Metabolite profile analysis: from raw data to regression and classification

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Transcriptomic and Metabolomic Analysis of a Pseudomonas-Resistant versus a Susceptible Arabidopsis Accession

Accessions of one plant species may show significantly different levels of susceptibility to stresses. The Arabidopsis thaliana accessions Col-0 and C24 differ significantly in their resistance to the pathogen Pseudomonas syringae pv. tomato (Pst). To help unravel the underlying mechanisms contributing to this naturally occurring variance in resistance to Pst, we analyzed changes in transcripts and compounds from primary and secondary metabolism of Col-0 and C24 at different time points after infection with Pst. Our results show that the differences in the resistance of Col-0 and C24 mainly involve mechanisms of salicylic-acid-dependent systemic acquired resistance, while responses of jasmonic-acid-dependent mechanisms are shared between the two accessions. In addition, arginine metabolism and differential activity of the biosynthesis pathways of aliphatic glucosinolates and indole glucosinolates may also contribute to the resistance. Thus, this study highlights the difference in the defense response strategies utilized by different genotypes.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Clinical Application of Metabolomics in Pancreatic Diseases: A Mini-Review

Metabolomics is a powerful new analytical method to describe the set of metabolites within cellular tissue and bodily fluids. Metabolomics can uncover detailed information about metabolic changes in organisms. The morphology of these metabolites represents the metabolic processes that occur in cells, such as anabolism, catabolism, inhomogeneous natural absorption and metabolism, detoxification, and metabolism of biomass energy. Because the metabolites of different diseases are different, the specificity of the changes can be found by metabolomics testing, which provides a new source of biomarkers for the early identification of diseases and the difference between benign and malignant states. Metabolomics has a wide application potential in pancreatic diseases, including early detection, diagnosis, and identification of pancreatic diseases. However, there are few studies on metabolomics in pancreatic diseases in the literature. This article reviews the application of metabolomics in the diagnosis, prognosis, treatment, and evaluation of pancreatic diseases.

Comparative metabolite profiling of two switchgrass ecotypes reveals differences in drought stress responses and rhizosheath weight

Rhizosheath comprises soil that adheres firmly to roots. In this study, two ecotypes of switchgrass with different rhizosheath sizes after drought stress were analyzed which showed metabolic differences under drought conditions. The rhizosheath comprises soil that adheres firmly to roots by a combination of root hairs and mucilage and may aid in root growth under soil drying. The aim of this work is to reveal the potential metabolites involved in rhizosheath formation under drought stress conditions. Panicum virgatum L. (switchgrass), which belongs to the Poaceae family, is an important biofuel and fodder crop in drought areas. Five switchgrass ecotypes (cv. Alamo, cv. Blackwake, cv. Summer, cv. Cave-in-Rock and cv. Kanlow) have a broad range of rhizosheath weight under drought conditions. For two selected ecotypes with contrast rhizosheath weight (cv. Alamo and cv. Kanlow), root hair length and density, lateral root number, root morphological parameters were measured, and real-time qRT-PCR was performed. Gas chromatography mass spectrophotometry (GC-MS) was used to determine the primary metabolites in the shoots and roots of selected ecotypes under drought stress conditions. The change trends of root hair length and density, lateral root number and related gene expression were consistent with rhizosheath weight in Alamo and Kanlow under drought and watered conditions. For root morphological parameters, Alamo grew deeper than Kanlow, while Kanlow exhibited higher values for other parameters. In this study, the levels of amino acids, sugars and organic acids were significantly changed in response to drought stress in two switchgrass ecotypes. Several metabolites including amino acids (arginine, isoleucine, methionine and cysteine) and sugars (kestose, raffinose, fructose, fucose, sorbose and xylose) in the large soil-sheathed roots of Alamo and Kanlow were significantly increased compared to small or no soil-sheathed roots of Alamo and Kanlow. Difference in rhizosheath size is reflected in the plant internal metabolites under drought stress conditions. Additionally, our results highlight the importance of using metabolite profiling and provide a better understanding of rhizosheath formation at the cellular level.

Sulphur systems biology-making sense of omics data

Systems biology approaches have been applied over the last two decades to study plant sulphur metabolism. These 'sulphur-omics' approaches have been developed in parallel with the advancing field of systems biology, which is characterized by permanent improvements of high-throughput methods to obtain system-wide data. The aim is to obtain a holistic view of sulphur metabolism and to generate models that allow predictions of metabolic and physiological responses. Besides known sulphur-responsive genes derived from previous studies, numerous genes have been identified in transcriptomics studies. This has not only increased our knowledge of sulphur metabolism but has also revealed links between metabolic processes, thus indicating a previously unexpected complex interconnectivity. The identification of response and control networks has been supported through metabolomics and proteomics studies. Due to the complex interlacing nature of biological processes, experimental validation using targeted or systems approaches is ongoing. There is still room for improvement in integrating the findings from studies of metabolomes, proteomes, and metabolic fluxes into a single unifying concept and to generate consistent models. We therefore suggest a joint effort of the sulphur research community to standardize data acquisition. Furthermore, focusing on a few different model plant systems would help overcome the problem of fragmented data, and would allow us to provide a standard data set against which future experiments can be designed and compared.

Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars

Polianthes tuberosa L. (tuberose) is a widely cultivated ornamental crop in Asian countries. Different cultivars of tuberose have been developed through breeding programs in India. However, no reports on floral fragrance and metabolite contents of these cultivars are available. In this study, an attempt has been made to evaluate the levels of both volatile and non-volatile metabolites from seven different cultivars of P. tuberosa. Presence of benzenoids, phenylpropanoids, terpenoids, and few fatty acid derivatives as emitted, endogenous and glycosylated forms were revealed from the studied cultivars. Further, chemometric analyses in both supervised and unsupervised manner led to identification of patterns among the cultivars. Among the seven cultivars, four distinct clusters were obtained linking to their volatiles, flavonoids and primary metabolite levels. Metabolic variations obtained from the cultivars also suggest cross-talks between phenylpropanoid, benzenoid, and flavonoid pathways. Thus metabolite profiling reported here may help in characterization of tuberose cultivars for perfumery utility and future breeding programme.

[High-performance liquid chromatography-mass spectrometry-based serum metabolic profiling in patients with HBV-related hepatocellular carcinoma]

OBJECTIVE

To explore the diagnostic value of the serum metabolites identified by high-performance liquid chromatography-mass spectrometry (HPLC/MS) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

METHODS

A total of 126 patients admitted to Tianjin Third Central Hospital were enrolled, including 27 patients with HBV-related hepatitis with negative viral DNA (DNA-N), 24 with HBV-related hepatitis with positive viral DNA, 24 with HBV-related liver cirrhosis, 27 with HBV-related HCC undergoing surgeries or radiofrequency ablation, and 24 with HBV-related HCC receiving interventional therapy, with 25 healthy volunteers as the normal control group. Serum samples were collected from all the subjects for HPLC/MS analysis, and the data were pretreated to establish an orthogonal partial least- squares discriminant analysis (OPLS-DA) model. The differential serum metabolites were preliminarily screened by comparisons between the HBV groups and the control group, and the characteristic metabolites were identified according to the results of non-parametric test. The potential clinical values of these characteristic metabolites were evaluated using receiver operator characteristic curve (ROC) analysis.

RESULTS

A total of 25 characteristic metabolites were identified in the HBV- infected patients, including 9 lysophosphatidylcholines, 2 fatty acids, 17α-estradiol, sphinganine, 5-methylcytidine, vitamin K2, lysophosphatidic acid, glycocholic acid and 8 metabolites with few reports. The patients with HBV- related HCC showed 22 differential serum metabolites compared with the control group, 4 differential metabolites compared with patients with HBV-related liver cirrhosis; 10 differential metabolites were identified in patients with HBV-related HCC receiving interventional therapy compared with those receiving surgical resection or radiofrequency ablation. From the normal control group to HBV-related HCC treated by interventional therapy, many metabolites underwent variations following a similar pattern.

CONCLUSIONS

We identified 25 characteristic metabolites in patients with HBV-related HCC, and these metabolites may have potential clinical values in the diagnosis of HBV-related HCC. The continuous change of some of these metabolites may indicate the possibility of tumorigenesis, and some may also have indications for the choice of surgical approach.

A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis

Background:

Metabolomics data generation and quantification are different from other types of molecular “omics” data in bioinformatics. Mass spectrometry (MS) based (gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), etc.) metabolomics data frequently contain missing values that make some quantitative analysis complex. Typically metabolomics datasets contain 10% to 20% missing values that originate from several reasons, like analytical, computational as well as biological hazard. Imputation of missing values is a very important and interesting issue for further metabolomics data analysis. </P><P> Objective: This paper introduces a new algorithm for missing value imputation in the presence of outliers for metabolomics data analysis. </P><P> Method: Currently, the most well known missing value imputation techniques in metabolomics data are knearest neighbours (kNN), random forest (RF) and zero imputation. However, these techniques are sensitive to outliers. In this paper, we have proposed an outlier robust missing imputation technique by minimizing twoway empirical mean absolute error (MAE) loss function for imputing missing values in metabolomics data.

Results:

We have investigated the performance of the proposed missing value imputation technique in a comparison of the other traditional imputation techniques using both simulated and real data analysis in the absence and presence of outliers.

Conclusion:

Results of both simulated and real data analyses show that the proposed outlier robust missing imputation technique is better performer than the traditional missing imputation methods in both absence and presence of outliers.

