosphorylated compounds, especially phosphoric acid and Glc-6-P. Most of the MSTs of unidentified compounds, which were selected from the top-ranking loading values of PCA components 1 to 5, exhibited significant differences that substantiated their importance as markers for nodules, roots, or shoot organs. Among these were MSTs that showed the most extreme changes between organs, such as MSTs [957; Suberylglycine (3TMS)] and [802; Methylcitric acid (4TMS)] (Figs. 5 and 7).
Current methods of metabolome analysis are far from comprehensive. We selected the GC-MS based method reported earlier (Fiehn et al., 2000 ; Roessner et al., 2000), which allows analysis of the low to medium Mr, soluble, polar metabolic complement and comprises primary metabolites, such as 24 major and minor amino acids; 29 hydroxylated, nonhydroxylated and aromatic organic acids; 4 amines and amides; 13 mono-, di-, and tri-saccharides; 10 polyols; and 7 phosphorylated compounds (Table I). The choice of this specific GC-MS approach was motivated by the almost complete coverage of those metabolite classes that have received attention in past studies of SNF, namely amino acids, carbohydrates, and organic acids. This enabled validation of some of the GC-MS data by comparison with published data. Although not as comprehensive as transcript profiles derived from whole-genome oligonucleotide arrays, metabolite profiles allow similar insights at the metabolic level. Biological samples can be classified according to their metabolic phenotype, e.g. the quantitative and qualitative make-up of the metabolome (Figs. 2 and 3); and metabolites, like gene transcripts, can be classified according to their distribution within the various samples under investigation (Figs. 6 and 7). Thus, sets of metabolites can be identified that are not only of diagnostic value but also may indicate the function of metabolites in certain organs or under certain conditions. Below, we discuss three aspects of metabolite profiling: (1) analytical and technical aspects; (2) the data mining strategy applied in this study; and (3) the potential for new insight into a biological process such as SNF that is afforded by comparative metabolomics.
The metabolome of an organism is subject to rapid, enzyme-catalyzed change in response to environmental as well as endogenous factors. Photosynthetic organisms like plants, for instance, undergo profound diurnal changes in metabolism that are related to the light-dark cycle. It is important to take this into account by standardizing growth conditions and synchronizing harvesting times. Equally important is the fact that enzymatic and nonenzymatic conversion of a metabolite to one or more products does not necessarily cease at the time of harvest. Precautions must be taken to avoid such conversions during harvesting, storage, extraction, and derivatization of metabolites (Kopka et al., 2004
Metabolite profiling data from GC-MS are highly complex, which presents challenges for identification and quantification of metabolites. Unfortunately, bioinformatics tools for automated processing of data are less well-developed for metabolomics than for proteomics and transcriptomics. Currently, the full nonredundant inventory of metabolites from one set of GC-MS profiles cannot be assessed without time consuming manual curation. For this reason, we produced so-called nonsupervised collections of redundant mass spectra from automatically generated mass spectral deconvolution of representative chromatograms (Wagner et al., 2003
A preliminary overview of general similarities and differences between samples is a useful first step in data analysis. Visual inspection of chromatograms is insufficient for this purpose (Fig. 1A). HCA and PCA have been widely applied for data reduction to avoid the need to check each single metabolite for relevant changes (e.g. Roessner et al., 2001a From the set of 136 MSTs that were identified by PCA to have potential diagnostic properties, we selected 2 MSTs representing a known (glucaric acid) and an unknown compound [802; Methylcitric acid (4TMS)] for further analysis (Fig. 5A). Validation of the diagnostic value of these metabolites was performed via PCA analysis of a second experiment, in which plants were grown under more variable conditions. Once again, both compounds were among the most influential metabolites separating root from shoot, and root from nodule samples, respectively (Fig. 3B). A subsequent analysis of the distribution pattern (Fig. 5) clearly supported the diagnostic properties of the selected compounds. After having established compound relevance we manually confirmed compound identity for glucaric acid and found mass spectral similarity of MST [802; Methylcitric acid (4TMS)] to methylcitric acid. Subsequent standard addition experiments with commercially available DL-2-methylcitric acid confirmed this similarity. As a by-product of this work, we discovered true DL-2-methylcitric acid among the MSTs of unidentified compounds and were able to add this novel identification and its distribution pattern to the set of fully characterized MSTs (Fig. 5B). In the absence of additional commercially available candidate reference substances, further attempts to identify MST [802; Methylcitric acid (4TMS)] will require time-consuming chemical purification of the compound and structural characterization, by NMR analysis, for example. HCA analysis was applied to all 87 MSTs representing known metabolites and to the 49 representing unidentified compounds, which were found to be interesting from PCA analysis (Fig. 6). HCA demonstrated that the distribution patterns of glucaric acid and MST, [802; Methylcitric acid (4TMS)] were not unique but that both metabolites belonged to groups of metabolites with similar distribution, namely class 2 comprising 24 nodule-enriched metabolites, which were enriched in this organ by between 2- and 200-fold, and class 8 comprising 20 shoot enriched metabolites, which were depleted in roots and nodules by between 2- and 50-fold (Table I). Moreover, HCA supported distribution patterns that were indicated by PCA; e.g. 13 flower-enriched metabolites of class 6 accumulated by more than 10-fold (Table I; Fig. 7). Although HCA allowed rough classification, ANOVA was required to distinguish between those metabolites that were significantly enriched and those that were not. Table I comprises all relevant comparisons that were performed and demonstrates a multitude of significant metabolite enrichments with factors >10-fold or <0.1-fold.
