Metabolite Identification in NMR-based Metabolomics

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Metabolite Identification in NMR-based Metabolomics Santosh Kumar Bharti1,* and Raja Roy2,** 1

Division of Cancer Imaging, Research, Russell H. Morgan Department of Radiology & Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD-21205, USA; 2Centre of Biomedical Research, SGPGIMS Campus, Lucknow-226014, Uttar Pradesh, India Abstract: To achieve goals in metabolomics investigations, it is necessary to produce a comprehensive metabolic profiling from biological samples. Identification of metabolites is one of the important steps in metabolomics studies and the conclusion drawn from such studies depends on how exactly the metabolites are identified. NMR is one of the most selective analytical techniques which offers structural information of molecules. But, due to complex biological sample matrix, metabolic identification step needs application of advanced NMR techniques and analytical strategies for better accuracy. This review covers the analytical methods and strategies used for identification of metabolites in NMR-based metabolomics. The specific problems and troubleshoots associated with identification of metabolites in biological samples are discussed in details.

Keywords: NMR spectroscopy, spiking, metabolomics, metabolites assignments, COSY, HSQC INTRODUCTION Metabolomics is defined as the systematic identification and quantitation of multi-parametric response under pathophysiological stimuli or genetic modifications [1]. Metabolomics is one of the fast growing fields in the era of "omics" which deals with profiling of metabolites in the cells, bio-fluids, tissues, organs, etc. using various analytical methods. Metabolites are the biochemical components involved in metabolism, that either act as intermediates, precursors, changing or are incorporated into biochemical reactions along metabolic pathways. Accumulations of metabolites show the end point of the response of an organism to a stimulus. Metabolome gives the total information about the metabolites produced by a single organism. Similar to the proteome, the metabolome is closely coupled to an organism’s genome and influenced by transcribed genes, and other surrounding cell materials [2]. Metabolic alterations in cells, bio-fluids, tissues, etc. are induced in response to environmental or developmental stimuli, biotic stress, pathological, or genetic mutation/change. Such metabolic alterations result in change in the steady-state concentration of intermediate pathway metabolites and in the end, metabolic products accumulate. To estimate such changes, concentration of metabolites must be monitored both spatially and with respect to time. Qualitative and quantitative estimation and total integration of metabolites give information about genome and proteome functions. Metabolomics is now a fast growing ‘omics’ field and is applied to a variety of disciplines including chemistry, biotechnology, plant science, medicine, biology, environmental science as major areas. *Address correspondence to these authors at the Division of Cancer Imaging, Research, Russell H. Morgan Department of Radiology & Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD21205, USA; Tel: +1 410 502 3555; E-mail: [email protected]; And **Centre of Biomedical Research, SGPGIMS Campus, Lucknow-226014, Uttar Pradesh, India; Tel: +91-522-2668215; E-mail: [email protected]

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The common analytical techniques used in the metabolomics include MS, LC-MS, GC-MS, LC-NMR and NMR spectroscopy. The advantage of using a particular technique will depend upon its sensitivity, feasibility, sample processing requirement and the maximum information obtained from one step analysis. NMR spectroscopy is one of the most widely used analytical techniques in metabolomics due to its specific advantages such as single step analysis, qNMR analysis, ease of sample preparation, non-destructive nature, high robustness, reproducibility and structural information of metabolites. A routine metabolomics study consists of five major steps which include sample preparation, identification and quantification of metabolites by analytical techniques, statistical data analysis and biological interpretation of the resulting data. The biological interpretation, concluding hypothesis and later steps, depend upon how accurately the metabolites in the biological sample are identified and quantified. Sometimes, metabolic assignments are not required in metabolic fingerprinting applications such as product control, treatment monitoring, comparative studies, etc. But to understand the biological mechanism, it is important to identify and quantify maximum number of metabolites present in biological samples. Any error in the identification and quantification of the metabolites will lead to wrong biological interpretation. So, the data generated from the analysis of the biological samples should be accurate. NMR is one of the most selective analytical techniques which give unambiguous structural information of molecule. However, because of complex biological samples matrix, metabolic identification step needs application of advanced NMR techniques and analytical strategies for better accuracy. The major problems that arise in metabolite identification using NMR in biological samples are high spectral crowding, presence of macromolecules, molecular interaction, dynamic range, enormous solvent concentration, dynamic range, sensitivity, etc. Recent © 2014 Bentham Science Publishers