The serum metabolic profiles of different Barcelona stages hepatocellular carcinoma associated with hepatitis B virus

The present study aimed to explore the characteristic ions distinguishing different Barcelona stages in patients with hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) using the ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) platform, and to evaluate their value in diagnosing and monitoring the progress of HCC. The serum was sampled from 20 healthy volunteers, 20 patients with HBV-induced cirrhosis and 75 patients with HBV-associated HCC of different BCLC stages. Samples were all examined using UPLC-MS. Principal components analysis (PCA) and the orthogonal partial least squares discriminant analysis (OPLS-DA) model were constructed to determine potential biomarkers. Then, the independent sample-nonparametric test was used to perform the final screening for ion identification. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of these ions. Serum metabolomic PCA and OPLS-DA models were established to diagnose different BCLC stages of HCC associated with HBV, with OPLS-DA model parameters (R2X=67.2%, R2Y=82%, Q2Y=61.1%). A total of 20 metabolites with statistically significant differences among groups were identified, primarily including amino acids, bile acid, fatty acid and phosphatidate. The area under the curve (AUC) of LysoPC [18:2 (9Z,12Z)], LysoPC (P-16:0), asparaginyl-proline and vaccenic acid in the comparison between HCC and cirrhosis were all increased compared with that of AFP, indicating a more improved diagnosis ability. Furthermore, the AUC of L-aspartyl-4-phosphate and LysoPC [20:5 (5Z,8Z,11Z,14Z,17Z)] in the stage A vs. B comparison were increased compared with that of AFP, but were decreased in the comparison between stage B and C. The present study succeeded in screening metabolic ions that reflect the progress of HCC with high diagnostic value. Thus, the identified ions may serve a role in clinically diagnosing HBV-associated HCC and monitoring the development of the disease.

Statistical and Multivariate Analysis of MS-Based Plant Metabolomics Data

Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.

Identification of a Potential ISR Determinant from Pseudomonas aeruginosa PM12 against Fusarium Wilt in Tomato

Biocontrol of plant diseases through induction of systemic resistance is an environmental friendly substitute to chemicals in crop protection measures. Different biotic and abiotic elicitors can trigger the plant for induced resistance. Present study was designed to explore the potential of Pseudomonas aeruginosa PM12 in inducing systemic resistance in tomato against Fusarium wilt. Initially the bioactive compound, responsible for ISR, was separated and identified from extracellular filtrate of P. aeruginosa PM12. After that purification and characterization of the bacterial crude extracts was carried out through a series of organic solvents. The fractions exhibiting ISR activity were further divided into sub-fractions through column chromatography. Sub fraction showing maximum ISR activity was subjected to Gas chromatography/mass spectrometry for the identification of compounds. Analytical result showed three compounds in the ISR active sub-fraction viz: 3-hydroxy-5-methoxy benzene methanol (HMB), eugenol and tyrosine. Subsequent bioassays proved that HMB is the potential ISR determinant that significantly ameliorated Fusarium wilt of tomato when applied as soil drench method at the rate of 10 mM. In the next step of this study, GC-MS analysis was performed to detect changes induced in primary and secondary metabolites of tomato plants by the ISR determinant. Plants were treated with HMB and Fusarium oxysporum in different combinations showing intensive re- modulations in defense related pathways. This work concludes that HMB is the potential elicitor involved in dynamic reprogramming of plant pathways which functionally contributes in defense responses. Furthermore the use of biocontrol agents as natural enemies of soil borne pathogens besides enhancing production potential of crop can provide a complementary tactic for sustainable integrated pest management.

Serum Metabonomics of Mild Acute Pancreatitis

BACKGROUND

Mild acute pancreatitis (MAP) is a common acute abdominal disease, and exhibits rising incidence in recent decades. As an important component of systemic biology, metabonomics is a new discipline developed following genomics and proteomics. In this study, the objective was to analyze the serum metabonomics of patients with MAP, aiming to screen metabolic markers with potential diagnostic values.

METHODS

An analysis platform with ultra performance liquid chromatography-high-resolution mass spectrometry was used to screen the difference metabolites related to MAP diagnosis and disease course monitoring.

RESULTS

A total of 432 endogenous metabolites were screened out from 122 serum samples, and 49 difference metabolites were verified, among which 12 difference metabolites were identified by nonparametric test. After material identification, eight metabolites exhibited reliable results, and their levels in MAP serum were higher than those in healthy serum. Four metabolites exhibited gradual downward trend with treatment process going on, and the differences were statistically significant (P < 0.05).

CONCLUSION

Metabonomic analysis has revealed eight metabolites with potential diagnostic values toward MAP, among which four metabolites can be used to monitor the disease course.

