NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Multiple sclerosis patient-derived CSF induces transcriptional changes in proliferating oligodendrocyte progenitors

BACKGROUND

Cerebrospinal fluid (CSF) is in contact with brain parenchyma and ventricles, and its composition might influence the cellular physiology of oligodendrocyte progenitor cells (OPCs) thereby contributing to multiple sclerosis (MS) disease pathogenesis.

OBJECTIVE

To identify the transcriptional changes that distinguish the transcriptional response induced in proliferating rat OPCs upon exposure to CSF from primary progressive multiple sclerosis (PPMS) or relapsing remitting multiple sclerosis (RRMS) patients and other neurological controls.

METHODS

We performed gene microarray analysis of OPCs exposed to CSF from neurological controls, or definitive RRMS or PPMS disease course. Results were confirmed by quantitative reverse transcriptase polymerase chain reaction, immunocytochemistry and western blot of cultured cells, and validated in human brain specimens.

RESULTS

We identified common and unique oligodendrocyte genes for each treatment group. Exposure to CSF from PPMS uniquely induced branching of cultured progenitors and related transcriptional changes, including upregulation (P<0.05) of the adhesion molecule GALECTIN-3/Lgals3, which was also detected at the protein level in brain specimens from PPMS patients. This pattern of gene expression was distinct from the transcriptional programme of oligodendrocyte differentiation during development.

CONCLUSIONS

Despite evidence of morphological differentiation induced by exposure to CSF of PPMS patients, the overall transcriptional response elicited in cultured OPCs was consistent with the activation of an aberrant transcriptional programme.

NMR metabonomics of cerebrospinal fluid distinguishes between Parkinson's disease and controls

This study assesses if nuclear magnetic resonance (NMR) metabonomics can discriminate between Parkinson's disease (PD) patients and control subjects, and consequently identify metabolic markers for the disease. One-dimensional (1)H NMR spectroscopy was used for quantitative analysis of metabolites in the cerebrospinal fluid (CSF) from 10 PD patients and 10 control individuals, together with uni- and multivariate statistical analysis to discriminate between the groups and to identify significantly altered metabolite concentrations. In total 60 metabolites were identified and of those 38 were quantified in all CSF samples. An overall lowering of metabolite content was observed in PD patients compared to control subjects (fold change of 0.85±0.30). Multivariate statistics reveal significant changes (ǀw*ǀ>0.2) among nine metabolites (alanine, creatinine, dimethylamine, glucose, lactate, mannose, phenylalanine, 3-hydroxyisobutyric acid and 3-hydroxyisovaleric acid). Three of these (alanine, creatinine and mannose) are identified as significantly changed also by univariate statistics (p<0.00132, Bonferroni corrected). Panels with all or a selected set of these metabolites were successfully used for discriminating between the two groups. In conclusion, NMR metabonomics can readily determine metabolite concentrations in CSF, identify putative biomarkers that distinguish between the PD patients and control subjects, and thus potentially become a tool for diagnostic purposes.

Metabolomics and diabetes: analytical and computational approaches

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

Enhancing metabolomics research through data mining

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable.

BIOLOGICAL SIGNIFICANCE

Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.

Metabonomics and drug development

Metabolites as an end product of metabolism possess a wealth of information about altered metabolic control and homeostasis that is dependent on numerous variables including age, sex, and environment. Studying significant changes in the metabolite patterns has been recognized as a tool to understand crucial aspects in drug development like drug efficacy and toxicity. The inclusion of metabonomics into the OMICS study platform brings us closer to define the phenotype and allows us to look at alternatives to improve the diagnosis of diseases. Advancements in the analytical strategies and statistical tools used to study metabonomics allow us to prevent drug failures at early stages of drug development and reduce financial losses during expensive phase II and III clinical trials. This chapter introduces metabonomics along with the instruments used in the study; in addition relevant examples of the usage of metabonomics in the drug development process are discussed along with an emphasis on future directions and the challenges it faces.