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developments and analytical methods have shown promising results in correct identification of metabolites and in resolving the associated problems. Specific applications of these methods and analytical strategies to resolve the problems associated with metabolic assignments, has been described in this review. Increasing applications of NMR-based metabolomics in medicine, microbiology, toxicology, cancer biology, plant biology etc. demanded the developments of comprehensive methods and protocols for precise and accurate metabolomics studies. Bechonert et al. have published comprehensive protocols for sample preparation and analysis of biological samples like serum, plasma, tissues extract, CSF, etc. [3] and HR-MAS NMR metabolic profiling of intact cells/tissues [4]. Recently, Bharti and Roy published a protocol for quantification of metabolites in biological samples, pharmaceuticals and natural product extracts [5]. In this review, an attempt is made to systematically compile the NMR experimental methods and analytical strategies for identification of metabolites in metabolomics. BASIC NMR EXPERIMENTAL METHODS FOR IDENTIFICATION OF METABOLITES Biological samples are a complex mixture of hundreds to thousands of metabolites in varying concentrations. Therefore, characterization of metabolites in such biological mixture, for example serum, urine CSF, pathological fluids and tissue extracts are different from the assignments of compounds in organic synthesis or those isolated from natural products. Identification of a new molecule without having its standard spectrum is a challenging task in metabolomics. It requires extensive analytical efforts and applications of advanced 1D and 2D and NMR techniques. Several one dimensional (1D) and two dimensional (2D) homo and hetero nucleus NMR techniques are used in combination for the identification of metabolites in biological samples. Some of these common techniques used for identification of metabolites in biological samples are discussed below. 1D NMR TECHNIQUES Single pulse experiment: The single pulse experiment consists of a relaxation delay and 90° hard pulse followed by acquisition time. Native body fluid samples having water in molar concentration give rise to a very intense peak in the spectrum that interferes with observation of the metabolites in mM concentration. This is known as a dynamic range problem in NMR spectroscopy. To overcome this, solvent suppression NMR techniques are required. Several solvent suppression techniques have been developed and have been used for the suppression of solvent signal in metabolomics samples. These include pre-saturation, WATERGATE [6, 7], WET [8], Excitation Sculpting [9], PURGE [10], etc. Presaturation is one of the simplest and most commonly used methods for solvent suppression. In this technique, a selective low power pulse on solvent frequency is applied to saturate water signal just before 90° hard pulse. A single pulse NMR spectrum of the samples is the first and the most important basic step in metabolic assignments and quantitation of metabolites, and hence it should be recorded at well opti-

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mized experimental parameters for better accuracy. Information such as multiplicity, coupling constant, integral values, number of signals, relative intensity, etc. can be drawn from single pulse NMR spectra for initial screening assignments of metabolites [11]. The major problem associated with presaturation technique is, suppression of water exchangeable protons (-NH, -SH, -OH) signals. To measure such signals, WATERGATE and Excitation Sculpting are preferred over presaturation. Homo-nuclear decoupling: Homo-nuclear decoupling is an application of low power radiation at one set of frequencies and observing its effects on the other coupling partners within the spectrum of the same nuclei i.e. decoupling nucleus and observing nucleus both being the same. Proton homo-nuclear decoupling is applied for simplification of NMR spectra, complex coupling pattern and identification of coupling partner in NMR spectra [12]. Researchers use homonuclear decoupling experiments to identify closely related fatty acids. For examples, Annarao et. al. differentiated linoleic acids (18:2n-6) from linolenic acids (18:3n-6) in Jatropha curcas developing seeds using homonuclear decoupling experiments [13]. Hetero-nuclear decoupling: Carbon Decoupled Proton NMR experiments; A metabolomics sample consists of a mixture comprising of hundreds of molecules with varying concentrations. Since, carbon nuclei (13C) have spin nuclei ½, it couples to 1H nuclei. The resulting heteronuclear couplings give rise to two additional signals within 50-100 Hz range, on both sides of the main proton signal that are called as 13C satellite (Fig. 1), highlighted by ‘*’ Notation). 13C satellites of molecules with very high concentration create anomalies in assignment of weak 1H signals of other metabolites present nearby. Lactate, creatinine, acetate, alanine, succinate, etc. raise such problems in assignment of NMR metabolomics spectra [14] (Fig. 1). This problem can be resolved by applying broadband decoupling of 13C nuclei which removes the 13C satellite from proton NMR spectra [15]. Proton Decoupled Carbon NMR experiments: 13C NMR application is limited due to low sensitivity and natural abundance of 13C nuclei [16], but recent developments in CryoProbe technologies provide an option for carbon (13C) NMR-based metabolomics profiling on biological samples [16]. Protons-Carbon (1H-13C) coupling results in splitting of the 13C resonances making carbon NMR spectra more crowded. Proton decoupling sequences are used to remove the hetero nuclear couplings resulting in simplified 13C spectra and increased sensitivity. 13C NMR with 1H decoupling can be used for profiling metabolomics samples having high metabolic concentration. Carbon NMR spectrum provides better resolution as compared to proton because of the wide 13 C chemical shift range which helps in identification of a maximum number of metabolites [16, 17]. 13

