Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy

One of the greatest challenges in metabolomics is the rapid and unambiguous identification and quantification of metabolites in a biological sample. Although one-dimensional (1D) proton nuclear magnetic resonance (NMR) spectra can be acquired rapidly, they are complicated by severe peak overlap that can significantly hinder the automated identification and quantification of metabolites. Furthermore, it is currently not reasonable to assume that NMR spectra of pure metabolites are available a priori for every metabolite in a biological sample. In this paper we develop and report on tests of methods that assist in the automatic identification of metabolites using proton two-dimensional (2D) correlation spectroscopy (COSY) NMR. Given a database of 2D COSY spectra for the metabolites of interest, our methods provide a list sorted by a heuristic likelihood of the metabolites present in a sample that has been analyzed using 2D COSY NMR. Our models attempt to correct the displacement of the peaks that can occur from one sample to the next, due to pH, temperature and matrix effects, using a statistical and chemical model. The correction of one peak can result in an implied correction of others due to spin–spin coupling. Furthermore, these displacements are not independent: they depend on the relative position of functional groups in the molecule. We report experimental results using defined mixtures of amino acids as well as real complex biological samples that demonstrate that our methods can be very effective at automatically and rapidly identifying metabolites.     

Metabolic Biomarker Panels of Response to Fusarium Head Blight Infection in Different Wheat Varieties

Metabolic changes in spikelets of wheat varieties FL62R1, Stettler, Muchmore and Sumai3 following Fusarium graminearum infection were explored using NMR analysis. Extensive 1D and 2D ¹H NMR measurements provided information for detailed metabolite assignment and quantification leading to possible metabolic markers discriminating resistance level in wheat subtypes. In addition, metabolic changes that are observed in all studied varieties as well as wheat variety specific changes have been determined and discussed. A new method for metabolite quantification from NMR data that automatically aligns spectra of standards and samples prior to quantification using multivariate linear regression optimization of spectra of assigned metabolites to samples’ 1D spectra is described and utilized. Fusarium infection-induced metabolic changes in different wheat varieties are discussed in the context of metabolic network and resistance.     

2D NMR metabonomic analysis: a novel method for automated peak alignment

MOTIVATION: Comparative metabolic profiling by nuclear magnetic resonance (NMR) is showing increasing promise for identifying inter-individual differences to drug response. Two dimensional (2D) (1)H (13)C NMR can reduce spectral overlap, a common problem of 1D (1)H NMR. However, the peak alignment tools for 1D NMR spectra are not well suited for 2D NMR. An automated and statistically robust method for aligning 2D NMR peaks is required to enable comparative metabonomic analysis using 2D NMR. RESULTS: A novel statistical method was developed to align NMR peaks that represent the same chemical groups across multiple 2D NMR spectra. The degree of local pattern match among peaks in different spectra is assessed using a similarity measure, and a heuristic algorithm maximizes the similarity measure for peaks across the whole spectrum. This peak alignment method was used to align peaks in 2D NMR spectra of endogenous metabolites in liver extracts obtained from four inbred mouse strains in the study of acetaminophen-induced liver toxicity. This automated alignment method was validated by manual examination of the top 50 peaks as ranked by signal intensity. Manual inspection of 1872 peaks in 39 different spectra demonstrated that the automated algorithm correctly aligned 1810 (96.7%) peaks. AVAILABILITY: Algorithm is available upon request.     

1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues

Metabolite fingerprinting provides a powerful method for discriminating between biological samples on the basis of differences in metabolism caused by such factors as growth conditions, developmental stage or genotype. This protocol describes a technique for acquiring metabolite fingerprints from samples of plant origin. The preferred method involves freezing the tissue rapidly to stop metabolism, extracting soluble metabolites using perchloric acid (HClO4) and then obtaining a fingerprint of the metabolic composition of the sample using 1D 1H NMR spectroscopy. The spectral fingerprints of multiple samples may be analyzed using either unsupervised or supervised multivariate statistical methods, and these approaches are illustrated with data obtained from the developing seeds of two genotypes of sunflower (Helianthus annuus). Preparation of plant extracts for analysis takes 2-3 d, but multiple samples can be processed in parallel and subsequent acquisition of NMR spectra takes approximately 30 min per sample, allowing 24-48 samples to be analyzed in a week.     