Rapid and non-invasive diagnosis of coronary artery disease via clinical laboratory parameters and1H-NMR spectra of human blood plasma

Coronary artery disease (CAD), one of the most common fatal diseases in the world, was examinedviainvestigation of the¹H-NMR spectra of human blood plasma and clinical laboratory parameters with the aim of early disease diagnosis.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

An UPLC-MS-based metabolomics investigation on the anti-fatigue effect of salidroside in mice

An ultra-performance liquid chromatography-quadrupole time-of-flight-based metabolomic approach was developed to study influence of salidroside, an anti-fatigue ingredient from Rhoiola rosea, on urinary metabolic profiling of rats to a single dose of 180 mg/kg per day. Unsupervised principal component analysis (PCA) and supervised orthogonal pre-projection to latent structures discriminate analysis (OPLS-DA) on metabolite profiling revealed obvious differentiation between the salidroside treated groups and controls in both positive and negative ion modes. Eleven urinary metabolites contributing to the differentiation were identified as anti-fatigue biomarkers: N-acetylserotonin, 2-Methoxyestrone 3-glucuronide, Taurine, Melatonin, Sorbitol, Geranyl diphosphate, Z-nucleotide, Cortisone, Dihydrocortisol, Sebacic acid, Pregnenolone sulfate. The physiological significance of these biomarkers is discussed. The work showed that metabolomics is a powerful tool in studying the anti-fatigue effects of natural compound salidroside on multiple targets in vivo.

NMR-based metabonomics survey in rats envenomed by Hemiscorpius lepturus venom

About 1% of scorpion stings in Iran have been reported in cities and more than 5% in rural areas. The genus Hemiscorpous lepturus belongs to the family Hemiscorpiidae that is endemic in the south-western province of Khoozestan and other parts of western Iran. Although this species is responsible for only 10% of the reported stings, it is also responsible for 95% of mortalities. The heavy cytotoxic and neurotoxic venomous effects of scorpion sting may cause serious disorders such as erythema, purpuric changes, bulla, necrosis and ulcer, either alone or in combination. The toxic effects of H. lepturus venom cause vast changes in both primary and secondary metabolites of the victim, which finally lead to death if not treated early enough. Metabolomics is the systematic study of chemical fingerprints resulting from cytotoxic and pathogenic reactions of cells. We studied the toxic mechanism of H. lepturus venom on metabolome profiling of the victims along with biochemical pathways, organs and physiological detectable effects of this venom by the help of (1)H NMR. Our results showed that pyrimidine, histidine and tyrosine metabolisms, and steroid hormone biosynthesis were the most affected pathways. The major action of the crude venom is on the pancreas, and also on the nerve cells, spleen and mitochondria, causing acute seizures, which resemble the early markers of myocardial injury and seizure disorders.

Metabolomics in dyslipidemia

Hyperlipidemia is an important public health problem with increased incidence and prevalence worldwide. Current clinical biomarkers, triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol lack the necessary specificity and sensitivity and only increase significantly after serious dyslipidemia. Therefore, sensitive biomarkers are needed for hyperlipidemia. Hyperlipidemia-specific biomarkers would improve clinical diagnosis and therapeutic treatment at early disease stages. The aim of metabolomics is to identify untargeted and global small-molecule metabolite profiles from cells, biofluids, and tissues. This method offers the potential for a holistic approach to improve disease diagnoses and our understanding of underlying pathologic mechanisms. This review summarizes analytical techniques, data collection and analysis for metabolomics, and metabolomics in hyperlipidemia animal models and clinical studies. Mechanisms of hypolipemia and antilipemic drug therapy are also discussed. Metabolomics provides a new opportunity to gain insight into metabolic profiling and pathophysiologic mechanisms of hyperlipidemia.

NMR-based metabolomics: from sample preparation to applications in nutrition research

Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Measurement of these metabolites is achieved through use of analytical techniques such as NMR and mass spectrometry coupled to liquid chromatography. Combining metabolomic data with multivariate data analysis tools allows the elucidation of alterations in metabolic pathways under different physiological conditions. Applications of NMR-based metabolomics have grown in recent years and it is now widely used across a number of disciplines. The present review gives an overview of the developments in the key steps involved in an NMR-based metabolomics study. Furthermore, there will be a particular emphasis on the use of NMR-based metabolomics in nutrition research.

