Biomedical subjects

Martin Robert

Publications and source records attributed to Martin Robert.

7 recordsLinked to original sources

PubMed2006

MathDAMP: a package for differential analysis of metabolite profiles.

BACKGROUND: With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods. RESULTS: We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites. CONCLUSION: Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.

Biomarkers↗

PubMed2006

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 gamma-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.

Animals↗

PubMed2006

Metabolomics approach for enzyme discovery.

The search for novel enzymes is an important but difficult task in functional genomics. Here, we present a systematic method based on in vitro assays in combination with metabolite profiling to discover novel enzymatic activities. A complex mixture of metabolites is incubated with purified candidate proteins and the reaction mixture is subsequently profiled by capillary electrophoresis electrospray ionization mass spectrometry (CE-MS). Specific changes in the metabolite composition can directly suggest the presence of an enzymatic activity while subsequent identification of the compounds whose level changed specifically can pinpoint the actual substrate(s) and product(s) of the reaction. We first evaluated the method using several Escherichia coli metabolic enzymes and then applied it to the functional screening of uncharacterized proteins. In this manner, YbhA and YbiV proteins were found to display both phosphotransferase and phosphatase activity toward different sugars/sugar phosphates. Our approach should be broadly applicable and useful for enzyme discovery in any system.

Electrophoresis, Capillary↗

PubMed2006

Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome using artificial neural networks.

We developed a computational method to predict the retention times of peptides in HPLC using artificial neural networks (ANN). We performed stepwise multiple linear regressions and selected for ANN input amino acids that significantly affected the LC retention time. Unlike conventional linear models, the trained ANN accurately predicted the retention time of peptides containing up to 50 amino acid residues. In 834 peptides, there was a strong correlation (R2 = 0.928) between measured and predicted retention times. We demonstrated the utility of our method by the prediction of the retention time of 121,273 peptides resulting from LysC-digestion of the Escherichia coli proteome. Our approach is useful for the proteome-wide characterization of peptides and the identification of unknown peptide peaks obtained in proteome analysis.

Chromatography, High Pressure Liquid↗

PubMed2005

All systems go: launching cell simulation fueled by integrated experimental biology data.

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.

Cell Physiological Phenomena↗

PubMed2004

Toward large-scale modeling of the microbial cell for computer simulation.

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

Biotechnology↗

PubMed2004

Qualitative and quantitative analysis of amino acids by capillary electrophoresis-electrospray ionization-tandem mass spectrometry.

We describe a method to identify and quantify amino acids using capillary electrophoresis-electrospray ionization-triple-quadrupole tandem mass spectrometry (CE-ESI-MS/MS). Amino acids, including physiological amino acids, were first separated by CE under acidic pH conditions and then detected by MS/MS. To efficiently introduce the whole sample into the capillary, no electrical potential was applied to the electrospray probe until running electrophoresis. The position of the electrosprayer with respect to the MS capillary entrance drastically affected sensitivity and generation of cluster ions. MS/MS with multiple reaction monitoring (MRM) detection was performed to obtain sufficient selectivity and sensitivity. Under optimized CE-MS/MS conditions, the minimum detectable levels for 32 free amino acids normally found in proteins and other physiological amino acids were between 0.1 and 14 micromol/L with pressure injection of 50 mbar for 3 s (3 nL) at a signal-to-noise ratio of 3. For most amino acids, this constitutes a severalfold increase in sensitivity compared to CE-MS. The relative standard deviations (% RSD) for all amino acids were better than 0.4% for migration times and between 1.4% and 8.6% for peak areas (n = 10). Since amino acids exhibited characteristic MS/MS spectra, this approach is useful for the simultaneous, selective, quantitative, and reproducible analysis of amino acids in physiological and biological samples that contain various kinds of matrices. The power of the method was demonstrated by analyzing amino acids in human urine.

Amino Acids↗