C-DEPT (Distortionless Enhancement by Polarization Transfer): The DEPT sequence distinguishes -CH-, -CH2-, and -CH3 groups in molecules. There are three versions of the DEPT experiment, DEPT-45, DEPT-90, and DEPT-135 classified, based on the pulse width (45°, 90°, and 135°, respectively) of the final 1H pulse in the sequence. Only DEPT-90 and DEPT-135 are sufficient to perform spectral

NMR Spectroscopic Techniques for Characterization of Metabolites in Biological Samples

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Fig. (1). Proton NMR spectrum (13C coupled) of infected human CSF sample expanded from 0.8 to 1.5 ppm. Arrow [*] highlighting the 13C satellite signals of lactate having very high concentration as compared to other metabolites. Intensity of lactate 13C satellite signal is comparable to proton signals of some other metabolites. Such a condition creates confusion for the analyst during metabolic assignments.

editing. The quaternary carbons will be absent in the DEPT spectra whereas, methine carbons (CH) will be positive in both DEPT-90 and DEPT-135. Methylene carbons (CH2 ) will be negative in the DEPT-135 but absent in the DEPT90; while methyl carbons (CH3) will be positive in the DEPT-135 and absent in the DEPT-90 [18]. Biological fluids need more scans/high concentration, high field magnets and CryoProbe technology for obtaining meaningful DEPT spectra. DEPT spectra is extensively used for structure determination of natural products, drug molecules, etc. [19]. DEPT sequence is mostly used in plant metabolomics for the identification of naturally abundant secondary metabolites in plant extracts. Hahn-Echo/Hahn Spin Echo sequence: Hahn Echo sequence is a basic spin echo sequence consisting of a 90° pulse-delay-180° pulse followed by acquisition. It is the easiest method for determining the multiplicity of metabolites resonances in a complex or highly crowded proton 1D NMR spectra. Hahn-Echo sequence has a potential utility in in-vivo metabolic profiling in biomedical MRS studies [20]. In the Hahn-Echo NMR spectrum, doublets and quartets get inverted and singlets and triplets remain in positive intensity direction. Hence, overlapping of singlet, doublet, triplet, quartet etc. can be easily determined by Hahn echo sequence. It has been used by many researchers for assigning metabolites in biological fluids [21]. CPMG (Carr Purcell Meiboom Gill Sequence): CPMG pulse sequence was initially developed to remove the magnetic field inhomogeneity and measurements of spin-spin (T2, transverse relaxation) relaxation by introducing a 180° pulse before acquisition to single pulse sequence [22]. T2 measurement involves the variation of echo-time (2τ*n) and monitoring the attenuation in the signal intensity due to spinspin relaxation. If the T2 of molecules is short, faster decay in the intensity will be observed [23]. High molecular weight molecules (e.g. proteins, lipoproteins and lipids) experience a short T2 relaxation time. This phenomenon is exploited in metabolomics for removing broad signals of high-molecular-