Unraveling biochemical pathways affected by mitochondrial dysfunctions using metabolomic approaches

Mitochondrial dysfunction(s) (MDs) can be defined as alterations in the mitochondria, including mitochondrial uncoupling, mitochondrial depolarization, inhibition of the mitochondrial respiratory chain, mitochondrial network fragmentation, mitochondrial or nuclear DNA mutations and the mitochondrial accumulation of protein aggregates. All these MDs are known to alter the capacity of ATP production and are observed in several pathological states/diseases, including cancer, obesity, muscle and neurological disorders. The induction of MDs can also alter the secretion of several metabolites, reactive oxygen species production and modify several cell-signalling pathways to resolve the mitochondrial dysfunction or ultimately trigger cell death. Many metabolites, such as fatty acids and derived compounds, could be secreted into the blood stream by cells suffering from mitochondrial alterations. In this review, we summarize how a mitochondrial uncoupling can modify metabolites, the signalling pathways and transcription factors involved in this process. We describe how to identify the causes or consequences of mitochondrial dysfunction using metabolomics (liquid and gas chromatography associated with mass spectrometry analysis, NMR spectroscopy) in the obesity and insulin resistance thematic.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Lipid characterization of individual porcine oocytes by dual mode DESI-MS and data fusion

The development of sensitive measurements to analyze individual cells is of relevance to elucidate specialized roles or metabolic functions of each cell under physiological and pathological conditions. Lipids play multiple and critical roles in cellular functions and the application of analytical methods in the lipidomics area is of increasing interest. In this work, in vitro maturation of porcine oocytes was studied. Two independent sources of chemical information (represented by mass spectra in the positive and negative ion modes) from single oocytes (immature oocytes, 24-h and 44-h in vitro matured oocytes) were acquired by using desorption electrospray ionization-mass spectrometry (DESI-MS). Low and mid-level data fusion strategies are presented with the aim of better exploring the large amount of chemical information contained in the two mass spectrometric lipid profiles. Data were explored by principal component analysis (PCA) within the two multi-block approaches to include information on free fatty acids, phospholipids, cholesterol-related molecules, di- and triacylglycerols. After data fusion, clearer differences among immature and in vitro matured porcine oocytes were observed, which provide novel information regarding lipid metabolism throughout oocyte maturation. In particular, changes in TAG composition, as well as increase in fatty acid metabolism and membrane complexity were evidenced during the in vitro maturation process. This information can assist the improvement of in vitro embryo production for porcine species.

Uncovering the molecular networks in periodontitis

Periodontitis is a complex immune-inflammatory disease that results from a preestablished infection in gingiva, mainly due to Gram-negative bacteria that colonize deeper in gingival sulcus and latter periodontal pocket. Host inflammatory and immune responses have both protective and destructive roles. Although cytokines, prostaglandins, and proteases struggle against microbial burden, these molecules promote connective tissue loss and alveolar bone resorption, leading to several histopathological changes, namely destruction of periodontal ligament, deepening of periodontal pocket, and bone loss, which can converge to attain tooth loss. Despite the efforts of genomics, transcriptomics, proteomics/peptidomics, and metabolomics, there is no available biomarker for periodontitis diagnosis, prognosis, and treatment evaluation, which could assist on the established clinical evaluation. Nevertheless, some genes, transcripts, proteins and metabolites have already shown a different expression in healthy subjects and in patients. Though, so far, 'omics approaches only disclosed the host inflammatory response as a consequence of microbial invasion in periodontitis and the diagnosis in periodontitis still relies on clinical parameters, thus a molecular tool for assessing periodontitis lacks in current dental medicine paradigm. Saliva and gingival crevicular fluid have been attracting researchers due to their diagnostic potential, ease, and noninvasive nature of collection. Each one of these fluids has some advantages and disadvantages that are discussed in this review.

Sample preparation issues in NMR-based plant metabolomics: optimisation for Vitis wood samples

INTRODUCTION

Nuclear magnetic resonance (NMR) is one of the most commonly used analytical techniques in plant metabolomics. Although this technique is very reproducible and simple to implement, sample preparation procedures have a great impact on the quality of the metabolomics data.

