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Background Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks. Results We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R. Conclusions TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data.

Background Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. Results In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine over-accumulation 1 ( mto1 ), and transparent testa4 ( tt4 ) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways. Conclusions We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.

MapMan is a user-driven tool that displays large genomics datasets onto diagrams of metabolic pathways or other processes. Here, we present new developments, including improvements of the gene assignments and the user interface, a strategy to visualize multilayered datasets, the incorporation of statistics packages, and extensions of the software to incorporate more biological information including visualization of coresponding genes and horizontal searches for similar global responses across large numbers of arrays.

To cope with oxidative stress, the metabolic network of plant cells must be reconfigured either to bypass damaged enzymes or to support adaptive responses. To characterize the dynamics of metabolic change during oxidative stress, heterotrophic Arabidopsis (Arabidopsis thaliana) cells were treated with menadione and changes in metabolite abundance and ¹³C-labeling kinetics were quantified in a time series of samples taken over a 6 h period. Oxidative stress had a profound effect on the central metabolic pathways with extensive metabolic inhibition radiating from the tricarboxylic acid cycle and including large sectors of amino acid metabolism. Sequential accumulation of metabolites in specific pathways indicated a subsequent backing up of glycolysis and a diversion of carbon into the oxidative pentose phosphate pathway. Microarray analysis revealed a coordinated transcriptomic response that represents an emergency coping strategy allowing the cell to survive the metabolic hiatus. Rather than attempt to replace inhibited enzymes, transcripts encoding these enzymes are in fact down-regulated while an antioxidant defense response is mounted. In addition, a major switch from anabolic to catabolic metabolism is signaled. Metabolism is also reconfigured to bypass damaged steps (e.g. induction of an external NADH dehydrogenase of the mitochondrial respiratory chain). The overall metabolic response of Arabidopsis cells to oxidative stress is remarkably similar to the superoxide and hydrogen peroxide stimulons of bacteria and yeast (Saccharomyces cerevisiae), suggesting that the stress regulatory and signaling pathways of plants and microbes may share common elements.

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms.

Background Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. Results Here we present rosettR , a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. Conclusions Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.

Background Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious. Results Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings. Conclusions Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at http://metmask.sourceforge.net , that implements our ideas.

Background Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs. Results Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment. Conclusions Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.

Plants can assimilate inorganic nitrogen (N) sources to organic N such as amino acids. N is the most important of the mineral nutrients required by plants and its metabolism is tightly coordinated with carbon (C) metabolism in the fundamental processes that permit plant growth. Increased understanding of N regulation may provide important insights for plant growth and improvement of quality of crops and vegetables because N as well as C metabolism are fundamental components of plant life. Metabolomics is a global biochemical approach useful to study N metabolism because metabolites not only reflect the ultimate phenotypes (traits), but can mediate transcript levels as well as protein levels directly and/or indirectly under different N conditions. This review outlines analytical and bioinformatic techniques particularly used to perform metabolomics for studying N metabolism in higher plants. Examples are used to illustrate the application of metabolomic techniques to the model plants Arabidopsis and rice, as well as other crops and vegetables.

Background The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings. Results Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones. Conclusions Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.