Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism

A Two-dimensional Electrophoresis Proteomic Reference Map and Systematic Identification of 1367 Proteins from a Cell Suspension Culture of the Model Legume Medicago truncatula

The proteome of a Medicago truncatula cell suspension culture was analyzed using two-dimensional electrophoresis and nanoscale HPLC coupled to a tandem Q-TOF mass spectrometer (QSTAR Pulsar i) to yield an extensive protein reference map. Coomassie Brilliant Blue R-250 was used to visualize more than 1661 proteins, which were excised, subjected to in-gel trypsin digestion, and analyzed using nanoscale HPLC/MS/MS. The resulting spectral data were queried against a custom legume protein database using the MASCOT search engine. A total of 1367 of the 1661 proteins were identified with high rigor, yielding an identification success rate of 83% and 907 unique protein accession numbers. Functional annotation of the M. truncatula suspension cell proteins revealed a complete tricarboxylic acid cycle, a nearly complete glycolytic pathway, a significant portion of the ubiquitin pathway with the associated proteolytic and regulatory complexes, and many enzymes involved in secondary metabolism such as flavonoid/isoflavonoid, chalcone, and lignin biosynthesis. Proteins were also identified from most other functional classes including primary metabolism, energy production, disease/defense, protein destination/storage, protein synthesis, transcription, cell growth/division, and signal transduction. This work represents the most extensive proteomic description of M. truncatula suspension cells to date and provides a reference map for future comparative proteomic and functional genomic studies of the response of these cells to biotic and abiotic stress.

Metabolomics: from pattern recognition to biological interpretation

Metabolomics is a technology that aims to identify and quantify the metabolome -- the dynamic set of all small molecules present in an organism or a biological sample. In this sense, the technique is distinct from metabolic profiling, which looks for target compounds and their biochemical transformation. The combination of both approaches is an emerging technique for the characterization of biological samples and for drug treatment. Metabolomics has proven to be very rapid and superior to any other post-genomics technology for pattern-recognition analyses of biological samples. Changing steady state concentrations and fluctuations of metabolites that occur within milliseconds are a result of biochemical processes such as signalling cascades: metabolomic techniques are instrumental in measuring these changes rapidly and sensitively. Metabolite data can be complemented by protein, transcript and external (environmental) data, thereby leading to the identification of multiple physiological biomarkers embedded in correlative molecular networks that are not approachable with targeted studies.

Quantification of saponins in aerial and subterranean tissues of Medicago truncatula

Triterpene saponins from aerial and subterranean organs of Medicago truncatula cv. Jemalong A-17 were qualitatively profiled and quantified using reverse-phase HPLC with on-line photodiode array detection and electrospray-ionization mass spectrometry (HPLC/PDA/ESI/MS). Absolute quantifications were performed for 3-O-beta-D-glucopyranosyl-medicagenic acid and soyasaponin 1 (3-O-[alpha-L-rhamnopyranosyl(1-->2)-beta-D-galactopyranosyl(1-->2)-beta-D-glucuronopyranoside] soyasapogenol B), whereas relative quantifications were determined for 29 other saponins in root, stem, leaf, seedpod, and seed. Roots contained the greatest total amount of saponins followed by leaf and seed, respectively. The quantitative data also reveal the differential accumulation of triterpene saponins in the various organs of M. truncatula. Specifically, relatively higher quantities of medicagenic acid conjugates accumulated in leaf and seed, whereas relatively higher levels of soysapogenol conjugates were observed in root. The differential accumulation of specific triterpene saponins is suggestive of spatially differentiated biosynthesis and/or biological function.

Methyl jasmonate and yeast elicitor induce differential transcriptional and metabolic re-programming in cell suspension cultures of the model legume Medicago truncatula

Exposure of cell suspension cultures of Medicago truncatula Gaerth. to methyl jasmonate (MeJA) resulted in up to 50-fold induction of transcripts encoding the key triterpene biosynthetic enzyme beta-amyrin synthase (betaAS; EC 5.4.99.-). Transcripts reached maximum levels at 24 h post-elicitation with 0.5 mM MeJA. The entry point enzymes into the phenylpropanoid and flavonoid pathways, L: -phenylalanine ammonia-lyase (PAL; EC 4.3.1.5) and chalcone synthase (CHS; EC 2.3.1.74), respectively, were not induced by MeJA. In contrast, exposure of cells to yeast elicitor (YE) resulted in up to 45- and 14-fold induction of PAL and CHS transcripts, respectively, at only 2 h post-elicitation. betaAS transcripts were weakly induced at 12 h after exposure to YE. Over 30 different triterpene saponins were identified in the cultures, many of which were strongly induced by MeJA, but not by YE. In contrast, cinnamic acids, benzoic acids and isoflavone-derived compounds accumulated following exposure of cultures to YE, but few changes in phenylpropanoid levels were observed in response to MeJA. DNA microarray analysis confirmed the strong differential transcriptional re-programming of the cell cultures for multiple genes in the phenylpropanoid and triterpene pathways in response to MeJA and YE, and indicated different responses of individual members of gene families. This work establishes Medicago cell cultures as an excellent model for future genomics approaches to understand the regulation of legume secondary metabolism.

Metabolomics data analysis, visualization, and integration

Metabolomics is the large-scale analysis of metabolites and as such requires bioinformatics tools for data analysis, visualization, and integration. This chapter describes the basic composition of chromatographically coupled mass spectrometry (MS) data sets used in metabolomics and describes in detail the steps necessary for extracting large-scale qualitative and quantitative information. This process involves noise filtering, peak picking and deconvolution, peak identification, peak alignment, and the creation of a final data matrix for statistical processing. Multivariate tools for comparative analysis are presented and illustrated using data for Medicago truncatula. Additional tools for visualizing and integrating metabolomics data within a biological context are discussed. Two tables are provided listing current metabolomics data processing and visualization software. Because metabolomics is rapidly maturing, a final section is presented concerning the need for data standardization and current efforts.