Phenylacetic Acid Is ISR Determinant Produced by Bacillus fortis IAGS162, Which Involves Extensive Re-modulation in Metabolomics of Tomato to Protect against Fusarium Wilt

Bacillus fortis IAGS162 has been previously shown to induce systemic resistance in tomato plants against Fusarium wilt disease. In the first phase of current study, the ISR determinant was isolated from extracellular metabolites of this bacterium. ISR bioassays combined with solvent extraction, column chromatography and GC/MS analysis proved that phenylacetic acid (PAA) was the potential ISR determinant that significantly ameliorated Fusarium wilt disease of tomato at concentrations of 0.1 and 1 mM. In the second phase, the biochemical basis of the induced systemic resistance (ISR) under influence of PAA was elucidated by performing non-targeted whole metabolomics through GC/MS analysis. Tomato plants were treated with PAA and fungal pathogen in various combinations. Exposure to PAA and subsequent pathogen challenge extensively re-modulated tomato metabolic networks along with defense related pathways. In addition, various phenylpropanoid precursors were significantly up-regulated in treatments receiving PAA. This work suggests that ISR elicitor released from B. fortis IAGS162 contributes to resistance against fungal pathogens through dynamic reprogramming of plant pathways that are functionally correlated with defense responses.

Unraveling heterogeneous susceptibility and the evolution of breast cancer using a systems biology approach

Background

An essential question in cancer is why individuals with the same disease have different clinical outcomes. Progress toward a more personalized medicine in cancer patients requires taking into account the underlying heterogeneity at different molecular levels.

Results

Here, we present a model in which there are complex interactions at different cellular and systemic levels that account for the heterogeneity of susceptibility to and evolution of ERBB2-positive breast cancers. Our model is based on our analyses of a cohort of mice that are characterized by heterogeneous susceptibility to ERBB2-positive breast cancers. Our analysis reveals that there are similarities between ERBB2 tumors in humans and those of backcross mice at clinical, genomic, expression, and signaling levels. We also show that mice that have tumors with intrinsically high levels of active AKT and ERK are more resistant to tumor metastasis. Our findings suggest for the first time that a site-specific phosphorylation at the serine 473 residue of AKT1 modifies the capacity for tumors to disseminate. Finally, we present two predictive models that can explain the heterogeneous behavior of the disease in the mouse population when we consider simultaneously certain genetic markers, liver cell signaling and serum biomarkers that are identified before the onset of the disease.

Conclusions

Considering simultaneously tumor pathophenotypes and several molecular levels, we show the heterogeneous behavior of ERBB2-positive breast cancer in terms of disease progression. This and similar studies should help to better understand disease variability in patient populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0599-z) contains supplementary material, which is available to authorized users.

Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

While in nucleotide sequencing, the analysis of DNA from complex mixtures of organisms is common, this is not yet true for mass spectrometric data analysis of complex mixtures. The comparative analyses of mass spectrometry data of microbial communities at the molecular level is difficult to perform, especially in the context of a host. The challenge does not lie in generating the mass spectrometry data, rather much of the difficulty falls in the realm of how to derive relevant information from this data. The informatics based techniques to visualize and organize datasets are well established for metagenome sequencing; however, due to the scarcity of informatics strategies in mass spectrometry, it is currently difficult to cross correlate two very different mass spectrometry data sets from microbial communities and their hosts. We highlight that molecular networking can be used as an organizational tool of tandem mass spectrometry data, automated database search for rapid identification of metabolites, and as a workflow to manage and compare mass spectrometry data from complex mixtures of organisms. To demonstrate this platform, we show data analysis from hard corals and a human lung associated with cystic fibrosis.

Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

Detailed and standardized protocols for plant cultivation in environmentally controlled conditions are an essential prerequisite to conduct reproducible experiments with precisely defined treatments. Setting up appropriate and well defined experimental procedures is thus crucial for the generation of solid evidence and indispensable for successful plant research. Non-invasive and high throughput (HT) phenotyping technologies offer the opportunity to monitor and quantify performance dynamics of several hundreds of plants at a time. Compared to small scale plant cultivations, HT systems have much higher demands, from a conceptual and a logistic point of view, on experimental design, as well as the actual plant cultivation conditions, and the image analysis and statistical methods for data evaluation. Furthermore, cultivation conditions need to be designed that elicit plant performance characteristics corresponding to those under natural conditions. This manuscript describes critical steps in the optimization of procedures for HT plant phenotyping systems. Starting with the model plant Arabidopsis, HT-compatible methods were tested, and optimized with regard to growth substrate, soil coverage, watering regime, experimental design (considering environmental inhomogeneities) in automated plant cultivation and imaging systems. As revealed by metabolite profiling, plant movement did not affect the plants' physiological status. Based on these results, procedures for maize HT cultivation and monitoring were established. Variation of maize vegetative growth in the HT phenotyping system did match well with that observed in the field. The presented results outline important issues to be considered in the design of HT phenotyping experiments for model and crop plants. It thereby provides guidelines for the setup of HT experimental procedures, which are required for the generation of reliable and reproducible data of phenotypic variation for a broad range of applications.