Metabolite fingerprinting and profiling in plants using NMR

Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned 1H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the 1H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of 1H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.     

Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics

Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method. C. Y. Lin and H. Wu contributed equally.     

13C-NMR study of hypoglycemia-induced glycolytic changes in embryonic mouse heart

BACKGROUND: Glucose metabolites can be detected in embryonic mouse tissues using 13C-NMR spectroscopy. The advantage of this method is in its chemical specificity and the ability to follow metabolic changes. METHODS: In this study, CD-1 mice were mated and embryos excised on gestational day (GD) 10.5 (plug = GD 0.5). Hearts were isolated and cultured in 150 mg/dl glucose (normoglycemic medium) or 40 mg/dl glucose (hypoglycemic medium) for 6 hr. 13C-labeled glucose comprised 62%-64% of total glucose in the culture medium. Pre- and postculture media were treated with deuterated water (D2O), and 13C spectra were obtained using a Bruker Avance 500 MHz spectrometer operating at 11.744 tesla (125.7 MHz for 13C). NMR spectra demonstrated resonances for 13C-glucose in preculture normoglycemic and hypoglycemic media. Postculture spectra for normoglycemic and hypoglycemic media demonstrated 13C-glucose signals as well as a signal for 13C-lactate. Area under the curve (AUC) was measured for the [1-(13)C-glucose] resonance from preculture media and the [3-(13)C-lactate] resonance from postculture media. The ratios of AUC for postculture [3-(13)C-lactate] to preculture [1-(13)C-glucose] were calculated and found to be higher in hypoglycemic than in normoglycemic media. RESULTS: Our results confirm earlier findings using radiolabeled substrates and suggest that 13C-NMR spectroscopy can be used to study glucose metabolism in isolated embryonic hearts exposed to hypoglycemia. CONCLUSIONS: NMR effectively measures glucose and its metabolite, lactate, in the same spectrum and thus determines metabolic flux in the isolated embryonic heart after exposure to hypoglycemia and normoglycemia. This method could evaluate glucose metabolism in embryonic tissues following other teratogenic exposures.     

Study of metabolite composition of eccrine sweat from healthy male and female human subjects by 1H NMR spectroscopy

The aim of the study was to evaluate metabolite variability in human eccrine sweat using a metabonomics based approach. Eccrine sweat is a dilute electrolyte solution whose primary function is to control body temperature via evaporative cooling. Although the composition of sweat is primarily water, previous studies have shown that a diverse array of organic and inorganic compounds are also present. Human eccrine sweat samples from 30 female and 30 male subjects were analysed using high-resolution ¹H nuclear magnetic resonance (NMR) spectroscopy in conjunction with statistical pattern recognition. High-resolution ¹H NMR spectroscopy produced spectra of the sweat samples that readily identified and quantified many different metabolites. The major metabolite classes found to be present were lactate, amino acids and lipids, with lactate being by far the most dominant metabolite found in all samples. Principal Components Analysis, Principal Components-Discriminant Analysis and Partial Least Squares-Discriminant Analysis of the eccrine sweat samples, revealed no significant differences in metabolite composition and concentration between female and male subjects. Also, the variation between subjects did not appear to be correlated with any other clinical information provided by the subjects. Overall, the spectra data set demonstrates the large physiological variability in terms of number of metabolites present and concentrations between subjects i.e. human eccrine sweat samples exhibit a high degree of inter-individual variability.     

24,26-Dihydroxyvitamin D2: a unique physiological metabolite of vitamin D2

A new vitamin D2 metabolite, 24,26-dihydroxyvitamin D2, has been detected in the plasma of rats fed physiologic amounts of vitamin D2. The identity of the new metabolite (isolated from cow plasma) was established by ultraviolet absorbance, mass spectroscopy, chemical reactivity, and NMR spectroscopy. Among these, the mass spectrum was unique for the presence of a peak at M-48 that was attributed to an intramolecular rearrangement involving both the C-24 and C-26 hydroxyl groups. A 300-MHz 1H NMR spectrum of 40 micrograms of metabolite indicated a downfield shift of the C-28 methyl group signal to delta 1.30 and a multiplet at delta 3.66 corresponding to the hydroxylated C-26 methyl group. We determined that the formation of 24,26-dihydroxyvitamin D2 represented a major pathway for further metabolism of 24-hydroxyvitamin D2 in rats, exceeding the formation of 24,25-dihydroxyvitamin D2. Standard bioassays revealed that 24,26-dihydroxyvitamin D2 possessed very little biological activity and most likely represents a deactivation pathway for 24-hydroxyvitamin D2.     