OBJECTIVE

Investigation of different sample preparation methods and establishment of an optimised protocol for untargeted NMR-based metabolomics of Vitis vinifera L. wood samples.

METHODS

Wood samples from two different cultivars of V. vinifera with well-defined phenotypes (Gamaret and 2091) were selected as reference materials. Different extraction solvents (successively, dichloromethane, methanol and water, as well as ethyl acetate and 7:3 methanol-water (v/v)) and deuterated solvents (methanol-d4, 7:3 chloroform-d-methanol-d4 (v/v), dimethylsulphoxide-d6 and 9:1 dimethylsulphoxide-d6-water-d2 (v/v)) were evaluated for NMR acquisition, and the spectral quality was compared. The optimal extract concentration, chemical shift stability and peak area repeatability were also investigated.

RESULTS

Ethyl acetate was found to be the most satisfactory solvent for the extraction of all representative chemical classes of secondary metabolites in V. vinifera wood. The optimal concentration of dried extract was 10 mg/mL and 7:3 chloroform-d-methanol-d4 (v/v) was the most suitable solvent system for NMR analysis. Multivariate data analysis was used to estimate the biological variation and clustering between different cultivars.

CONCLUSION

Close attention should be paid to all required procedures before NMR analysis, especially to the selection of an extraction solvent and a deuterated solvent system to perform an extensive metabolomic survey of the specific matrix.

Metabonomic analysis of serum of workers occupationally exposed to arsenic, cadmium and lead for biomarker research: a preliminary study

Environmental metabonomics is the application of metabonomics to characterize the interactions of organisms with their environment. Metabolic profiling is an exciting addition to the armory of the epidemiologist for the discovery of new disease risk biomarkers and diagnostics. This work is a continuation of research searching for preclinical serum markers in a group of 389 healthy smelter workers exposed to lead, cadmium and arsenic. Changes in the metabolic profiles were studied using Proton Nuclear Magnetic Resonance Spectroscopy on pooled serum samples from both the metal exposed and control groups. These multivariate metabonomic datasets were analyzed with Principal Component Analysis and Partial Least Squares Discriminant Analysis. Analysis of metabolic profiles of people exposed to heavy metals suggests energy metabolism disturbance induced by heavy metals. Changes in lipid fraction (very-low-density lipoprotein - VLDL, low-density lipoprotein - LDL), unsaturated lipids and in the level of amino acids suggest perturbation of the metabolism of lipids and amino acids. This study illustrated the high reliability of NMR-based metabonomic profiling on the study of the biochemical effects induced by the mixture of heavy metals. This approach is capable of identifying intermediate biomarkers of response to toxicants at environmental/occupational concentrations, paving the way to its use in a monitoring of smelter workers exposed to low doses of lead, cadmium and arsenic.

Unravelling of the health effects of polyphenols is a complex puzzle complicated by metabolism

Plant metabolism creates complex mixtures of polyphenols in plant foods. Epidemiology and human trials reduced this complexity, by studying specific foods; subclasses of polyphenols; individual polyphenols, or total antioxidant capacity (TAC). This implies the following assumptions: (1) a limited number of potent polyphenols exists; (2) well-defined natural potent mixtures of polyphenols exist; (3) polyphenols share a common biological activity (e.g. antioxidant activity). To find potent polyphenols (1st assumption), in vitro screening has been widely applied, but most published results are of limited use because metabolism, changing biological activity profoundly, has frequently not been considered. The abundant anecdotal evidence for natural potent mixtures of polyphenols (2nd assumption) on the internet is very hard to verify. Additionally, cross-cultural studies have revealed the potency of e.g. cocoa. Polyphenols share the antioxidant phenolic group which inspired researchers to measure their antioxidant activity, thus greatly reducing complexity (3rd assumption). Unfortunately, the elegant antioxidant hypothesis has to be rejected, because poor absorption and extensive metabolism annihilate any contribution to the endogenous body antioxidants. In conclusion, the above assumptions are hard to verify, and no quick answers are to be expected. Future research should focus on structure-activity relations at nanomolar levels and explore metabolomics.

Profiling of volatile organic compounds in exhaled breath as a strategy to find early predictive signatures of asthma in children

Wheezing is one of the most common respiratory symptoms in preschool children under six years old. Currently, no tests are available that predict at early stage who will develop asthma and who will be a transient wheezer. Diagnostic tests of asthma are reliable in adults but the same tests are difficult to use in children, because they are invasive and require active cooperation of the patient. A non-invasive alternative is needed for children. Volatile Organic Compounds (VOCs) excreted in breath could yield such non-invasive and patient-friendly diagnostic. The aim of this study was to identify VOCs in the breath of preschool children (inclusion at age 2-4 years) that indicate preclinical asthma. For that purpose we analyzed the total array of exhaled VOCs with Gas Chromatography time of flight Mass Spectrometry of 252 children between 2 and 6 years of age. Breath samples were collected at multiple time points of each child. Each breath-o-gram contained between 300 and 500 VOCs; in total 3256 different compounds were identified across all samples. Using two multivariate methods, Random Forests and dissimilarity Partial Least Squares Discriminant Analysis, we were able to select a set of 17 VOCs which discriminated preschool asthmatic children from transient wheezing children. The correct prediction rate was equal to 80% in an independent test set. These VOCs are related to oxidative stress caused by inflammation in the lungs and consequently lipid peroxidation. In conclusion, we showed that VOCs in the exhaled breath predict the subsequent development of asthma which might guide early treatment.

Multi-analytical platform metabolomic approach to study miltefosine mechanism of action and resistance in Leishmania

Miltefosine (MT) (hexadecylphosphocholine) was implemented to cope with resistance against antimonials, the classical treatment in Leishmaniasis. Given the scarcity of anti- Leishmania (L) drugs and the increasing appearance of resistance, there is an obvious need for understanding the mechanism of action and development of such resistance. Metabolomics is an increasingly popular tool in the life sciences due to it being a relatively fast and accurate technique that can be applied either with a particular focus or in a global manner to reveal new knowledge about biological systems. Three analytical platforms, gas chromatography (GC), liquid chromatography (LC) and capillary electrophoresis (CE) have been coupled to mass spectrometry (MS) to obtain a broad picture of metabolic changes in the parasite. Impairment of the polyamine metabolism from arginine (Arg) to trypanothione in susceptible parasites treated with MT was in some way expected, considering the reactive oxygen species (ROS) production described for MT. Importantly, in resistant parasites an increase in the levels of amino acids was the most outstanding feature, probably related to the adaptation of the resistant strain for its survival inside the parasitophorous vacuole.

Magnetic susceptibility to measure total protein concentration from NMR metabolite spectra: Demonstration on blood plasma

PURPOSE

Accurate metabolite and protein quantification in blood plasma and other body fluids from one single NMR measurement, allowing for improved quantitative metabolic profiling and better assessment of metabolite-protein interactions.

THEORY AND METHODS

The total protein concentration is derived from the common chemical-shift changes-caused by protein-induced bulk magnetic susceptibility (BMS)-measured on well-accessible and exchange-free metabolite resonances. These BMS shifts are simply obtained by external referencing with respect to 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid, sodium salt in a coaxial insert.

RESULTS

Based on blood-plasma data from five volunteers, the estimated accuracy of the BMS method is ≤ 5% with respect and comparable to the 3.8% error of the standard colorimetric, Biuret, method. Valine, alanine, glucose, leucine, and lactate display no exchange-induced shift changes. Their well-accessible signals act as reliable probes for pure protein-induced BMS. The slopes and intercepts of their chemical-shift change versus protein concentration were derived from metabolite mixtures with (fatted) human and bovine albumin acting as blood-plasma mimics.

CONCLUSION

The BMS method, demonstrated on blood plasma, can also be used on other samples containing sufficient protein (> 10 g/L). Also, it allows measurement of the presence and sign of exchange-induced chemical-shift changes.