Opposite variations in fumarate and malate dominate metabolic phenotypes of Arabidopsis salicylate mutants with abnormal biomass under chilling

In chilling conditions (5°C), salicylic acid (SA)-deficient mutants (sid2, eds5 and NahG) of Arabidopsis thaliana produced more biomass than wild type (Col-0), whereas the SA overproducer cpr1 was extremely stunted. The hypothesis that these phenotypes were reflected in metabolism was explored using 600 MHz (1) H nuclear magnetic resonance (NMR) analysis of unfractionated polar shoot extracts. Biomass-related metabolic phenotypes were identified as multivariate data models of these NMR 'fingerprints'. These included principal components that correlated with biomass. Also, partial least squares-regression models were found to predict the relative size of plants in previously unseen experiments in different light intensities, or relative size of one genotype from the others. The dominant signal in these models was fumarate, which was high in SA-deficient mutants, intermediate in Col-0 and low in cpr1 at 5°C. Among signals negatively correlated with biomass, malate was prominent. Abundance of transcripts of the FUM2 cytosolic fumarase (At5g50950) showed strong positive correlation with fumarate levels and with biomass, whereas no significant differences were found for the FUM1 mitochondrial fumarase (At2g47510). It was confirmed that the morphological effects of SA under chilling find expression in the metabolome, with a role of fumarate highlighted.

Metabolic differences in ripening of Solanum lycopersicum 'Ailsa Craig' and three monogenic mutants

Application of mass spectrometry enables the detection of metabolic differences between groups of related organisms. Differences in the metabolic fingerprints of wild-type Solanum lycopersicum and three monogenic mutants, ripening inhibitor (rin), non-ripening (nor) and Colourless non-ripening (Cnr), of tomato are captured with regard to ripening behaviour. A high-resolution tandem mass spectrometry system coupled to liquid chromatography produced a time series of the ripening behaviour at discrete intervals with a focus on changes post-anthesis. Internal standards and quality controls were used to ensure system stability. The raw data of the samples and reference compounds including study protocols have been deposited in the open metabolomics database MetaboLights via the metadata annotation tool Isatab to enable efficient re-use of the datasets, such as in metabolomics cross-study comparisons or data fusion exercises.

Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages

BACKGROUND

High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology.

RESULTS

We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input-output model of the phosphoprotein-cytokine network.

CONCLUSIONS

We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process.

Influence of missing values substitutes on multivariate analysis of metabolomics data

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%–20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods.

Review: toxicometabolomics

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

Milk metabolites and their genetic variability

The composition of milk is crucial to evaluate milk performance and quality measures. Milk components partly contribute to breeding scores, and they can be assessed to judge metabolic and energy status of the cow as well as to serve as predictive markers for diseases. In addition to the milk composition measures (e.g., fat, protein, lactose) traditionally recorded during milk performance test via infrared spectroscopy, novel techniques, such as gas chromatography-mass spectrometry, allow for a further analysis of milk into its metabolic components. Gas chromatography-mass spectrometry is suitable for measuring several hundred metabolites with high throughput, and thus it is applicable to study sources of genetic and nongenetic variation of milk metabolites in dairy cows. Heritability and mode of inheritance of metabolite measurements were studied in a linear mixed model approach including expected (pedigree) and realized (genomic) relationship between animals. The genetic variability of 190 milk metabolite intensities was analyzed from 1,295 cows held on 18 farms in Mecklenburg-Western Pomerania, Germany. Besides extensive pedigree information, genotypic data comprising 37,180 single nucleotide polymorphism markers were available. Goodness of fit and significance of genetic variance components based on likelihood ratio tests were investigated with a full model, including marker- and pedigree-based genetic effects. Broad-sense heritability varied from zero to 0.699, with a median of 0.125. Significant additive genetic variance was observed for highly heritable metabolites, but dominance variance was not significantly present. As some metabolites are particularly favorable for human nutrition, for instance, future research should address the identification of locus-specific genetic effects and investigate metabolites as the molecular basis of traditional milk performance test traits.

ADEMA: an algorithm to determine expected metabolite level alterations using mutual information

Metabolomics is a relatively new “omics” platform, which analyzes a discrete set of metabolites detected in bio-fluids or tissue samples of organisms. It has been used in a diverse array of studies to detect biomarkers and to determine activity rates for pathways based on changes due to disease or drugs. Recent improvements in analytical methodology and large sample throughput allow for creation of large datasets of metabolites that reflect changes in metabolic dynamics due to disease or a perturbation in the metabolic network. However, current methods of comprehensive analyses of large metabolic datasets (metabolomics) are limited, unlike other “omics” approaches where complex techniques for analyzing coexpression/coregulation of multiple variables are applied. This paper discusses the shortcomings of current metabolomics data analysis techniques, and proposes a new multivariate technique (ADEMA) based on mutual information to identify expected metabolite level changes with respect to a specific condition. We show that ADEMA better predicts De Novo Lipogenesis pathway metabolite level changes in samples with Cystic Fibrosis (CF) than prediction based on the significance of individual metabolite level changes. We also applied ADEMA's classification scheme on three different cohorts of CF and wildtype mice. ADEMA was able to predict whether an unknown mouse has a CF or a wildtype genotype with 1.0, 0.84, and 0.9 accuracy for each respective dataset. ADEMA results had up to 31% higher accuracy as compared to other classification algorithms. In conclusion, ADEMA advances the state-of-the-art in metabolomics analysis, by providing accurate and interpretable classification results.

