Multivariate Analysis in Metabolomics

Metabolomics by Proton High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance of Tomato Plants Treated with Two Secondary Metabolites Isolated from Trichoderma

Trichoderma fungi release 6-pentyl-2H-pyran-2-one (1) and harzianic acid (2) secondary metabolites to improve plant growth and health protection. We isolated metabolites 1 and 2 from Trichoderma strains, whose different concentrations were used to treat seeds of Solanum lycopersicum. The metabolic profile in the resulting 15 day old tomato leaves was studied by high-resolution magic-angle-spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy directly on the whole samples without any preliminary extraction. Principal component analysis (PCA) of HRMAS NMR showed significantly enhanced acetylcholine and γ-aminobutyric acid (GABA) content accompanied by variable amount of amino acids in samples treated with both Trichoderma secondary metabolites. Seed germination rates, seedling fresh weight, and the metabolome of tomato leaves were also dependent upon doses of metabolites 1 and 2 treatments. HRMAS NMR spectroscopy was proven to represent a rapid and reliable technique for evaluating specific changes in the metabolome of plant leaves and calibrating the best concentration of bioactive compounds required to stimulate plant growth.

A Urinary Metabolic Signature for Multiple Sclerosis and Neuromyelitis Optica

Urine is a metabolite-rich biofluid that reflects the body's effort to maintain chemical and osmotic homeostasis. Clinical diagnosis routinely relies on urine samples because the collection process is easy and noninvasive. Despite these advantages, urine is an under-investigated source of biomarkers for multiple sclerosis (MS). Nuclear magnetic resonance spectroscopy (NMR) has become a common approach for analyzing urinary metabolites for disease diagnosis and biomarker discovery. For illustration of the potential of urinary metabolites for diagnosing and treating MS patients, and for differentiating between MS and other illnesses, 38 urine samples were collected from healthy controls, MS patients, and neuromyelitis optica-spectrum disorder (NMO-SD) patients and analyzed with NMR, multivariate statistics, one-way ANOVA, and univariate statistics. Urine from MS patients exhibited a statistically distinct metabolic signature from healthy and NMO-SD controls. A total of 27 metabolites were differentially altered in the urine from MS and NMO-SD patients and were associated with synthesis and degradation of ketone bodies, amino acids, propionate and pyruvate metabolism, tricarboxylic acid cycle, and glycolysis. Metabolites altered in urine from MS patients were shown to be related to known pathogenic processes relevant to MS, including alterations in energy and fatty acid metabolism, mitochondrial activity, and the gut microbiota.

Quantitative and qualitative shifts in defensive metabolites define chemical defense investment during leaf development in Inga, a genus of tropical trees

Selective pressures imposed by herbivores are often positively correlated with investments that plants make in defense. Research based on the framework of an evolutionary arms race has improved our understanding of why the amount and types of defenses differ between plant species. However, plant species are exposed to different selective pressures during the life of a leaf, such that expanding leaves suffer more damage from herbivores and pathogens than mature leaves. We hypothesize that this differential selective pressure may result in contrasting quantitative and qualitative defense investment in plants exposed to natural selective pressures in the field. To characterize shifts in chemical defenses, we chose six species of Inga, a speciose Neotropical tree genus. Focal species represent diverse chemical, morphological, and developmental defense traits and were collected from a single site in the Amazonian rainforest. Chemical defenses were measured gravimetrically and by characterizing the metabolome of expanding and mature leaves. Quantitative investment in phenolics plus saponins, the major classes of chemical defenses identified in Inga, was greater for expanding than mature leaves (46% and 24% of dry weight, respectively). This supports the theory that, because expanding leaves are under greater selective pressure from herbivores, they rely more upon chemical defense as an antiherbivore strategy than do mature leaves. Qualitatively, mature and expanding leaves were distinct and mature leaves contained more total and unique metabolites. Intraspecific variation was greater for mature leaves than expanding leaves, suggesting that leaf development is canalized. This study provides a snapshot of chemical defense investment in a speciose genus of tropical trees during the short, few-week period of leaf development. Exploring the metabolome through quantitative and qualitative profiling enables a more comprehensive examination of foliar chemical defense investment.

Biotransformation of Cobicistat: Metabolic Pathways and Enzymes

BACKGROUND

Cobicistat (COBI) is a pharmacoenhancer for antiretroviral therapy.

OBJECTIVE

The current study was designed to profile the metabolic pathways of COBI and to determine the enzymes that contribute to COBI metabolism.

METHOD

We screened COBI metabolites in mice and human liver microsomes. We also used cDNAexpressed human cytochromes P450 (CYPs) to explore the role of human enzymes in COBI metabolism.

RESULTS

Twenty new and three known metabolites of COBI were identified in mouse urine and feces. These new metabolic pathways of COBI include glycine conjugation, N-acetyl cysteine conjugation, morpholine ring-opening, and thiazole ring-opening. Twelve of COBI metabolites were further confirmed in mouse and human liver microsomes, including nine new metabolites. Consistent with the previous report, CYP3A4 and CYP2D6 were determined as the major enzymes that contribute to COBI metabolism.

CONCLUSION

This study provided a full map of COBI metabolism. These results can be used to manage CYP-mediated drug-drug interactions and adverse drug reactions that are associated with COBI-containing regimens in human.

A batch correction method for liquid chromatography-mass spectrometry data that does not depend on quality control samples

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography-mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. "Background correction" methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

Metabolome analysis of 20 taxonomically related benzylisoquinoline alkaloid-producing plants

BACKGROUND

Recent progress toward the elucidation of benzylisoquinoline alkaloid (BIA) metabolism has focused on a small number of model plant species. Current understanding of BIA metabolism in plants such as opium poppy, which accumulates important pharmacological agents such as codeine and morphine, has relied on a combination of genomics and metabolomics to facilitate gene discovery. Metabolomics studies provide important insight into the primary biochemical networks underpinning specialized metabolism, and serve as a key resource for metabolic engineering, gene discovery, and elucidation of governing regulatory mechanisms. Beyond model plants, few broad-scope metabolomics reports are available for the vast number of plant species known to produce an estimated 2500 structurally diverse BIAs, many of which exhibit promising medicinal properties.

RESULTS

We applied a multi-platform approach incorporating four different analytical methods to examine 20 non-model, BIA-accumulating plant species. Plants representing four families in the Ranunculales were chosen based on reported BIA content, taxonomic distribution and importance in modern/traditional medicine. One-dimensional (1)H NMR-based profiling quantified 91 metabolites and revealed significant species- and tissue-specific variation in sugar, amino acid and organic acid content. Mono- and disaccharide sugars were generally lower in roots and rhizomes compared with stems, and a variety of metabolites distinguished callus tissue from intact plant organs. Direct flow infusion tandem mass spectrometry provided a broad survey of 110 lipid derivatives including phosphatidylcholines and acylcarnitines, and high-performance liquid chromatography coupled with UV detection quantified 15 phenolic compounds including flavonoids, benzoic acid derivatives and hydroxycinnamic acids. Ultra-performance liquid chromatography coupled with high-resolution Fourier transform mass spectrometry generated extensive mass lists for all species, which were mined for metabolites putatively corresponding to BIAs. Different alkaloids profiles, including both ubiquitous and potentially rare compounds, were observed.

CONCLUSIONS

Extensive metabolite profiling combining multiple analytical platforms enabled a more complete picture of overall metabolism occurring in selected plant species. This study represents the first time a metabolomics approach has been applied to most of these species, despite their importance in modern and traditional medicine. Coupled with genomics data, these metabolomics resources serve as a key resource for the investigation of BIA biosynthesis in non-model plant species.

Defensive Armor of Potato Tubers: Nonpolar Metabolite Profiling, Antioxidant Assessment, and Solid-State NMR Compositional Analysis of Suberin-Enriched Wound-Healing Tissues

The cultivation, storage, and distribution of potato tubers are compromised by mechanical damage and suboptimal healing. To investigate wound-healing progress in cultivars with contrasting russeting patterns, metabolite profiles reported previously for polar tissue extracts were complemented by GC/MS measurements for nonpolar extracts and quantitative (13)C NMR of interfacial solid suspensions. Potential marker compounds that distinguish cultivar type and wound-healing time point included fatty acids, fatty alcohols, alkanes, glyceryl esters, α,ω-fatty diacids, and hydroxyfatty acids. The abundant long-chain fatty acids in nonpolar extracts and solids from the smooth-skinned Yukon Gold cultivar suggested extensive suberin biopolymer formation; this hypothesis was supported by high proportions of arenes, alkenes, and carbonyl groups in the solid and among the polar markers. The absence of many potential marker classes in nonpolar Atlantic extracts and interfacial solids suggested a limited extent of suberization. Modest scavenging activities of all nonpolar extracts indicate that the majority of antioxidants produced in response to wounding are polar.

Generalized adaptive intelligent binning of multiway data

NMR metabolic fingerprinting methods almost exclusively rely upon the use of one-dimensional (1D) ¹H NMR data to gain insights into chemical differences between two or more experimental classes. While 1D ¹H NMR spectroscopy is a powerful, highly informative technique that can rapidly and nondestructively report details of complex metabolite mixtures, it suffers from significant signal overlap that hinders interpretation and quantification of individual analytes. Two-dimensional (2D) NMR methods that report heteronuclear connectivities can reduce spectral overlap, but their use in metabolic fingerprinting studies is limited. We describe a generalization of Adaptive Intelligent binning that enables its use on multidimensional datasets, allowing the direct use of nD NMR spectroscopic data in bilinear factorizations such as principal component analysis (PCA) and partial least squares (PLS).

Metabolic Impacts of Using Nitrogen and Copper-Regulated Promoters to Regulate Gene Expression in Neurospora crassa

The filamentous fungus Neurospora crassa is a long-studied eukaryotic microbial system amenable to heterologous expression of native and foreign proteins. However, relatively few highly tunable promoters have been developed for this species. In this study, we compare the tcu-1 and nit-6 promoters for controlled expression of a GFP reporter gene in N. crassa. Although the copper-regulated tcu-1 has been previously characterized, this is the first investigation exploring nitrogen-controlled nit-6 for expression of heterologous genes in N. crassa. We determined that fragments corresponding to 1.5-kb fragments upstream of the tcu-1 and nit-6 open reading frames are needed for optimal repression and expression of GFP mRNA and protein. nit-6 was repressed using concentrations of glutamine from 2 to 20 mM and induced in medium containing 0.5–20 mM nitrate as the nitrogen source. Highest levels of expression were achieved within 3 hr of induction for each promoter and GFP mRNA could not be detected within 1 hr after transfer to repressing conditions using the nit-6 promoter. We also performed metabolic profiling experiments using proton NMR to identify changes in metabolite levels under inducing and repressing conditions for each promoter. The results demonstrate that conditions used to regulate tcu-1 do not significantly change the primary metabolome and that the differences between inducing and repressing conditions for nit-6 can be accounted for by growth under nitrate or glutamine as a nitrogen source. Our findings demonstrate that nit-6 is a tunable promoter that joins tcu-1 as a choice for regulation of gene expression in N. crassa.

Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states.

Combining DI-ESI-MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death.

Liquid Chromatography-Mass Spectrometry Metabolic and Lipidomic Sample Preparation Workflow for Suspension-Cultured Mammalian Cells using Jurkat T lymphocyte Cells

Metabolomics is the comprehensive study of metabolism as it pertains to an organism or biological system. Lipidomics, a subset discipline of metabolomics, encompasses the study of cellular lipid functions: including pathways, networks, and interactions. The abundance of metabolites and lipids, along with their contribution to health and disease, makes metabolomics a valuable tool for biomarker research. Disease biomarker identification requires a reproducible, sensitive, and accurate analytical platform. Although transcriptomic and proteomic areas have well-established protocols for sample preparation and data processing, the metabolomics field is still developing comparable standardized conventions. Furthermore, of the few comparative LC-MS metabolomic studies that have been applied to mammalian cell cultures, most are targeted to adherent cell lines. The purpose of this work was to optimize a sample preparation workflow for the cellular metabolomic analysis of suspension-cultured mammalian cells using commercially available Jurkat T lymphocytes as a model system. The current investigation evaluated commonly used sample preparation techniques for reproducibility, accuracy, and applicability to untargeted biomarker discovery. Results show ammoniated cell rinsing solutions to be an effective means to remove extracellular components present in the media without causing ion suppression or affecting the integrity of the cellular membrane. Additionally, a novel workflow was designed to allow for the combined analysis of metabolites and lipids from mammalian suspension cells from a single cell pellet. The Folch lipid extraction protocol was coupled to an 80% MeOH metabolite isolation to ensure high extraction efficiency for phospholipids and triacylglycerides. While the workflow was tailored to cells in suspension, it could also be applied to adherent cell lines.

Phylogeny in defining model plants for lignocellulosic ethanol production: a comparative study of Brachypodium distachyon, wheat, maize, and Miscanthus x giganteus leaf and stem biomass

The production of ethanol from pretreated plant biomass during fermentation is a strategy to mitigate climate change by substituting fossil fuels. However, biomass conversion is mainly limited by the recalcitrant nature of the plant cell wall. To overcome recalcitrance, the optimization of the plant cell wall for subsequent processing is a promising approach. Based on their phylogenetic proximity to existing and emerging energy crops, model plants have been proposed to study bioenergy-related cell wall biochemistry. One example is Brachypodium distachyon, which has been considered as a general model plant for cell wall analysis in grasses. To test whether relative phylogenetic proximity would be sufficient to qualify as a model plant not only for cell wall composition but also for the complete process leading to bioethanol production, we compared the processing of leaf and stem biomass from the C3 grasses B. distachyon and Triticum aestivum (wheat) with the C4 grasses Zea mays (maize) and Miscanthus x giganteus, a perennial energy crop. Lambda scanning with a confocal laser-scanning microscope allowed a rapid qualitative analysis of biomass saccharification. A maximum of 108-117 mg ethanol·g(-1) dry biomass was yielded from thermo-chemically and enzymatically pretreated stem biomass of the tested plant species. Principal component analysis revealed that a relatively strong correlation between similarities in lignocellulosic ethanol production and phylogenetic relation was only given for stem and leaf biomass of the two tested C4 grasses. Our results suggest that suitability of B. distachyon as a model plant for biomass conversion of energy crops has to be specifically tested based on applied processing parameters and biomass tissue type.

Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Identification of biochemical differences between different forms of male infertility by nuclear magnetic resonance (NMR) spectroscopy

PURPOSE

The aim of this study was to analyze the seminal plasma of patients with idiopathic/male factor infertility and healthy controls with proven fertility by NMR spectroscopy, with a hope of establishing difference in biomarker profiles, if any, between the groups.

METHODS

A total of 103 subjects visiting the infertility clinic of Manipal University with normozoospermic parameters, oligozoospermia, asthenozoospermia, azoospermia and teratozoospermia were included. Semen characteristics were analysed by standard criteria. Seminal plasma was subjected to NMR spectroscopy at a 700 MHz (1)H frequency. The resultant data was analyzed by appropriate software.

RESULTS

The analysis revealed significant differences between the fertile control group and other forms of male infertility. Interestingly, seminal plasma profile of the idiopathic infertility group showed distinct segregation from the control population as well as other infertile groups. The difference in biomarker profiles between the idiopathic infertility and the rest of the groups combined could originate from either the up-regulation or down regulation of a several compounds, including lysine, arginine, tyrosine, citrate, proline and fructose.

CONCLUSION

Our data suggests the presence of a metabolic reason behind the origin of idiopathic infertility. (1)H NMR based metabonomic profiling based on concentration of biomarker lysine has the potential to aid in the detection and diagnosis of idiopathic infertility in an efficient manner.

Mid-ATR-FTIR spectroscopic profiling of HIV/AIDS sera for novel systems diagnostics in global health

Global health, whether in developed or developing countries, is in need of robust systems diagnostics for major diseases, such as HIV/AIDS, impacting the world populations. Fourier transform Infrared (FTIR) spectroscopy of serum is a quick and reagent-free methodology with which to analyze metabolic alterations such as those caused by disease or treatment. In this study, Attenuated Total Reflectance Fourier-Transform (ATR-FTIR) Spectroscopy was investigated as a means of distinguishing HIV-infected treatment-experienced (HIV(pos) ART(pos), n=39) and HIV-infected-treatment-naïve (HIV(pos) ART(neg), n=16) subjects from uninfected control subjects (n=30). Multivariate pattern recognition techniques, including partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA), successfully distinguished sample classes, while univariate approaches identified significant differences (p<0.05) after Benjamini-Hochberg corrections. OPLS-DA discriminated between all groups with sensitivity, specificity, and accuracy of >90%. Compared to uninfected controls, HIV(pos) ART(pos) and HIV(pos) ART(neg) subjects displayed significant differences in spectral regions linked to lipids/fatty acids (3010 cm(-1)), carbohydrates (1299 cm(-1); 1498 cm(-1)), glucose (1035 cm(-1)), and proteins (1600 cm(-1); 1652 cm(-1)). These are all molecules shown by conventional biochemical analysis to be affected by HIV/ART interference. The biofluid metabolomics approach applied here successfully differentiated global metabolic profiles of HIV-infected patients and uninfected controls and detected potential biomarkers for development into indicators of host response to treatment and/or disease progression. Our findings therefore contribute to ongoing efforts for capacity-building in global health for robust omics science and systems diagnostics towards major diseases impacting population health.

Quantitative NMR metabolite profiling of methicillin-resistant and methicillin-susceptible Staphylococcus aureus discriminates between biofilm and planktonic phenotypes

Wound bioburden in the form of colonizing biofilms is a major contributor to nonhealing wounds. Staphylococcus aureus is a Gram-positive, facultative anaerobe commonly found in chronic wounds; however, much remains unknown about the basic physiology of this opportunistic pathogen, especially with regard to the biofilm phenotype. Transcriptomic and proteomic analysis of S. aureus biofilms have suggested that S. aureus biofilms exhibit an altered metabolic state relative to the planktonic phenotype. Herein, comparisons of extracellular and intracellular metabolite profiles detected by (1)H NMR were conducted for methicillin-resistant (MRSA) and methicillin-susceptible (MSSA) S. aureus strains grown as biofilm and planktonic cultures. Principal component analysis distinguished the biofilm phenotype from the planktonic phenotype, and factor loadings analysis identified metabolites that contributed to the statistical separation of the biofilm from the planktonic phenotype, suggesting that key features distinguishing biofilm from planktonic growth include selective amino acid uptake, lipid catabolism, butanediol fermentation, and a shift in metabolism from energy production to assembly of cell-wall components and matrix deposition. These metabolite profiles provide a basis for the development of metabolite biomarkers that distinguish between biofilm and planktonic phenotypes in S. aureus and have the potential for improved diagnostic and therapeutic use in chronic wounds.

¹H NMR Spectroscopy and Multivariate Analysis of Monovarietal EVOOs as a Tool for Modulating Coratina-Based Blends

Coratina cultivar-based olives are very common among 100% Italian extra virgin olive oils (EVOOs). Often, the very spicy character of this cultivar, mostly due to the high polyphenols concentration, requires blending with other “sweetener” oils. In this work, monovarietal EVOO samples from the Coratina cultivar (Apulia, Italy) were investigated and compared with monovarietal EVOO from native or recently introduced Apulian (Italy) cultivars (Ogliarola Garganica, Ogliarola Barese, Cima di Mola, Peranzana, Picholine), from Calabria (Italy) (Carolea and Rossanese) and from other Mediterranean countries, such as Spain (Picual) and Greece (Kalamata and Koroneiki) by ¹H NMR spectroscopy and multivariate analysis (principal component analysis (PCA)). In this regard, NMR signals could allow a first qualitative evaluation of the chemical composition of EVOO and, in particular, of its minor component content (phenols and aldehydes), an intrinsic behavior of EVOO taste, related to the cultivar and geographical origins. Moreover, this study offers an opportunity to address blended EVOOs tastes by using oils from a specific region or country of origin.

The current state of drug discovery and a potential role for NMR metabolomics

The pharmaceutical industry has significantly contributed to improving human health. Drugs have been attributed to both increasing life expectancy and decreasing health care costs. Unfortunately, there has been a recent decline in the creativity and productivity of the pharmaceutical industry. This is a complex issue with many contributing factors resulting from the numerous mergers, increase in out-sourcing, and the heavy dependency on high-throughput screening (HTS). While a simple solution to such a complex problem is unrealistic and highly unlikely, the inclusion of metabolomics as a routine component of the drug discovery process may provide some solutions to these problems. Specifically, as the binding affinity of a chemical lead is evolved during the iterative structure-based drug design process, metabolomics can provide feedback on the selectivity and the in vivo mechanism of action. Similarly, metabolomics can be used to evaluate and validate HTS leads. In effect, metabolomics can be used to eliminate compounds with potential efficacy and side effect problems while prioritizing well-behaved leads with druglike characteristics.

Simultaneous Phase and Scatter Correction for NMR Datasets

Nuclear magnetic resonance (NMR) spectroscopy has proven invaluable in the diverse field of chemometrics due to its ability to deliver information-rich spectral datasets of complex mixtures for analysis by techniques such as principal component analysis (PCA). However, NMR datasets present a unique challenge during preprocessing due to differences in phase offsets between individual spectra, thus complicating the correction of random dilution factors that may also occur. We show that simultaneously correcting phase and dilution errors in NMR datasets representative of metabolomics data yields improved cluster quality in PCA scores space, even with significant initial phase errors in the data.

Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes

Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologically-irrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies.

NMR Metabolomics Analysis of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.