Rapid LC-MS-based metabolomics method to study the Fusarium infection of barley

Ultra high performance liquid chromatography with quadrupole/time-of-flight mass spectrometry was applied to evaluate the potential of nontarget metabolomic fingerprinting in order to distinguish Fusarium-infected and control barley samples. First, the sample extraction and instrumental conditions were optimized to obtain the broadest possible representation of polar/medium-polar compounds occurring in extracts obtained from barley grain samples. Next, metabolomic fingerprints of extracts obtained from nine barley varieties were acquired under ESI conditions in both positive and negative mode. Each variety of barley was tested in two variants: artificially infected by Fusarium culmorum at the beginning of heading and a control group (no infection). In addition, the dynamics of barley infection development was monitored using this approach. The experimental data were statistically evaluated by principal component analysis, hierarchical clustering analysis, and orthogonal partial least-squares discriminant analysis. The differentiation of barley in response to F. culmorum infection was feasible using this metabolomics-based method. Analysis in positive mode provided a higher number of molecular features as compared to that performed under negative mode setting. However, the analysis in negative mode permitted the detection of deoxynivalenol and deoxynivalenol-3-glucoside considered as resistance-indicator metabolites in barley.

Riboflavin supplementation improves energy metabolism in mice exposed to acute hypoxia

This study investigated the effects of riboflavin on energy metabolism in hypoxic mice. Kunming mice were fed diets containing riboflavin at doses of 6, 12, 24 and 48 mg/kg, respectively for 2 weeks before exposure to a simulated altitude of 6000 m for 8 h. Changes of riboflavin status and energy metabolism were assessed biochemically. Simultaneously, a (1)H nuclear magnetic resonance (NMR) based metabolomic technique was used to track the changes of plasma metabolic profiling. It was found that the content of hepatic riboflavin was decreased and erythrocyte glutathione activation coefficient was elevated significantly under hypoxic condition. Meanwhile, increased plasma pyruvate, lactate, beta-hydroxybutyrate and urea, as well as decreased plasma carnitine were observed. Riboflavin supplementation improved riboflavin status remarkably in hypoxic mice and decreased plasma levels of pyruvate, free fatty acids and beta-hydroxybutyrate significantly. Plasma carnitine was increased in response to riboflavin supplementation. Results obtained from (1)H NMR analysis were basically in line with the data from biochemical assays and remarkable changes in plasma taurine, choline and some other metabolites were also indicated. It was concluded that riboflavin requirement was increased under acute hypoxic condition and riboflavin supplementation was effective in improving energy metabolism in hypoxic mice.

NMR metabolomics profiling of blood plasma mimics shows that medium- and long-chain fatty acids differently release metabolites from human serum albumin

Metabolite profiling by NMR of body fluids is increasingly used to successfully differentiate patients from healthy individuals. Metabolites and their concentrations are direct reporters of body biochemistry. However, in blood plasma the NMR-detected free-metabolite concentrations are also strongly affected by interactions with the abundant plasma proteins, which have as of yet not been considered much in metabolic profiling. We previously reported that many of the common NMR-detected metabolites in blood plasma bind to human serum albumin (HSA) and many are released by fatty acids present in fatted HSA. HSA is the most abundant plasma protein and main transporter of endogenous and exogenous metabolites. Here, we show by NMR how the two most common fatty acids (FAs) in blood plasma - the long-chain FA, stearate (C18:0) and medium-chain FA, myristate (C14:0) - affect metabolite-HSA interaction. Of the set of 18 common NMR-detected metabolites, many are released by stearate and/or myristate, lactate appearing the most strongly affected. Myristate, but not stearate, reduces HSA-binding of phenylalanine and pyruvate. Citrate signals were NMR invisible in the presence of HSA. Only at high myristate-HSA mole ratios 11:1, is citrate sufficiently released to be detected. Finally, we find that limited dilution of blood-plasma mimics releases HSA-bound metabolites, a finding confirmed in real blood plasma samples. Based on these findings, we provide recommendations for NMR experiments for quantitative metabolite profiling.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Focus: a robust workflow for one-dimensional NMR spectral analysis