High resolution deuterium NMR studies of bacterial metabolism

High resolution deuterium NMR spectra were obtained from suspensions of five bacterial strains: Escherichia coli, Clostridium perfringens, Klebsiella pneumoniae, Proteus mirabilis, and Staphylococcus aureus. Deuterium-labeled D-glucose at C-1, C-2, and C-6 was used to monitor dynamically anaerobic metabolism. The flux of glucose through the various bacterial metabolic pathways could be determined by following the disappearance of glucose and the appearance of the major end products in the 2H NMR spectrum. The presence of both labeled and unlabeled metabolites could be detected using 1H NMR spectroscopy since the proton resonances in the labeled species are shifted upfield due to an isotopic chemical shift effect. The 1H-1H scalar coupling observed in both the 2H and 1H NMR spectra was used to assign definitively the resonances of labeled species. An increase in the intensity of natural abundance deuterium signal of water can be used to monitor pathways in which a deuteron is lost from the labeled metabolite. The steps in which label loss can occur are outlined, and the influence these processes have on the ability of 2H NMR spectroscopy to monitor metabolism are assessed.     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.     

基于核磁共振波谱的盐芥盐胁迫代谢组学分析

以甲醇/水(1∶1)作为溶剂,利用高分辨核磁共振氢谱分析了盐生模式植物盐芥(Thellungiella salsuginea)代谢组对盐胁迫的响应。根据1H核磁共振(NMR)波谱,在盐芥莲座叶中准确鉴定出23种代谢产物,包括11种氨基酸、4种糖类、6种有机酸和2种其他代谢产物。主成分分析表明,150、300 mmol/L NaCl处理盐芥的代谢组与对照均有显著差异(P<0.05),两种浓度的NaCl处理对盐芥代谢组的影响也不相同。盐胁迫处理以后,盐芥23种代谢产物含量均发生显著变化,除天冬氨酸、延胡索酸受盐胁迫诱导含量下降以外,其余代谢物含量均不同程度升高。这些代谢物主要参与了糖类代谢途径、氨基酸合成途径、三羧酸循环和甜菜碱合成途径,这些代谢途径在盐芥响应盐胁迫过程中有重要作用。     

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS

Urinary metabolic perturbations associated with acute and chronic acetaminophen-induced hepatotoxicity were investigated using nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography/mass spectrometry (UPLC/MS) metabonomics approaches to determine biomarkers of hepatotoxicity. Acute and chronic doses of acetaminophen (APAP) were administered to male Sprague-Dawley rats. NMR and UPLC/MS were able to detect both drug metabolites and endogenous metabolites simultaneously. The principal component analysis (PCA) of NMR or UPLC/MS spectra showed that metabolic changes observed in both acute and chronic dosing of acetaminophen were similar. Histopathology and clinical chemistry studies were performed and correlated well with the PCA analysis and magnitude of metabolite changes. Depletion of antioxidants (e.g. ferulic acid), trigonelline, S-adenosyl-l-methionine, and energy-related metabolites indicated that oxidative stress was caused by acute and chronic acetaminophen administration. Similar patterns of metabolic changes in response to acute or chronic dosing suggest similar detoxification and recovery mechanisms following APAP administration.     

Rapid identification of Candida species by using nuclear magnetic resonance spectroscopy and a statistical classification strategy

Nuclear magnetic resonance (NMR) spectra were acquired from suspensions of clinically important yeast species of the genus Candida to characterize the relationship between metabolite profiles and species identification. Major metabolites were identified by using two-dimensional correlation NMR spectroscopy. One-dimensional proton NMR spectra were analyzed by using a staged statistical classification strategy. Analysis of NMR spectra from 442 isolates of Candida albicans, C. glabrata, C. krusei, C. parapsilosis, and C. tropicalis resulted in rapid, accurate identification when compared with conventional and DNA-based identification. Spectral regions used for the classification of the five yeast species revealed species-specific differences in relative amounts of lipids, trehalose, polyols, and other metabolites. Isolates of C. parapsilosis and C. glabrata with unusual PCR fingerprinting patterns also generated atypical NMR spectra, suggesting the possibility of intraspecies discontinuity. We conclude that NMR spectroscopy combined with a statistical classification strategy is a rapid, nondestructive, and potentially valuable method for identification and chemotaxonomic characterization that may be broadly applicable to fungi and other microorganisms.     