Metabolomics is an experimental approach that analyzes differences in metabolite levels detected in experimental samples. It has been used in the literature to understand the changes in metabolism with respect to diseases or drugs. Unlike transcriptomics or proteomics, which analyze gene and protein expression levels respectively, the techniques that consider co-regulation of multiple metabolites are quite limited. In this paper, we propose a novel technique, called ADEMA, which computes the expected level changes for each metabolite with respect to a given condition. ADEMA considers multiple metabolites at the same time and is mutual information (MI)-based. We show that ADEMA predicts metabolite level changes for young mice with Cystic Fibrosis (CF) better than significance testing that considers one metabolite at a time. Using three different datasets that contain CF and wild-type (WT) mice, we show that ADEMA can classify an individual as being CF or WT based on the metabolic profiles (with 1.0, 0.84, and 0.9 accuracy, respectively). Compared to other well-known classification algorithms, ADEMA's accuracy is higher by up to 31%.

The form of nitrogen nutrition affects resistance against Pseudomonas syringae pv. phaseolicola in tobacco

Different forms of nitrogen (N) fertilizer affect disease development; however, this study investigated the effects of N forms on the hypersensitivity response (HR)-a pathogen-elicited cell death linked to resistance. HR-eliciting Pseudomonas syringae pv. phaseolicola was infiltrated into leaves of tobacco fed with either NO₃⁻ or NH₄⁺. The speed of cell death was faster in NO₃⁻-fed compared with NH₄⁺-fed plants, which correlated, respectively, with increased and decreased resistance. Nitric oxide (NO) can be generated by nitrate reductase (NR) to influence the formation of the HR. NO generation was reduced in NH₄⁺-fed plants where N assimilation bypassed the NR step. This was similar to that elicited by the disease-forming P. syringae pv. tabaci strain, further suggesting that resistance was compromised with NH₄⁺ feeding. PR1a is a biomarker for the defence signal salicylic acid (SA), and expression was reduced in NH₄⁺-fed compared with NO₃⁻ fed plants at 24h after inoculation. This pattern correlated with actual SA measurements. Conversely, total amino acid, cytosolic and apoplastic glucose/fructose and sucrose were elevated in - treated plants. Gas chromatography/mass spectroscopy was used to characterize metabolic events following different N treatments. Following NO₃⁻ nutrition, polyamine biosynthesis was predominant, whilst after NH₄⁺ nutrition, flux appeared to be shifted towards the production of 4-aminobutyric acid. The mechanisms whereby feeding enhances SA, NO, and polyamine-mediated HR-linked defence whilst these are compromised with NH₄⁺, which also increases the availability of nutrients to pathogens, are discussed.

Principal components analysis

Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored. The visualization and statistical analysis of these new variables, the principal components, can help to find similarities and differences between samples. Important original variables that are the major contributors to the first few components can be discovered as well.This chapter seeks to deliver a conceptual understanding of PCA as well as a mathematical description. We describe how PCA can be used to analyze different datasets, and we include practical code examples. Possible shortcomings of the methodology and ways to overcome these problems are also discussed.

Low-dose ionizing radiation-induced blood plasma metabolic response in a diverse genetic mouse population

Understanding the biological effects and biochemical mechanisms of low-dose ionizing radiation (LDIR) is important for setting exposure limits for the safe use of nuclear power and medical diagnostic procedures. Although several studies have highlighted the effects of ionizing radiation on metabolism, most studies have focused on uniform genetic mouse populations. Here, we report the metabolic response to LDIR (10 cGy X ray) on a genetically diverse mouse population (142 mice) generated from a cross of radiation-sensitive (BALB/c) and radiation-resistant (SPRET/EiJ) parental strains. GC-TOF profiling of plasma metabolites was used to compare exposed vs. sham animals. From this, 16 metabolites were significantly altered in the LDIR treated vs. sham group. Use of two significantly altered metabolites, thymine and 2-monostearin, was found to effectively segregate the two treatments. Multivariate statistical analysis was used to identify genetic polymorphisms correlated with metabolite abundance (e.g., amino acids, fatty acids, nucleotides and TCA cycle intermediates). Genetic analysis of metabolic phenotypes showed suggestive linkages for fatty acid and amino acid metabolism. However, metabolite abundance was found to be a function of low-dose ionizing radiation exposure, and not of the underlying genetic variation.