One-dimensional (1)H NMR represents one of the most commonly used analytical techniques in metabolomic studies. The increase in the number of samples analyzed as well as the technical improvements involving instrumentation and spectral acquisition demand increasingly accurate and efficient high-throughput data processing workflows. We present FOCUS, an integrated and innovative methodology that provides a complete data analysis workflow for one-dimensional NMR-based metabolomics. This tool will allow users to easily obtain a NMR peak feature matrix ready for chemometric analysis as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. The algorithm development has been focused on solving the critical difficulties that appear at each data processing step and that can dramatically affect the quality of the results. As well as method integration, simplicity has been one of the main objectives in FOCUS development, requiring very little user input to perform accurate peak alignment, peak picking, and metabolite identification. The new spectral alignment algorithm, RUNAS, allows peak alignment with no need of a reference spectrum, and therefore, it reduces the bias introduced by other alignment approaches. Spectral alignment has been tested against previous methodologies obtaining substantial improvements in the case of moderate or highly unaligned spectra. Metabolite identification has also been significantly improved, using the positional and correlation peak patterns in contrast to a reference metabolite panel. Furthermore, the complete workflow has been tested using NMR data sets from 60 human urine samples and 120 aqueous liver extracts, reaching a successful identification of 42 metabolites from the two data sets. The open-source software implementation of this methodology is available at http://www.urr.cat/FOCUS.

Simultaneous, untargeted metabolic profiling of polar and nonpolar metabolites by LC-Q-TOF mass spectrometry

At its most ambitious, untargeted metabolomics aims to characterize and quantify all of the metabolites in a given system. Metabolites are often present at a broad range of concentrations and possess diverse physical properties complicating this task. Performing multiple sample extractions, concentrating sample extracts, and using several separation and detection methods are common strategies to overcome these challenges but require a great amount of resources. This protocol describes the untargeted, metabolic profiling of polar and nonpolar metabolites with a single extraction and using a single analytical platform.

Volume determination with two standards allows absolute quantification and improved chemometric analysis of metabolites by NMR from submicroliter samples

The accurate measurement of metabolite concentrations in miniscule biological sample volumes is often desirable, yet it remains challenging. In many cases, the starting analyte volumes are imprecisely known, or not directly measurable, and hence absolute metabolite concentrations are difficult to calculate. Here, we introduce volume determination using two standards (VDTS) as a general quantitative method for the analysis of polar metabolites in submicrolitre samples using ¹H NMR spectroscopy. This approach permits the back calculation of absolute metabolite concentrations from small biological samples of unknown volume. Where small sample volumes are also variable, VDTS can improve multivariate chemometric analysis. In this context, principal component analysis (PCA) yielded more logically consistent and biologically insightful outputs when we used volume-corrected spectra, calculated using VDTS, rather than probabilistic quotient normalization (PQN) of raw spectra. As proof-of-principle, the VDTS-based method and PCA were used to analyze polar metabolites in the hemolymph (blood) extracted from larvae of the very small but widely used genetic model organism Drosophila. This analysis showed that the hemolymph metabolomes of males and females are markedly different when larvae are well fed. However, gender-specific metabolomes tend to converge when larval dietary nutrients are restricted. We discuss the biological implications of these surprising results and compare and contrast them to previous analyses of Drosophila hemolymph and mammalian blood plasma. Together, these findings reveal an interesting and hitherto unknown sexual dimorphism in systemic Drosophila metabolites, clearly warranting further biological investigation. Importantly, the VDTS approach should be adaptable to many different analytical platforms, including mass spectrometry.

Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases

In the recent years, there has been an increase in the number of metabolomic approaches used, in parallel with proteomic and functional genomic studies. The wide variety of chemical types of metabolites available has also accelerated the use of different techniques in the investigation of the metabolome. At present, metabolomics is applied to investigate several human diseases, to improve their diagnosis and prevention, and to design better therapeutic strategies. In addition, metabolomic studies are also being carried out in areas such as toxicology and pharmacology, crop breeding, and plant biotechnology. In this review, we emphasize the use and application of metabolomics in human diseases and plant research to improve human health.