Metabolic differentiation of Arabidopsis treated with methyl jasmonate using nuclear magnetic resonance spectroscopy

NMR spectroscopy combined with principal component analysis was applied to Arabidopsis thaliana treated with methyl jasmonate in order to obtain macroscopic metabolic changes caused by the treatment. As the first step several chromatographic and NMR spectroscopic techniques were utilized to identify metabolites of Arabidopsis. Sephadex LH-20 showed a high efficiency in the separation of phenolic metabolites in the plant. For identification of minor metabolites two-dimensional J-resolved NMR technique was directly applied to the plant extract and results in a number of elucidation of the metabolites of which signals overlap in ¹H NMR spectra. The chemical structure of the identified metabolites were confirmed by various two-dimensional NMR spectroscopy including correlated spectroscopy, heteronuclear single quantum coherence, and heternuclear multiple bond correlation. As next step, a statistical approach, principal component analysis based on projected J-resolved NMR spectra was performed for metabolic alteration of methyl jasmonate-treated Arabidopsis. The results show that methyl jasmonate caused an increase of flavonoids, fumaric acid, sinapoyl malate, sinigrin, tryptophan, valine, threonine, and alanine and a decrease of malic acid, feruloyl malate, glutamine, and carbohydrates after 24 h treatment.     

A new meta-ANOVA approach for synthesizing information under signal-heterogeneity setting with application to nuclear magnetic resonance spectroscopic data

A new synthesizing statistical methodology is proposed to resolve issues of signal-heterogeneity in data sets collected through high-resolution ¹H nuclear magnetic resonance (NMR) spectroscopy. This signal-heterogeneity is typically caused by subjective operations for processing spectral profiles and measuring peak areas, non-homogeneous biological phases of experimental subjects, and variations of systems in multi-center. All these causes are likely to simultaneously impact signals of metabolic changes and their precision in a nonlinear fashion. As a combined effect, signal-heterogeneity chiefly manifests through non-homomorphic patterns of standardized treatment mean deviations spanning all experiments, and makes most remedial statistical models with linearity structure invalid. By avoiding a huge and very complex model, we develop a simple meta-ANOVA approach to synthesize many one-way-layout ANOVA analyses from individual experiments. A scale-invariant F-ratio statistic is taken as the summarizing sufficient statistic of a non-centrality parameter that supposedly captures the information about metabolic change from each experiment. Then a joint-likelihood function of a common non-centrality is constructed as the basis for maximum likelihood estimation and Chi-square likelihood ratio testing for statistical inference. We apply the meta-ANOVA to detect metabolic changes of three metabolites identified through pattern recognition on NMR spectral profiles obtained from muscle and liver tissues. We also detect effect differences among different treatments via meta-ANOVA multiple comparison.     

Improved methods for the acquisition and interpretation of NMR metabolomic data

The high spectral congestion typically observed in one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectra of tissue extracts and biofluids limits the metabolic information that can be extracted. This study evaluates the application of two-dimensional J-resolved (JRES) spectroscopy for metabolomics, which can provide proton-decoupled projected 1D spectra (p-JRES). This approach is illustrated by an investigation of embryogenesis in Japanese medaka (Oryzias latipes), an established fish model for developmental toxicology. When combined with optimized spectral pre-processing,(2) including a 0.005-ppm bin width for data segmentation and a logarithmic transformation, the reduced congestion in the p-JRES spectra increases the likelihood that a specific metabolite can be accurately integrated and thus increases the extractable information content of the spectra. Principal components analysis of the p-JRES spectra reveals the concept of a developmental trajectory that summarizes the changes in the NMR-visible metabolome throughout medaka embryogenesis. Advantages and potential disadvantages of the p-JRES approach are discussed.     

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Background

Metabolic phenotyping has become an important ‘bird''s-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of ‘top-down’ chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems.

Methodology/Principal Findings

The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 ¹H and ¹³C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each ¹³C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient ¹³C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts.

Conclusions/Significance

Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of ¹³C atoms of given metabolites on development-dependent changes in the 56 identified ¹³C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism.