Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics

Identification and quantification of water-soluble metabolites by cryoprobe-assisted nuclear magnetic resonance spectroscopy applied to microbial fermentation

We highlight a range of cryoprobe-assisted NMR methods for studying metabolite production by cyanobacteria, which should be valuable for a wide range of biological applications requiring ultrasensitivity and precise concentration determination over a large dynamic range. Cyroprobe-assisted (1)H and (13)C NMR have been applied to precise determination of metabolic products excreted during autofermentation in two cyanobacterial species: filamentous Arthrospira (Spirulina) maxima CS-328 and unicellular Synechococcus sp. PCC 7002. Several fermentative end products were identified and quantified in concentrations ranging from 50 to 3000 microM in cell-free media (a direct measurement of native-like samples) with less than 5.5% relative error in under 10 min of acquisition per sample with the assistance of an efficient water-suppression protocol. Relaxation times (T1) of these metabolites in aqueous ((1)H(2)O) solution were measured and found to vary by nearly threefold, necessitating generation of individual calibration curves for each species for highest precision. However, using a 4.5 x longer overall recycle delay between scans, the metabolite concentrations can be predicted within 25% error by calibrating only to a single calibration standard (succinate); other metabolites are then calculated on the basis of their signal integrals and known proton degeneracies. Precise ratios of concentrations of (13)C-labeled versus unlabeled metabolites were determined from integral ratios of (1)H peaks that exhibit (13)C-(1)H J-couplings and independently confirmed by direct measurement of areas of corresponding (13)C resonances. (13)C NMR was used to identify and quantify production of osmolytes, trehalose, and glucosylglycerol by A. maxima.

Metabolic profiling of intracellular metabolites in fermentation broths from beta-lactam antibiotics production by liquid chromatography-tandem mass spectrometry methods

An analytical platform comprising three LC-ESI-MS/MS methods is presented for qualitative and quantitative profiling of more than 200 intracellular metabolites. Employing a silica based zwitterionic stationary phase in the HILIC mode, in total 223 hydrophilic metabolites can be determined. In particular, amino acids, organic acids as well as nucleotide sugars were found to be well separable and detectable under acidic mobile phase conditions, while in comparison especially phosphates such as nucleotides, coenzymes or sugar phosphates as well as sugars and sugar acids performed better at higher pH. Additionally, 21 less polar analytes turned out to be amenable for separation and analysis on a pentafluorophenyl modified silica stationary phase in RP mode. Solutes were detected by tandem mass spectrometry on a triple quadrupole instrument in the selected reaction monitoring (SRM) mode and specific SRM transitions for 258 metabolites are provided. All three methods were validated with respect to the limit of quantification, linear dynamic range, precision and accuracy. Applicability of the analytical platform was evaluated by analysis of the targeted metabolites in extracts of beta-lactam antibiotics fermentation broths. Thereby, 87 metabolites were determined qualitatively in penicillin fermentation broths, and 94 compounds were found in cephalosporin extracts. In addition, a number of selected metabolites that can be determined by at least two of the presented LC-MS/MS methods was analyzed quantitatively by both, external calibration using pure standards as well as by matrix-matched calibration performing standard addition. Quantitative results obtained with the different methods agreed well, however, for some analytes external calibration was found to be ill-suited due to matrix effects.

MS-based Plant Metabolomic Approaches for Biomarker Discovery

Metabolomics, which aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples, is playing an increasingly important role in plant science. Various biological issues have been successfully studied by this holistic approach that includes global metabolite composition assessment, mutant characterization, taxonomy, developmental processes, stress response, interaction with environment, quality control assessment and mode of action of herbal medicine. This review summarizes the main mass spectrometry methods used for performing these studies and discusses the potential, but also the current limitations of the various approaches. The intention is not to cover exhaustively the field, which has considerably grown over about one decade, but to give a brief insight into the methods commonly employed and discuss some applications that reveal the potential of metabolomics in phytochemistry and systems biology.

Comparison of gas chromatography-coupled time-of-flight mass spectrometry and 1H nuclear magnetic resonance spectroscopy metabolite identification in white wines from a sensory study investigating wine body

Metabolite profiles of white wines, including Chardonnay, Pinot gris, Riesling, Sauvignon blanc, and Viognier varieties, were determined using both gas chromatography-coupled time-of-flight mass spectrometry (GC-TOF-MS) and proton nuclear magnetic resonance spectroscopy ((1)H NMR). A total of 108 metabolites were identified by GC-TOF-MS, and 51 metabolites were identified by (1)H NMR; the majority of metabolites identified include the most abundant compounds found in wine (ethanol, glycerol, sugars, organic acids, and amino acids). Compositional differences in these wines correlating to the wine sensory property "body", or viscous mouthfeel, as scored by a trained panel were identified using partial least-squares (PLS) regression. Independently calculated GC-TOF-MS and NMR-based PLS models demonstrate potential for predictive models to replace expensive, time-consuming sensory panels. At the modeling stage, correlations between the measured and predicted values have coefficients of determination of 0.83 and 0.75 for GC-TOF-MS and (1)H NMR, respectively. Additionally, the MS- and NMR-based models present new insights into the chemical basis for wine mouthfeel properties.

Investigation of chemometric instrumental transfer methods for high-resolution NMR

The implementation of direct standardization (DS), piecewise direct standardization (PDS), and double-window piecewise direct standardization (DWPDS) instrumental transfer techniques for high-resolution (1)H NMR spectral data was explored. The ability to transfer a multivariate calibration model developed for a "master or target" NMR instrument configuration to seven different ("secondary") NMR instrument configurations was measured. Partial least-squares (PLS) calibration of glucose, glycine, and citrate metabolite relative concentrations in model mixtures following mapping of the secondary instrumental configurations using DS, PDS, or DWPDS instrumental transfer allowed the performance of the different transfer methods to be assessed. Results from these studies suggest that DS and PDS transfer techniques produce similar improvements in the error of prediction compared to each other and provide a significant improvement over standard spectral preprocessing techniques including reference deconvolution and spectral binning. The DS instrumental transfer method produced the largest percent improvement in the predictions of concentrations for these model mixtures but, in general, required that additional transfer calibration standards be used. Limitations of the different instrumental transfer methods with respect to sample subset selection are also discussed.

Lemna minor L. as a model organism for ecotoxicological studies performing 1H NMR fingerprinting

A validated method applying (1)H NMR fingerprinting for the study of metabolic changes caused in Lemna minor L. by various phytotoxic substances is presented. (1)H NMR spectra of crude extracts from untreated and treated colonies with the herbicides glyphosate, mesotrione, norflurazon, paraquat and the phytotoxin pyrenophorol were subjected to multivariate analyses for detecting differences between groups of treatments. Partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were carried out in order to discriminate and classify treatments according to the observed changes in the metabolome of the plant. Although the compounds at the concentrations used did not cause macroscopically observable symptoms of phytotoxicity, characteristic metabolic changes were detectable by analyzing (1)H NMR spectra. Analyses results revealed that metabonomics applying (1)H NMR fingerprinting is a potential method for the investigation of toxicological effects of xenobiotics on L. minor, and possibly on other duckweed species, helping in the understanding of such interactions.

Identification of 4-deoxythreonic acid present in human urine using HPLC and NMR techniques

The 1H NMR spectrum of urine exhibits a large number of detectable and quantifiable metabolites and hence urine metabolite profiling is potentially useful for the study of systems biology and the discovery of biomarkers for drug development or clinical applications. While a number of metabolites (50-100) are readily detectable in urine by NMR, a much larger number is potentially available if lower concentration species can be detected unambiguously. Lower concentration metabolites are thought to be more specific to certain disease states and thus it is important to detect these metabolites with certainty. We report the identification of 4-deoxythreonic acid, a relatively low concentration endogenous metabolite that has not been previously identified in the 1H NMR spectrum of human urine. The use of HPLC and NMR spectroscopy facilitated the unequivocal and non-invasive identification of the molecule in urine which is complicated by extensive peak overlap and multiple, similar resonances from other metabolites such as 3-hydroxybutanoic acid. High-resolution detection and good sensitivity were achieved by the combination of multiple chromatographic fraction collection, sample pre-concentration, and the use of a cryogenically cooled NMR probe.

Metabolomics tools for identifying biomarkers for neuropsychiatric diseases

The repertoire of biochemicals (or small molecules) present in cells, tissue, and body fluids is known as the metabolome. Today, clinicians utilize only a very small part of the information contained in the metabolome, as revealed by the quantification of a limited set of analytes to gain information on human health. Examples include measuring glucose or cholesterol to monitor diabetes and cardiovascular health, respectively. With a focus on comprehensively studying the metabolome, the rapidly growing field of metabolomics captures the metabolic state of organisms at the global or "-omics" level. Given that the overall health status of an individual is captured by his or her metabolic state, which is a reflection of what has been encoded by the genome and modified by environmental factors, metabolomics has the potential to have a great impact upon medical practice by providing a wealth of relevant biochemical data. Metabolomics promises to improve current, single metabolites-based clinical assessments by identifying metabolic signatures (biomarkers) that embody global biochemical changes in disease, predict responses to treatment or medication side effects (pharmachometabolomics). State of the art metabolomic analytical platforms and informatics tools are being used to map potential biomarkers for a multitude of disorders including those of the central nervous system (CNS). Indeed, CNS disorders are linked to disturbances in metabolic pathways related to neurotransmitter systems (dopamine, serotonin, GABA and glutamate); fatty acids such as arachidonic acid-cascade; oxidative stress and mitochondrial function. Metabolomics tools are enabling us to map in greater detail perturbations in many biochemical pathways and links among these pathways this information is key for development of biomarkers that are disease-specific. In this review, we elaborate on some of the concepts and technologies used in metabolomics and its promise for biomarker discovery. We also highlight early findings from metabolomic studies in CNS disorders such as schizophrenia, Major Depressive Disorder (MDD), Bipolar Disorder (BD), Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).