A tyrosine aminotransferase involved in tocopherol synthesis in Arabidopsis

The metabolic function of the predicted Arabidopsis tyrosine aminotransferase (TAT) encoded by the At5g53970 gene was studied using two independent knock-out mutants. Gas chromatography-mass spectrometry based metabolic profiling revealed a specific increase in tyrosine levels, supporting the proposed function of At5g53970 as a tyrosine-specific aminotransferase not involved in tyrosine biosynthesis, but rather in utilization of tyrosine for other metabolic pathways. The TAT activity of the At5g53970-encoded protein was verified by complementation of the Escherichia coli tyrosine auxotrophic mutant DL39, and in vitro activity of recombinantly expressed and purified At5g53970 was found to be specific for tyrosine. To investigate the physiological role of At5g53970, the consequences of reduction in tyrosine utilization on metabolic pathways having tyrosine as a substrate were analysed. We found that tocopherols were substantially reduced in the mutants and we conclude that At5g53970 encodes a TAT important for the synthesis of tocopherols in Arabidopsis.

[Application of liquid chromatography-mass spectrometry in the study of metabolic profiling of cirrhosis in different grades]

The metabolic profiles were obtained by high performance liquid chromatography combined with a LTQ Orbitrap XL mass spectrometer (HPLC-LTQ Orbitrap XL MS) platform to analyze serum specimens from the people of healthy control group and patients of hepatitis B virus (HBV)-induced cirrhosis. Then the data were analyzed with the pattern recognition methods and nonparametric test. The orthogonal partial least squares-discriminant analysis (OPLS-DA) mode (R2(Y) = 90.1%, Q2 = 66.7%) was constructed by the serum metabolic profiles of Child-Pugh grades A, B, C groups and a healthy control group in the training set and the good discrimination ability of this mode for the testing set was demonstrated with the accuracy of 93.8%. The corresponding specific metabolic biomarkers used to distinguish different cirrhosis grades were discovered, such as lysophosphatidylcholine (LPC), glycolchenodeoxycholic acid (GCDCA), cysteine, glycine, aminoadipic acid, pipecolic acid. The results suggest that the metabolic profiling of serum can be used to construct the discrimination mode and discover the metabolic biomarkers for the HBV-induced cirrhosis, which will support the diagnosis and evaluation of the HBV-induced cirrhosis.

Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques

PLS regression models were developed using mixtures of cellulose, xylan, and lignin in a ternary mixture experimental design for multivariate model calibration. Mid-infrared spectra of these representative samples were recorded using Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FT-IR) spectroscopy and regressed against their known composition using Partial Least Squares (PLSs) multivariate techniques. The regression models were cross-validated and then used to predict the unknown compositions of two Arabidopsis cultivars, B10 and C10. The effect of various data preprocessing techniques on the final predictive ability of the PLS regression models was also evaluated. The predicted compositions of B10 and C10 by the PLS regression model after second derivative data preprocessing were similar to the results provided by a third-party analysis. This study suggests that mixture designs could be used as calibration standards in PLS regression for the compositional analysis of lignocellulosic materials if the infrared data is appropriately preprocessed.

Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress

Adaptation to abiotic stresses like drought is an important acquirement of agriculturally relevant crops like maize. Development of enhanced drought tolerance in crops grown in climatic zones where drought is a very dominant stress factor therefore plays an essential role in plant breeding. Previous studies demonstrated that corn yield potential and enhanced stress tolerance are associated traits. In this study, we analyzed six different maize hybrids for their ability to deal with drought stress in a greenhouse experiment. We were able to combine data from morphophysiological parameters measured under well-watered conditions and under water restriction with metabolic data from different organs. These different organs possessed distinct metabolite compositions, with the leaf blade displaying the most considerable metabolome changes following water deficiency. Whilst we could show a general increase in metabolite levels under drought stress, including changes in amino acids, sugars, sugar alcohols, and intermediates of the TCA cycle, these changes were not differential between maize hybrids that had previously been designated based on field trial data as either drought-tolerant or susceptible. The fact that data described here resulted from a greenhouse experiment with rather different growth conditions compared to natural ones in the field may explain why tolerance groups could not be confirmed in this study. We were, however, able to highlight several metabolites that displayed conserved responses to drought as well as metabolites whose levels correlated well with certain physiological traits.

Inhibition of aconitase by nitric oxide leads to induction of the alternative oxidase and to a shift of metabolism towards biosynthesis of amino acids

Nitric oxide (NO) is a free radical molecule involved in signalling and in hypoxic metabolism. This work used the nitrate reductase double mutant of Arabidopsis thaliana (nia) and studied metabolic profiles, aconitase activity, and alternative oxidase (AOX) capacity and expression under normoxia and hypoxia (1% oxygen) in wild-type and nia plants. The roots of nia plants accumulated very little NO as compared to wild-type plants which exhibited ∼20-fold increase in NO emission under low oxygen conditions. These data suggest that nitrate reductase is involved in NO production either directly or by supplying nitrite to other sites of NO production (e.g. mitochondria). Various studies revealed that NO can induce AOX in mitochondria, but the mechanism has not been established yet. This study demonstrates that the NO produced in roots of wild-type plants inhibits aconitase which in turn leads to a marked increase in citrate levels. The accumulating citrate enhances AOX capacity, expression, and protein abundance. In contrast to wild-type plants, the nia double mutant failed to show AOX induction. The overall induction of AOX in wild-type roots correlated with accumulation of glycine, serine, leucine, lysine, and other amino acids. The findings show that NO inhibits aconitase under hypoxia which results in accumulation of citrate, the latter in turn inducing AOX and causing a shift of metabolism towards amino acid biosynthesis.

