Alexander Erban

TagFinder for the quantitative analysis of gas chromatography—mass spectrometry (GC-MS)-based metabolite profiling experiments

MOTIVATION Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. STRATEGY TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. AVAILABILITY Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.

Downregulation of cinnamoyl-coenzyme A reductase in poplar: multiple-level phenotyping reveals effects on cell wall polymer metabolism and structure.

Cinnamoyl-CoA reductase (CCR) catalyzes the penultimate step in monolignol biosynthesis. We show that downregulation of CCR in transgenic poplar (Populus tremula × Populus alba) was associated with up to 50% reduced lignin content and an orange-brown, often patchy, coloration of the outer xylem. Thioacidolysis, nuclear magnetic resonance (NMR), immunocytochemistry of lignin epitopes, and oligolignol profiling indicated that lignin was relatively more reduced in syringyl than in guaiacyl units. The cohesion of the walls was affected, particularly at sites that are generally richer in syringyl units in wild-type poplar. Ferulic acid was incorporated into the lignin via ether bonds, as evidenced independently by thioacidolysis and by NMR. A synthetic lignin incorporating ferulic acid had a red-brown coloration, suggesting that the xylem coloration was due to the presence of ferulic acid during lignification. Elevated ferulic acid levels were also observed in the form of esters. Transcript and metabolite profiling were used as comprehensive phenotyping tools to investigate how CCR downregulation impacted metabolism and the biosynthesis of other cell wall polymers. Both methods suggested reduced biosynthesis and increased breakdown or remodeling of noncellulosic cell wall polymers, which was further supported by Fourier transform infrared spectroscopy and wet chemistry analysis. The reduced levels of lignin and hemicellulose were associated with an increased proportion of cellulose. Furthermore, the transcript and metabolite profiling data pointed toward a stress response induced by the altered cell wall structure. Finally, chemical pulping of wood derived from 5-year-old, field-grown transgenic lines revealed improved pulping characteristics, but growth was affected in all transgenic lines tested.

Phosphorus Stress in Common Bean: Root Transcript and Metabolic Responses

Phosphorus (P) is an essential element for plant growth. Crop production of common bean (Phaseolus vulgaris), the most important legume for human consumption, is often limited by low P in the soil. Functional genomics were used to investigate global gene expression and metabolic responses of bean plants grown under P-deficient and P-sufficient conditions. P-deficient plants showed enhanced root to shoot ratio accompanied by reduced leaf area and net photosynthesis rates. Transcript profiling was performed through hybridization of nylon filter arrays spotted with cDNAs of 2,212 unigenes from a P deficiency root cDNA library. A total of 126 genes, representing different functional categories, showed significant differential expression in response to P: 62% of these were induced in P-deficient roots. A set of 372 bean transcription factor (TF) genes, coding for proteins with Inter-Pro domains characteristic or diagnostic for TF, were identified from The Institute of Genomic Research/Dana Farber Cancer Institute Common Bean Gene Index. Using real-time reverse transcription-polymerase chain reaction analysis, 17 TF genes were differentially expressed in P-deficient roots; four TF genes, including MYB TFs, were induced. Nonbiased metabolite profiling was used to assess the degree to which changes in gene expression in P-deficient roots affect overall metabolism. Stress-related metabolites such as polyols accumulated in P-deficient roots as well as sugars, which are known to be essential for P stress gene induction. Candidate genes have been identified that may contribute to root adaptation to P deficiency and be useful for improvement of common bean.

Transcript and metabolite profiling of the adaptive response to mild decreases in oxygen concentration in the roots of Arabidopsis plants.