PCA analysis revealed that many compounds were enriched in nodules compared to other plant organs, including Asn, Glu, Gln, homoSer, Cys, putrescine, mannitol, threonic acid, gluconic acid, glyceric acid-3-P, glycerol-3-P, and octadecanoic acid. Some of these differences were expected and confirm what is known about nodule metabolism. For instance, SNF is a source of ammonium for amino acid biosynthesis and many legumes, including Lotus export fixed nitrogen in the form of amines, especially Asn and Gln (Vance et al., 1987
A number of compatible solutes, which typically accumulate in plants in response to osmotic stress, were found to be elevated in nodules compared to roots and other organs. These included the polyols, ononitol, mannitol, and sorbitol; the amino acid, Pro; and the polyamine, putrescine (Table I). Accumulation of osmoprotectants in nodules may indicate that cells in this organ are subject to osmotic stress. Hypoxia, which can cause osmotic stress in plant cells via effects on water uptake and loss (Nuccio et al., 1999
Relatively high levels of Cys were found in nodules (Table I), which is unusual for plant tissues. Two genes encoding Cys synthases were found to be expressed at higher levels in nodules than in roots of Lotus (Colebatch et al., 2004 While it is not possible to gauge from our GC-MS data the separate contribution that bacteroids make to most metabolite pools, some of the unusual and unidentified compounds that accumulate in nodules, e.g. [802; Methylcitric acid (4TMS)], may be exclusively bacterial products (Table I). Elucidation of the structures of these compounds and their biosynthetic origin will undoubtedly lead to a better understanding of nodule metabolism and the metabolic interactions between legumes and rhizobia. Another important area for future work is metabolic flux determination in nodules. The data presented here give a static picture of metabolite levels averaged over whole organs and provide little insight into metabolic compartmentation or flux though specific pathways. Nonetheless, the resources developed during this project, e.g. MST libraries, will provide a firm basis upon which to build such studies in the future.
Biological Material, Plant Growth, and Harvesting
Lotus japonicus cv. GIFU seeds were scarified 3 x 10 s in liquid nitrogen, sterilized 10 min in 2% bleach solution, rinsed 5 times with sterile distillated water, and moved to a petri dish with filter paper soaked in B&D medium (Broughton and Dilworth, 1971 Two sets of experiments were performed. The first set comprised plant material harvested 12 weeks after germination in the course of 1 diurnal cycle, at 2, 8, and 14 h within the light cycle and at 2, 4, and 6 h during the dark period, respectively. While the diurnal changes were not a topic of this investigation, an equal representation of all diurnal stages was generated for a nonbiased organ-to-organ comparison. A second set of experiments was performed in the course of 6 months, early summer to winter. Samples were taken randomly in the middle of the light cycle at different developmental stages, 7 to 17 weeks after germination. Plants were cultivated either in an open pot or a closed glass jar. This experimental set was expected to be highly variable but allowed to verify persistent metabolic features of L. japonicus organs. At each harvest, plants were carefully pulled from the quartz sand and a complete set of six organ samples prepared by immediate shock freezing in liquid nitrogen, i.e. nodules, lateral and primary root, mature and developing leaves, and flowers. Leaves were separated according to morphological criteria into a group of young expanding leaves from the apex of the plant and a group of mature fully expanded leaves from the middle of the plant shoot. Senescent leaves were discarded. Whole flowers were prepared including all floral organs, petals, sepals, carpels, stamen, and pollen. Lateral roots without visible nodule primordia were collected, followed by pink nodules sampled in a representative range of various sizes. The harvest was completed by preparing the primary root, i.e. 2 cm of the main root directly below the hypocotyl. Only samples without nodules and lateral roots were collected. Primary root material had to be sliced before shock freezing to improve subsequent grinding under liquid nitrogen. Samples were stored for a maximum of 4 weeks at –80°C until GC-MS analysis.
Frozen samples of 25 to 50 mg fresh weight were ground for 2 min in 2-mL micro vials with a clean stainless steel metal ball (5-mm diameter) using a ball mill grinder (MM200, Retsch, Haan, Germany) set to 30 cycles/s. All material was thoroughly precooled in liquid nitrogen. Frozen powder was extracted with hot MeOH/CHCl3 and the fraction of polar metabolites prepared by liquid partitioning into water as described earlier (Wagner et al., 2003
MSTs are defined as full mass spectra obtained from GC-MS chromatograms. MSTs are described by chromatographic retention, for example RI, and mass spectrum, i.e. a set of fragments that are characterized by m/z and relative fragment intensity and normalized to the most abundant fragment of the MS. MSTs represent chemical derivatives of metabolites or metabolites that are not derivatized. MSTs of unidentified compounds can be identified in later experiments by exploiting the above characteristics in standard addition experiments of pure reference substances to the complex biological matrix of interest. Each mass fragment that belongs to one MST can be used for quantification, and we name these fragments through combination of a single m/z and RI from the MST: for example, fragment 279_2758 below. The best fragment for quantification is generally the most abundant one. However, since metabolite profiles comprise hundreds of MSTs of identified and unidentified compounds, mass fragments need to be highly selective. Because metabolite profiles may contain unexpected, novel MSTs, we analyze multiple fragments per MSTs. If all fragments of one MST exhibit the same relative change, we automatically select the most abundant fragment for quantification. If fragment ratios exhibit discrepancies, we manually overrule the automatic choice and select the next best specific fragment for quantification.
Reference substances for standard addition experiments were from Sigma-Aldrich (Schnelldorf, Germany) except for DL-2-methylcitric acid, which was obtained from C/D/N Isotopes (Pointe-Claire, Quebec, Canada). Lactic acid and benzoic acid were laboratory contaminations as was oligomethyl-cyclosiloxane, monitored by mass fragment 279_2758. This compound was a chemical artifact caused by the N-methyl-N-(trimethylsilyl)-trifluoroacetamide reagent. Mass spectra were analyzed by AMDIS software (http://chemdata.nist.gov/mass-spc/amdis/; National Institute of Standards and Technology) and compared with commercial and user libraries in NIST02 format (http://chemdata.nist.gov/mass-spc/Srch_v1.7/index.html; National Institute of Standards and Technology). L. japonicus MSTs are made available via the Internet at the CSB.DB resource (http://csbdb.mpimp-golm.mpg.de/gmd.html).
We manually selected one or more specific mass fragments and corresponding retention time windows for identified and still unidentified MSTs from L. japonicus. The find algorithm of the MassLab 1.4v software (ThermoQuest, Manchester, UK) was used to automatically retrieve peak areas and chromatographic retention from GC-MS metabolite profiles. Peak identification and area integration was manually supervised as described above. Peak areas with low intensity were rejected.
In accordance with Colebatch et al. (2004)
PCA was performed after log10 transformation of the relative responses, log10(Ri). Missing values were either manually replaced, in the case of identified MSTs, or defined as average of the respective sample group after log10 transformation. If no response was retrievable for any of the samples of a specific organ, log10(Ri) = 0 was substituted for PCA analysis. HCA was applied to classify MSTs, which represented identified metabolites and a selection of unidentified metabolites, according to their relative abundance in different organs. For this purpose, average normalized responses (avgNi) were calculated of each MST and organ. Missing data were substituted by the normalized response at detection limit. HCA was performed after range normalization using Euclidian distance and average linkage. All procedures including ANOVA and visualization were performed with EXCEL software and the S-Plus 2000 software package standard edition release 3 (Insightful, Berlin Germany), and multivariate and cluster analysis was essentially as reported earlier (Colebatch et al., 2004
The authors thank Nicole Gatzke, Cornelia Wagner, and Katrin Bieberich for their patient assistance and technical expertise. The authors greatly appreciate Dr. Andreas Richter (Institute of Ecology and Conservation Biology, Vienna, Austria) for making pinitol and ononitol available for GC-MS standard addition experiments. Received October 20, 2004; returned for revision December 8, 2004; accepted December 12, 2004.
1 Present address: Université Montpellier 2, CC 002, Place Eugène Bataillon, F–34095 Montpellier cedex 05, France.  Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.104.054957. * Corresponding author; e-mail udvardi{at}mpimp-golm.mpg.de; fax 49–331–567–8250.
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TOP
ABSTRACT
RESULTS
0.01 are indicated in bold format.
RI = 0.3 to 0.5), as described in (Wagner et al., 2003