Transcript and metabolite analysis of the Trichoderma-induced systemic resistance response to Pseudomonas syringae in Arabidopsis thaliana

In the present study we have assessed, by transcriptional and metabolic profiling, the systemic defence response of Arabidopsis thaliana plants to the leaf pathogen Pseudomonas syringae pv. tomato DC3000 (Pst) induced by the beneficial fungus Trichoderma asperelloides T203. Expression analysis (qPCR) of a set of 137 Arabidopsis genes related to Pst defence responses showed that T203 root colonization is not associated with major detectable transcriptomic changes in leaves. However, plants challenged with the bacterial pathogen showed quantitative differences in gene expression when pre-inoculated with T203, supporting priming of the plant by this beneficial fungus. Among the defence-related genes affected by T203, lipid transfer protein (LTP)4, which encodes a member of the lipid transfer pathogenesis-related family, is upregulated, whereas the WRKY40 transcription factor, known to contribute to Arabidopsis susceptibility to bacterial infection, shows reduced expression. On the other hand, root colonization by this beneficial fungus substantially alters the plant metabolic profile, including significant changes in amino acids, polyamines, sugars and citric acid cycle intermediates. This may in part reflect an increased energy supply required for the activation of plant defences and growth promotion effects mediated by Trichoderma species.

Identification of enzymatic and regulatory genes of plant metabolism through QTL analysis in Arabidopsis

The biochemical diversity in the plant kingdom is estimated to well exceed 100,000 distinct compounds (Weckwerth, 2003) and 4000 to 20,000 metabolites per species seem likely (Fernie et al., 2004). In recent years extensive progress has been made towards the identification of enzymes and regulatory genes working in a complex network to generate this large arsenal of metabolites. Genetic loci influencing quantitative traits, e.g. metabolites or biomass, may be mapped to associated molecular markers, a method called quantitative trait locus mapping (QTL mapping), which may facilitate the identification of novel genes in biochemical pathways. Arabidopsis thaliana, as a model organism for seed plants, is a suitable target for metabolic QTL (mQTL) studies due to the availability of highly developed molecular and genetic tools, and the extensive knowledge accumulated on the metabolite profile. While intensely studied, in particular since the availability of its complete sequence, the genome of Arabidopsis still comprises a large proportion of genes with only tentative function based on sequence homology. From a total number of 33,518 genes currently listed (TAIR 9, http://www.arabidopsis.org), only about 25% have direct experimental evidence for their molecular function and biological process, while for more than 30% no biological data are available. Modern metabolomics approaches together with continually extended genomic resources will facilitate the task of assigning functions to those genes. In our previous study we reported on the identification of mQTL (Lisec et al., 2008). In this paper, we summarize the current status of mQTL analyses and causal gene identification in Arabidopsis and present evidence that a candidate gene located within the confidence interval of a fumarate mQTL (AT5G50950) encoding a putative fumarase is likely to be the causal gene of this QTL. The total number of genes molecularly identified based on mQTL studies is still limited, but the advent of multi-parallel analysis techniques for measurement of gene expression, as well as protein and metabolite abundances and for rapid gene identification will assist in the important task of assigning enzymes and regulatory genes to the growing network of known metabolic reactions.

Metabolomics: creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.

MetaboAnalyst: a web server for metabolomic data analysis and interpretation

Metabolomics is a newly emerging field of 'omics' research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca.

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.

MeltDB: a software platform for the analysis and integration of metabolomics experiment data

MOTIVATION

The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context.

RESULTS

To facilitate the systematic storage, analysis and integration of metabolomics experiments, we have implemented MeltDB, a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. MeltDB supports open file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The system covers the process from raw data to the visualization of results in a knowledge-based background and is integrated into the context of existing software platforms of genomics and transcriptomics at Bielefeld University. We demonstrate the potential of MeltDB by means of a sample experiment where we dissect the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris on the level of measured metabolites. Experimental data are stored, analyzed and annotated within MeltDB and accessible via the public MeltDB web server.

AVAILABILITY

The system is publicly available at http://meltdb.cebitec.uni-bielefeld.de.

Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

Background

One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.

Results

We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers.

Conclusion

Our specialized realization of self-organizing maps is well-suitable to gain insight into complex pattern variation in a large set of metabolite profiles. In comparison to other methods our visualization approach facilitates the identification of interesting groups of metabolites by means of a convenient overview on relevant intensity patterns. In particular, the visualization effectively supports researchers in analyzing many putative clusters when the true number of biologically meaningful groups is unknown.