Background and Aims Oxygen can fall to low concentrations within plant tissues, either because of environmental factors that decrease the external oxygen concentration or because the movement of oxygen through the plant tissues cannot keep pace with the rate of oxygen consumption. Recent studies document that plants can decrease their oxygen consumption in response to relatively small changes in oxygen concentrations to avoid internal anoxia. The molecular mechanisms underlying this response have not been identified yet. The aim of this study was to use transcript and metabolite profiling to investigate the genomic response of arabidopsis roots to a mild decrease in oxygen concentrations. Methods Arabidopsis seedlings were grown on vertical agar plates at 21, 8, 4 and 1 % (v/v) external oxygen for 0·5, 2 and 48 h. Roots were analysed for changes in transcript levels using Affymetrix whole genome DNA microarrays, and for changes in metabolite levels using routine GC-MS based metabolite profiling. Root extension rates were monitored in parallel to investigate adaptive changes in growth. Key Results The results show that root growth was inhibited and transcript and metabolite profiles were significantly altered in response to a moderate decrease in oxygen concentrations. Low oxygen leads to a preferential up-regulation of genes that might be important to trigger adaptive responses in the plant. A small but highly specific set of genes is induced very early in response to a moderate decrease in oxygen concentrations. Genes that were down-regulated mainly encoded proteins involved in energy-consuming processes. In line with this, root extension growth was significantly decreased which will ultimately save ATP and decrease oxygen consumption. This was accompanied by a differential regulation of metabolite levels at short- and long-term incubation at low oxygen. Conclusions The results show that there are adaptive changes in root extension involving large-scale reprogramming of gene expression and metabolism when oxygen concentration is decreased in a very narrow range.

Retention index thresholds for compound matching in GC-MS metabolite profiling

The generation of retention index (RI) libraries is an expensive and time-consuming effort. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared use. The precision of RI determination and accuracy of RI transfer between 8 method variants employing 5%-phenyl-95%-dimethylpolysiloxane capillary columns was investigated using a series of 9 n-alkanes (C(10)-C(36)). The precision of the RI determination of 13 exemplary fatty acid methyl esters (C(8) ME-C(30) ME) was 0.22-0.33 standard deviation (S.D.) expressed in RI units in low complexity samples. In the presence of complex biological matrices this precision may deteriorate to 0.75-1.11. Application of the previously proposed Kováts, van den Dool or 3rd-5th order polynomial regression algorithms resulted in similar precision of RI calculation. For transfer of empirical van den Dool-RI properties between the chromatography variants 3rd order regression was found to represent the minimal necessary assumption. The range of typical regression coefficients was r(2)=0.9988-0.9998 and accuracy of RI prediction between chromatography variants varied between 5.1 and 19.8 (0.29-0.69%) S.D. of residual RI error, RI(predicted)-RI(determined) (n>64). Accuracy of prediction was enhanced when subsets of chemically similar compound classes were used for regression, for example organic acids and sugars exhibited 0.78 (n=29) and 3.74 (n=37) S.D. of residual RI error, respectively. In conclusion, we suggest use of percent RI error rather than absolute RI units for the definition of matching thresholds. Thresholds of 0.5-1.0% may apply to most transfers between chromatography variants. These thresholds will not solve all matching ambiguities in complex samples. Therefore, we recommend co-analysis of reference substances with each GC-MS profiling experiment. Composition of these defined reference mixtures may best approximate or mimic the quantitative and qualitative composition of the biological matrix under investigation.

Comprehensive dissection of spatio-temporal metabolic shifts in primary, secondary and lipid metabolism during developmental senescence in Arabidopsis thaliana

Spatiotemporal analysis during developmental senescence provides a rich catalog of metabolites in relation to leaf and silique development in Arabidopsis. Developmental senescence is a coordinated physiological process in plants and is critical for nutrient redistribution from senescing leaves to newly formed sink organs, including young leaves and developing seeds. Progress has been made concerning the genes involved and the regulatory networks controlling senescence. The resulting complex metabolome changes during senescence have not been investigated in detail yet. Therefore, we conducted a comprehensive profiling of metabolites, including pigments, lipids, sugars, amino acids, organic acids, nutrient ions, and secondary metabolites, and determined approximately 260 metabolites at distinct stages in leaves and siliques during senescence in Arabidopsis (Arabidopsis thaliana). This provided an extensive catalog of metabolites and their spatiotemporal cobehavior with progressing senescence. Comparison with silique data provides clues to source-sink relations. Furthermore, we analyzed the metabolite distribution within single leaves along the basipetal sink-source transition trajectory during senescence. Ceramides, lysolipids, aromatic amino acids, branched chain amino acids, and stress-induced amino acids accumulated, and an imbalance of asparagine/aspartate, glutamate/glutamine, and nutrient ions in the tip region of leaves was detected. Furthermore, the spatiotemporal distribution of tricarboxylic acid cycle intermediates was already changed in the presenescent leaves, and glucosinolates, raffinose, and galactinol accumulated in the base region of leaves with preceding senescence. These results are discussed in the context of current models of the metabolic shifts occurring during developmental and environmentally induced senescence. As senescence processes are correlated to crop yield, the metabolome data and the approach provided here can serve as a blueprint for the analysis of traits and conditions linking crop yield and senescence.

Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.

Metabolic labeling of plant cell cultures with K(15)NO3 as a tool for quantitative analysis of proteins and metabolites.

Strategies for robust quantitative comparison between different biological samples are of high importance in experiments that address biological questions beyond the establishment of protein lists. Here, we propose the use of 15N-KNO3 as the only nitrogen source in Arabidopsis cell cultures in order to achieve a metabolically fully labeled cell population. Proteins from such metabolically labeled culture are distinguishable from unlabeled protein populations by a characteristic mass shift that depends on the amino acid composition of the tryptic peptide analyzed. In addition, the metabolically labeled cell extracts are also suitable for comparative quantitative analysis of nitrogen-containing cellular metabolic complement. Protein extracts from unlabeled and from standardized 15N-labeled cells were combined into one sample for joined analytical processing. This has the advantage of (i) reduced experimental variability and (ii) immediate relative quantitation at the level of single extracted peptide and metabolite spectra. Together ease and accuracy of relative quantitation for profiling experiments is substantially improved. The metabolic labeling strategy has been validated by mixtures of protein extracts and metabolite extracts from the same cell cultures in known ratios of labeled to unlabeled extracts (1:1, 1:4, and 4:1). We conclude that saturating metabolic 15N-labeling provides a robust and affordable integrative strategy to answer questions in quantitative proteomics and nitrogen focused metabolomics.

Predicting Arabidopsis freezing tolerance and heterosis in freezing tolerance from metabolite composition.

Heterosis, or hybrid vigor, is one of the most important tools in plant breeding and has previously been demonstrated for plant freezing tolerance. Freezing tolerance is an important trait because it can limit the geographical distribution of plants and their agricultural yield. Plants from temperate climates increase in freezing tolerance during exposure to low, non-freezing temperatures in a process termed ‘cold acclimation’. Metabolite profiling has indicated a major reprogramming of plant metabolism in the cold, but it has remained unclear in previous studies which of these changes are related to freezing tolerance. In the present study, we have used metabolic profiling to discover combinations of metabolites that predict freezing tolerance and its heterosis in Arabidopsis thaliana. We identified compatible solutes and, in particular, the pathway leading to raffinose as crucial statistical predictors for freezing tolerance and its heterosis, while some TCA cycle intermediates contribute only to predicting the heterotic phenotype. This indicates coordinate links between heterosis and metabolic pathways, suggesting that a limited number of regulatory genes may determine the extent of heterosis in this complex trait. In addition, several unidentified metabolites strongly contributed to the prediction of both freezing tolerance and its heterosis and we present an exemplary analysis of one of these, identifying it as a hexose conjugate.

Global changes in the transcript and metabolic profiles during symbiotic nitrogen fixation in phosphorus-stressed common bean plants.

Phosphorus (P) deficiency is widespread in regions where the common bean (Phaseolus vulgaris), the most important legume for human consumption, is produced, and it is perhaps the factor that most limits nitrogen fixation. Global gene expression and metabolome approaches were used to investigate the responses of nodules from common bean plants inoculated with Rhizobium tropici CIAT899 grown under P-deficient and P-sufficient conditions. P-deficient inoculated plants showed drastic reduction in nodulation and nitrogenase activity as determined by acetylene reduction assay. Nodule transcript profiling was performed through hybridization of nylon filter arrays spotted with cDNAs, approximately 4,000 unigene set, from the nodule and P-deficient root library. A total of 459 genes, representing different biological processes according to updated annotation using the UniProt Knowledgebase database, showed significant differential expression in response to P: 59% of these were induced in P-deficient nodules. The expression platform for transcription factor genes based in quantitative reverse transcriptase-polymerase chain reaction revealed that 37 transcription factor genes were differentially expressed in P-deficient nodules and only one gene was repressed. Data from nontargeted metabolic profiles indicated that amino acids and other nitrogen metabolites were decreased, while organic and polyhydroxy acids were accumulated, in P-deficient nodules. Bioinformatics analyses using MapMan and PathExpress software tools, customized to common bean, were utilized for the analysis of global changes in gene expression that affected overall metabolism. Glycolysis and glycerolipid metabolism, and starch and Suc metabolism, were identified among the pathways significantly induced or repressed in P-deficient nodules, respectively.