Martin Robert

Differential Metabolomics Reveals Ophthalmic Acid as an Oxidative Stress Biomarker Indicating Hepatic Glutathione Consumption

Metabolomics is an emerging tool that can be used to gain insights into cellular and physiological responses. Here we present a metabolome differential display method based on capillary electrophoresis time-of-flight mass spectrometry to profile liver metabolites following acetaminophen-induced hepatotoxicity. We globally detected 1,859 peaks in mouse liver extracts and highlighted multiple changes in metabolite levels, including an activation of the ophthalmate biosynthesis pathway. We confirmed that ophthalmate was synthesized from 2-aminobutyrate through consecutive reactions with γ-glutamylcysteine and glutathione synthetase. Changes in ophthalmate level in mouse serum and liver extracts were closely correlated and ophthalmate levels increased significantly in conjunction with glutathione consumption. Overall, our results provide a broad picture of hepatic metabolite changes following acetaminophen treatment. In addition, we specifically found that serum ophthalmate is a sensitive indicator of hepatic GSH depletion, and may be a new biomarker for oxidative stress. Our method can thus pinpoint specific metabolite changes and provide insights into the perturbation of metabolic pathways on a large scale and serve as a powerful new tool for discovering low molecular weight biomarkers.

Inactivation of Tumor Suppressor p53 by Mot-2, a hsp70 Family Member

The mortalin genes, mot-1 andmot-2, are hsp70 family members that were originally cloned from normal and immortal murine cells, respectively. Their proteins differ by only two amino acid residues but exhibit different subcellular localizations, arise from two distinct genes, and have contrasting biological activities. We report here that the two proteins also differ in their interactions with the tumor suppressor protein p53. The pancytosolic mot-1 protein in normal cells did not show colocalization with p53; in contrast, nonpancytosolic mot-2 and p53 overlapped significantly in immortal cells. Transfection ofmot-2 but not mot-1 resulted in the repression of p53-mediated transactivation in p53-responsive reporter assays. Inactivation of p53 by mot-2 was supported by the down-regulation of p53-responsive genes p21WAF-1 andmdm-2 in mot-2-transfected cells only. Furthermore, NIH 3T3 cells transfected with expression plasmid encoding green fluorescent protein-taggedmot-2 but not mot-1 showed an abrogation of nuclear translocation of wild-type p53. These results demonstrate a novel mechanism of p53 inactivation by mot-2 protein.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis.

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Metabolite Profiling Reveals YihU as a Novel Hydroxybutyrate Dehydrogenase for Alternative Succinic Semialdehyde Metabolism in Escherichia coli

The search for novel enzymes and enzymatic activities is important to map out all metabolic activities and reveal cellular metabolic processes in a more exhaustive manner. Here we present biochemical and physiological evidence for the function of the uncharacterized protein YihU in Escherichia coli using metabolite profiling by capillary electrophoresis time-of-flight mass spectrometry. To detect enzymatic activity and simultaneously identify possible substrates and products of the putative enzyme, we profiled a complex mixture of metabolites in the presence or absence of YihU. In this manner, succinic semialdehyde was identified as a substrate for YihU. The purified YihU protein catalyzed in vitro the NADH-dependent reduction of succinic semialdehyde to γ-hydroxybutyrate. Moreover, a yihU deletion mutant displayed reduced tolerance to the cytotoxic effects of exogenous addition of succinic semialdehyde. Profiling of intracellular metabolites following treatment of E. coli with succinic semialdehyde supports the existence of a YihU-catalyzed reduction of succinic semialdehyde to γ-hydroxybutyrate in addition to its known oxidation to succinate and through the tricarboxylic acid cycle. These findings suggest that YihU is a novel γ-hydroxybutyrate dehydrogenase involved in the metabolism of succinic semialdehyde, and other potentially toxic intermediates that may accumulate under stress conditions in E. coli.

Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data.

CE‐TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE‐TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time (tm) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured tm (R=0.952 and 0.905 using complete and cross‐validation data sets, respectively). Using the trained model, we subsequently predicted the tm for 2938 metabolites available from the public databases and assigned tentative identities to noise‐filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE‐TOFMS profiling.

Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol

Comparative whole-genome sequencing enables the identification of specific mutations during adaptation of bacteria to new environments and allelic replacement can establish their causality. However, the mechanisms of action are hard to decipher and little has been achieved for epistatic mutations, especially at the metabolic level. Here we show that a strain of Escherichia coli carrying mutations in the rpoC and glpK genes, derived from adaptation in glycerol, uses two distinct metabolic strategies to gain growth advantage. A 27-bp deletion in the rpoC gene first increases metabolic efficiency. Then, a point mutation in the glpK gene promotes growth by improving glycerol utilization but results in increased carbon wasting as overflow metabolism. In a strain carrying both mutations, these contrasting carbon/energy saving and wasting mechanisms work together to give an 89% increase in growth rate. This study provides insight into metabolic reprogramming during adaptive laboratory evolution for fast cellular growth.

Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.

Visualization of three-way comparisons of omics data

BackgroundDensity plot visualizations (also referred to as heat maps or color maps) are widely used in different fields including large-scale omics studies in biological sciences. However, the current color-codings limit the visualizations to single datasets or pairwise comparisons.ResultsWe propose a color-coding approach for the representation of three-way comparisons. The approach is based on the HSB (hue, saturation, brightness) color model. The three compared values are assigned specific hue values from the circular hue range (e.g. red, green, and blue). The hue value representing the three-way comparison is calculated according to the distribution of three compared values. If two of the values are identical and one is different, the resulting hue is set to the characteristic hue of the differing value. If all three compared values are different, the resulting hue is selected from a color gradient running between the hues of the two most distant values (as measured by the absolute value of their difference) according to the relative position of the third value between the two. The saturation of the color representing the three-way comparison reflects the amplitude (or extent) of the numerical difference between the two most distant values according to a scale of interest. The brightness is set to a maximum value by default but can be used to encode additional information about the three-way comparison.ConclusionWe propose a novel color-coding approach for intuitive visualization of three-way comparisons of omics data.

E. coli metabolomics: capturing the complexity of a “simple” model

As the workhorse of early studies on metabolism, the metabolic pathways of E.coli are arguably the best characterized. The richness of information available aboutits pathways is broader than for any other model. However, in spite of decades of descriptive work,only recently can a significant number of E. coli metabolicnetwork constituents be analyzed simultaneously. The advent of metabolomic methods that allow tocapture qualitative as well as quantitative information about the intracellular and extracellularmetabolite profiles is starting to shed light on the remaining complexity of this simpler model. Herewe describe important findings about the physiology of E. coliresulting from emerging metabolomic studies. While a vast number of intracellular metabolitesin E. coli still remain to be characterized, the information obtainedfrom those studies can provide an unprecedented amount of information about metabolic pathways includingtheir functional elucidation, enzyme activity, metabolic fluxes, network robustness, or even the discoveryof completely novel reactions or pathways. These results are also being used to populate rich databasesand to develop computational models of E. coli metabolism thathave already proven effective to predict cellular states and will shed light on complex and untilnow still elusive regulatory principles.

Extracellular metabolite dynamics and temporal organization of metabolic function in E. coli

Bacteria dynamically exchange with their environment by constantly uptaking nutrients and secreting metabolic products and other biomolecules. While such secreted metabolites may represent a high-level reporter of metabolic activity of the culture, relatively few studies have focused on their characterization. In addition, metabolites may be potential mediators of intercellular interactions. This study aims at identifying candidate mediators of intercellular exchanges and population behavior from temporal patterns of metabolites. To do this, we used capillary electrophoresis mass spectrometry (CE-MS) to monitor secreted metabolites in synchronized continuous culture of E. coli displaying respiratory oscillations. We observed that multiple metabolites are secreted in significant quantities in the extracellular medium, including amino acids and other intermediates of central metabolism. Some of the secreted metabolite dynamics appear linked to the known valine toxicity in E. coli and are also associated with the respiratory oscillations and their dynamics. Moreover, the dynamics in the level of several amino acids appeared well correlated, suggesting organized cycles of secretion/reuptake during respiratory and metabolic shifts linked to valine levels. Overall, the current results suggest that multiple metabolites are produced and likely exchanged by E. coli during continuous growth. These appear to reflect the internal metabolic state of the cell and may form an underappreciated level of information exchange that cell populations use to coordinate activities.