Emerging applications of metabolomics in studying chemopreventive phytochemicals

Phytochemicals from diet and herbal medicines are under intensive investigation for their potential use as chemopreventive agents to block and suppress carcinogenesis. Chemical diversity of phytochemicals, together with complex metabolic interactions between phytochemicals and biological system, can overwhelm the capacity of traditional analytical platforms, and thus pose major challenges in studying chemopreventive phytochemicals. Recent progresses in metabolomics have transformed it to become a robust systems biology tool, suitable for examining both chemical and biochemical events that contribute to the cancer prevention activities of plant preparations or their bioactive components. This review aims to discuss the technical platform of metabolomics and its existing and potential applications in chemoprevention research, including identifying bioactive phytochemicals in plant extracts, monitoring phytochemical exposure in humans, elucidating biotransformation pathways of phytochemicals, and characterizing the effects of phytochemicals on endogenous metabolism and cancer metabolism.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Getting your peaks in line: a review of alignment methods for NMR spectral data

One of the most significant challenges in the comparative analysis of Nuclear Magnetic Resonance (NMR) metabolome profiles is the occurrence of shifts between peaks across different spectra, for example caused by fluctuations in pH, temperature, instrument factors and ion content. Proper alignment of spectral peaks is therefore often a crucial preprocessing step prior to downstream quantitative analysis. Various alignment methods have been developed specifically for this purpose. Other methods were originally developed to align other data types (GC, LC, SELDI-MS, etc.), but can also be applied to NMR data. This review discusses the available methods, as well as related problems such as reference determination or the evaluation of alignment quality. We present a generic alignment framework that allows for comparison and classification of different alignment approaches according to their algorithmic principles, and we discuss their performance.

Metabolomic study of the fever model induced by baker's yeast and the antipyretic effects of aspirin in rats using nuclear magnetic resonance and gas chromatography-mass spectrometry

A metabolomic investigation of baker's yeast-induced fever in rats was carried out. Plasma derived from Sprague-Dawley rats treated by subcutaneous administration of 20% (w/v) baker's yeast was analyzed using gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR). Statistical data analysis using t-test and orthogonal partial least-squares discriminant analysis revealed many significant changes in the metabolic data in the plasma of the fever group. Clear separation was achieved between the fever and control groups. Seventeen marked metabolites were found in the fever group. The metabolites, which include amino acids, carbohydrate, organic acids, and fatty acids, mostly contributed to the discrimination of plasma samples from the control and fever groups. These results suggested that fever may involve in the perturbation of amino acid metabolism coupled with energy metabolism, lipid metabolism, and glycometabolism. After determining the antipyretic effects of aspirin on the fever group, four metabolites in the fever rat plasma were found to be signally regulated and recognized as potential biomarkers, including 3-hydroxybutyric acid, gamma-aminobutyric acid, glucose, and linoleic acid. The metabolic relationships that possibly exist between these potential biomarkers were speculated, and the mechanism of baker's yeast-induced fever was illustrated based on the metabolic relationships. This study found that metabolomic approaches such as GC-MS and NMR could be used as potential powerful tools to investigate the biochemical changes and mechanisms in certain pathological states at the metabolism level.

Influence of Freezing and Storage Procedure on Human Urine Samples in NMR-Based Metabolomics

It is consensus in the metabolomics community that standardized protocols should be followed for sample handling, storage and analysis, as it is of utmost importance to maintain constant measurement conditions to identify subtle biological differences. The aim of this work, therefore, was to systematically investigate the influence of freezing procedures and storage temperatures and their effect on NMR spectra as a potentially disturbing aspect for NMR-based metabolomics studies. Urine samples were collected from two healthy volunteers, centrifuged and divided into aliquots. Urine aliquots were frozen either at -20 °C, on dry ice, at -80 °C or in liquid nitrogen and then stored at -20 °C, -80 °C or in liquid nitrogen vapor phase for 1-5 weeks before NMR analysis. Results show spectral changes depending on the freezing procedure, with samples frozen on dry ice showing the largest deviations. The effect was found to be based on pH differences, which were caused by variations in CO2 concentrations introduced by the freezing procedure. Thus, we recommend that urine samples should be frozen at -20 °C and transferred to lower storage temperatures within one week and that freezing procedures should be part of the publication protocol.