weight compounds and retaining sharp signals from small molecules [23, 24]. CPMG is most commonly applied to serum, seminal fluids, CSF, pericardial fluid, ascitic fluids, amniotic fluid, etc. for the suppression of broad signals that arise due to the presence of proteins, lipoproteins and lipids [24]. CPMG also applies to HR-MAS NMR spectroscopy of cancer tissues, animal tissues and plant tissues to remove the broad signal(s) and to get better baseline as compared to single pulse sequence [4]. Comparison of CPMG and single pulse sequence NMR spectra of serum sample is shown by Bharti et al. demonstrating the removal of protein broad signal with better baseline [23]. Several small molecules present in low concentration are overlapped by the broad resonances of proteins and lipids. CPMG pulse sequence with water pre-saturation with improved receiver gain and higher number of scans shows clear signals of such molecules. Some of the molecules which bind to the proteins may get suppressed by CPMG and therefore, the analyst should be careful and use a de-binder to remove the binding or interaction [25]. TWO DIMENSIONAL NMR TECHNIQUES JRES (J-Resolved Spectroscopy): JRES is one of the most important experiments used for assignment of metabolites on the basis of their coupling constants and simplification of proton spectra using 1D projection calculations [26]. The 2D-JRES spectra provides chemical shift in the first dimension and J-value in the second dimension. Therefore, J-value can be measured with a higher accuracy for overlapping resonances in chemical shift dimension [24]. Furthermore, sum of projections of JRES spectrum provides a proton-decoupled 1D spectrum that can be further explored for routine metabolomics analysis. DQF-COSY (Double Quantum Filtered Correlation Spectroscopy): Two dimensional COSY improves the accuracy of metabolic identifications by extending the chemical shift information into second frequency dimension. In addition to spectral resolution, COSY NMR helps in the identifi-


cation of spin network associated with a metabolites through J-coupling and consequently helps in identification of metabolites. Methyl signals from lactate and threonine are undistinguishable at 1.33 in one dimensional spectrum. However, cross peaks in COSY spectrum for lactate (1.33, 4.12 ppm) and threonine (1.33, 4.25 ppm) help in their identification and contribution at 1.33 resonance peaks. DQF-COSY is preferred over normal COSY experiments because the intense peaks from the singlets in the DQF-COSY get suppressed and resolution adjacent to diagonal peaks is improved. The drawback of DQF-COSY is reduced sensitivity, thus requiring longer experimental time. Selective COSY (1D COSY): Selective COSY can be applied when limited information is required. It gives similar information as 2D COSY, but is selectively applied on specific resonances in order to find out its coupling partners. Selective COSY consists of a selective pulse, evolution period and finally 90° hard pulse followed by acquisition. Selective COSY experiments are used for detection of metabolites in biological fluids or spin systems in a molecule without pre-saturation of water and lipid components [27]. TOCSY (Total Correlation Spectroscopy): It is also known as Homo-nuclear Hartmann-Hahn spectroscopy (HOHAHA), and is used to detect the fragment(s) of molecules (entire spin system) connected via J-coupling. In TOCSY, magnetization is dispersed over the entire spin system (3rd or 4th spin) of molecules by successive scalar coupling. Therefore, not only the signals that appear in a COSY spectrum but also additional signals which originate from the interaction of all protons of a spin system are also observed. In biological samples, along with COSY, TOCSY is used for further refinement of the assignments. Selective TOCSY: Selective 1D TOCSY is a variant of 2D TOCSY, and is applied on specific resonance for the detection of entire spin system connected with J-coupling. The advantage of 1D selective TOCSY is that its acquisition time is less as compared to its 2D version. However, this experiment gives total correlation for only a few spins. It is applied for the semi-quantitative analysis of biological fluids, assignment of metabolites in biological fluids, large organic molecules, and analysis of minor components in the presence of solvent(s) or high concentration compounds in a mixture without pre-saturation etc. [28] Selective 1D COSY and TOCSY experiments are best used to find out the spins correlation quickly for a limited number of metabolites. HSQC (Hetero-nuclear Single Quantum Coherence): HSQC is one of the most important and frequently used hetero-nuclear 2D experiments for metabolic profiling. HSQC correlates with the chemical shift of proton attached to hetero-nuclei such as 13C, 15N etc. via large one-bond heteronuclear coupling (JCH). Chemical shift of one nucleus - usually 1H is detected in the directly measured dimension, while chemical shift of second nucleus (13C) is recorded in the indirect dimension [18]. One of the advantages of 1H-13C HSQC for metabolomics samples is the high dispersion of chemical shift in second dimension because of the wide range of 13C chemical shift. The second advantage of the HSQC is better sensitivity as compared to other heteronuclear experiments. Therefore, it can easily be applied frequently on samples in low concentration [29].

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HMBC (Hetero-nuclear Multiple Bond Correlation): HMBC experiments correlate with the chemical shift of proton to carbon nuclei separated from two or three chemical bonds. In HMBC NMR spectra, each proton (1H) shows several cross peaks for each coupled 13C nuclei. HMBC is mainly used for the assignments of quaternary and carbonyl carbons as well as for arrangement of spin systems. A carbonyl carbon can be easily recognized by HMBC because it shows correlations with neighboring 1H nuclei. HMBC experiment significantly assists in complete structure determination of new molecules present in metabolomics samples such as plant extracts, isolates etc. [11]. The application of HMBC to metabolomics is limited due to its low sensitivity and enhanced experimental time. However, its application is reported in HR-MAS NMR and plant extracts, isolate samples having high metabolites concentration [30]. HSQC-TOCSY: The 2D HSQC-TOCSY experiment is a hybrid inverse experiment consisting of an initial HSQC pulse train followed by a TOCSY mixing sequence [18]. TOCSY mixing sequence relays the original proton-carbon correlation peak onto neighboring protons within the same spin-system, thus producing a 13C-dispersed TOCSY spectrum. The HSQC-TOCSY spectrum correlates the chemical shifts of heteronucleus13C (indirect dimension) and 1H protons (direct dimension) via the direct hetero-nuclear coupling (J 13C-1H). Two different cross-peaks are present for each 13C resonance: first cross peak at the resonance of the directly bonded proton and the second at the resonances of other protons in the same spin system. This experiment has proved very useful especially when analyzing complex proton spectra for which the 2D TOCSY spectrum is overcrowded [31]. OTHER ANALYTICAL STRATEGIES Literature NMR spectroscopic data and structural information of most of the metabolites can be easily found in literature and scientific databases. Chemical shift, coupling constant and coupling pattern reported in the literature of NMR-based metabolomics studies are also very helpful in the screening assignments of metabolites. There is plenty of literature available related to assignments of metabolites in human body fluids and pathological fluid samples like serum, plasma [32], urine [17], liver abscess [33], brain abscess [34], ascetic fluid [35], follicular fluid [36], amniotic fluids [37], seminal fluid [38], saliva [39], CSF [40], cancer tissues of different organs like brain [41], breast [42], colon [43], liver [44], kidney [45], lung [46], pancreas [47], gallbladder [48], ovary [49], oral tissue [50], uterus [51], prostate [52], etc. Analysts should explore previously reported NMR-based metabolomics studies similar to their works. For assignment of unknown metabolites in biological samples, chemical shift, splitting pattern and coupling constants of known molecules can be used from standard spectra available at biological NMR data banks [53]. Spiking Experiments Identification of metabolites having very low concentration is quite cumbersome. Applications of 2D


NMR experiments to such metabolites are limited due to reduced sensitivity when compared with 1D 1H experiments. Metabolites having singlet resonances (acetate, succinate, acetoacetate etc.) cannot be easily identified by two dimensional experiments. Spiking experiments can be applied to identify such metabolites. Metabolites having dynamic range problem, can also be identified very easily by spiking experiments followed by 1D & 2D experiments. In spiking method, NMR spectrum of unknown sample is recorded, followed by re-recording of the second spectrum of the same sample with spiking of pure compounds (suspected metabolites). All the experimental parameters for both the spectra should be exactly the same. Increase in the intensity of all resonances of the suspected metabolite confirms its presence in the sample. For example, assignment of aspartic acid, asparagine and methionine in liver abscess sample is shown in Figure 2. Asparagine, aspartic acid and threonine were in low concentration as compared to other metabolites and were overlapping with each other. Standard solutions of aspartic acid, asparagine

Fig. (2). Proton NMR spectra of [A] human liver pus sample. Aliquots of the same liver pus sample recorded at the same experimental parameters after spiking with [B] aspartic acid [C] asparagine and [D] methionine. Dotted lines in [B], [C] and [D] shows original intensity of liver pus sample before spiking.


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and methionine were prepared separately in D2O. All these metabolites were individually spiked in separate aliquots of the same liver abscess sample and the NMR spectra were recorded. After addition of aspartic acid in liver abscess sample, increase in only aspartic acid’s resonances was observed (Fig. 2B). The intensity of other metabolites resonances remained the same. This confirms the presence of aspartic acid. Similarly, asparagine and methionine were also assigned. Standard Spectra, NMR Data Banks and Libraries Comparing NMR spectra of standard compounds with spectra of metabolomics sample can help in the identification of metabolites. NMR spectra of pure metabolites in the similar solvent system can be recorded for comparative assignments. Biological Magnetic Resonance Bank and Human Metabolome DataBase provide the options to download the 1D, COSY, TOCSY, 1H-13C HSQC, DEPT and HMBC NMR raw data of standard metabolites [53, 54]. These data can be processed accordingly using regular NMR software and used for comparison with spectra of metabolomics samples [33]. For example, comparison of one and two dimensional spectra of standard metabolites with liver abscess spectra is shown in (Figs. 3 and 4). Sample and standard spectra recorded at different NMR frequency may cause little difficulties in comparison because of varying

Fig. (3). Comparison of proton NMR spectrum of human liver pus sample [A, Black] with standard spectra of [B, Red] methionine [C, Sky Blue] asparagine and [D, Green] aspartic acid. When all the standard spectra overlapped to spectra of liver pus sample, corresponding resonance of each metabolite matched to standard spectra [A].


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tra of about 250 metabolites. All the NMR data are available in the public domain and can be used for metabolic assignments. Standard NMR spectra of metabolites which have been recorded at different pH values (6.6, 7.0, and 7.4) highlights the effect of pH on chemical shifts. In this database, each metabolite has sixteen NMR spectra including 1D and 2D J-resolved spectra which are recorded using different water suppression techniques and acquisition parameters. Madison Metabolomics Consortium Database (MMCD): MMCD provides NMR and Mass spectrometry based metabolomics database. This is an open database, which provides a facility to carry out chemical shift based search for unknown resonances. Standard metabolites are recorded at defined conditions and are available on the website and their raw NMR data file can also be downloaded. Experimental conditions, NMR chemical shifts determined by empirical and/or theoretical approaches, biological information about metabolites, links to references and other public databases are also available on MMCD.

Fig. (4). Comparison of 1H-13C HSQC spectrum of human liver pus sample (blue) with standard 1H-13C HSQC NMR spectrum of glucose (red) was carried out for assignments of glucose peaks. 1D proton spectra are standard glucose (red, bottom side) and liver pus is also overlaid (blue, top). Matching HSQC peaks of glucose are highlighted by a circle mark.

natural line width. In such cases, J-coupling, splitting pattern, inter-resonance chemical shift difference etc., can be measured separately for both the sample and standard spectra for comparative assignments. There are a number of NMR data banks and libraries now available which help in the assignment of small molecules in biological samples like serum, urine, pathological fluids, tissue extracts, plant extracts, etc. Some of the most commonly used libraries and their details are given below. Biological Magnetic Resonance Bank (BMRB): Several metabolites of standard NMR spectra are available (http://www.bmrb.wisc.edu/metabolomics/) which are recorded at physiological pH and aqueous solubility. Technical help: 1H, 13C, 1H-13C, chemical shift search option for unknown resonances are available. Raw NMR spectral data along with complete 1D and 2D resonances assignment can be downloaded. Human Metabolome Database (HMDB): This database also has a variety of primary and secondary metabolites with their experimental and predicted NMR spectra (http://www.hmdb.ca/). For every metabolite, the complete biological, clinical and chemical description related to its structure, function, presence and concentration in biological systems, pathways, etc. is listed. Raw NMR data file in Bruker or Varian format is available. Several compounds other than metabolites like drug molecules are also reported in HMDB. Birmingham Metabolite Library (BML): BML has a collection of 1D 1H and 2D J-resolved 1H standard NMR spec-

Yeast Metabolome Database (YMDB): YMDB is a database for small molecule metabolites which are produced during metabolism in Saccharomyces cerevisiae (known as Baker’s yeast). Most of the metabolites listed on YMDB (http://www.ymdb.ca) are common for other metabolomics samples too. The metabolites are very well listed with their MS, MS-MS, GC-MS, 1D NMR 2D NMR spectra along with their chemical, biological and related pathways information. Offline NMR database: Some of the databases can be purchased and used offline for standard spectra and chemical shift search. These softwares include Sigma Aldrich FTNMR library, ACD/Labs, Chenomx, etc. The Small Molecule Pathway Database (SMPDB): This database is designed specifically for the elucidation of specific metabolic pathways and new pathway discoveries in metabolomics, transcriptomics, proteomics, and systems biology. SMPDB has more than 350 small molecules related to human’s pathways. SMPDB does not provide much help in the assignment of small molecules, but greatly helps in finding the correlation between metabolites and tracking the other metabolites in abnormal or diseased conditions. All pathways and respective metabolites are well described as hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, signaling pathways and drug-action pathways. SMPDB helps in identifying most of the metabolites from the same metabolic pathway or organ and tissue specific pathways. Platform for Riken Metabolomics (PRIMe): It is a web based platform for metabolomics and transcriptomics related analysis. It also provides some unique tools for metabolomics, transcriptomics and integrated analysis for other ‘omics’ data. Standard multidimensional NMR spectra for each metabolite along with MS/MS, LC-MS, GC-MS spectral data are available on this resource. Using Spin Assign application on PRIMe, annotated 1H, 13C and 1H-13C HSQC peaks can be assigned in bulk. A complete list of chemical shift of all resonances in columns (1D in one column, 2D in two columns) is needed for Spin Assign applications which compare these chemical shifts in available online database. Drift in


chemical shift tolerance and removal of solvent signal from chemical shift list is controlled by users. Software Assisted Assignments Increasing applications of metabolomics encourage researchers to develop software tools and computer programs for automatic and fast data analysis. Software and scripts fasten the metabolic assignments steps and consequently speed up the overall metabolomics study. There are a number of softwares available for NMR metabolomics which can be used for metabolite identification, quantitation and statistical data analysis. These include CCPN [55], Chenomx [56], rNMR [57], MetaboLab [58], MetaboHunter [59], MetaboMiner [60], RANSY [61], STOCSY [62], IQMNMR [63], Mery-B [64], MetaboAnalyst [65], etc. Summarizing detailed principle and application of these software is beyond the scope of this review. Metabolic Pathways Knowledge of metabolic pathways, cycles, organ specific metabolism, etc. can also help in screening the possible metabolites present in the samples. In pathological samples, such as pyogenic brain abscess, amoebic brain, abdomen and liver abscess, pyogenic liver abscess, etc., knowledge resources related to specific pathogen metabolism of pathogen or pathogenesis helps in the assignment of some unusual metabolites [20, 33, 34]. For example, high concentration of bacterial fermentation products such as acetate, succinate, formate, propionate, butyrate, etc. found in pyogenic abscess [20, 33]. Metabolic pathways in human and rat models are well established and can be utilized during assignment of metabolites (e.g. KEGG, http://www.genome.jp/kegg/pathway.html). International Union of Biochemistry and Molecular Biology (IUBMB) has also published metabolic pathways chart designed by Dr. Donald Nicholson (http://www.iubmbnicholson.org/bio.html). These resources not only help in the assignments of metabolites but also in biological interpretation of the data and understanding the pathophysiological mechanisms.

STOCSY and RANSY Statistical total correlation spectroscopy (STOCSY) is one of the very useful methods for metabolomics data analysis. It also helps in identifying metabolites from complex NMR spectra [62]. The correlation matrix generated from STOCSY analysis of a set of 1D NMR spectra identifies peaks from the same metabolites because these peaks show high correlation, which helps in the identification of metabolites. STOCSY method has also been applied to heteronuclear NMR experiments and data generated from different analytical techniques such as MS, LC-NMR, UPLC-MS, etc. STOCSY correlation matrix also gives information about positive and negative correlations among metabolites involved in metabolic pathways. This correlation matrix gives very useful information about group variables in complex spectra and metabolic biomarker recovery but complicates the identification of individual metabolites due to considerably high number of correlation values. On the basis of a similar approach, Wie et al. have developed a new method


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for the identification of metabolites from complex NMR spectra known as ratio analysis NMR spectroscopy (RANSY) which identifies all the signals from a metabolites on the basis of ratio of intensity of peaks or integral areas [61]. In this method, mean ratio across the multiple NMR spectra in the data set is calculated and then divided by standard deviation of ratio to generate a new spectrum that shows relationship among the resonances from same metabolites. The newly generated NMR spectrum contains only selective peaks of the desired metabolite with good signal to noise ratio. This spectrum can be further used for the identification of metabolites by comparing standard NMR spectra of metabolites from databases. RANSY method can also be applied on two dimensional NMR spectra which may provide better resolution as compared to the one dimensional spectra. Fractionation, Separation and Isolation Owing to spectral congestion, dynamic range, solvent peak, masking of small metabolites due lipids and proteins, etc. sometimes it becomes very difficult to a identify maximum number of metabolites. In such cases, fractions of samples in multiple phases, separation of different classes of metabolites or isolation of specific metabolites, are required for their accurate identification. In human or animal metabolomics studies, tissues, serum, plasma, bone marrow, etc. are subjected to solvent extraction to separate lipids in organic fraction, polar metabolites in aqueous fraction and precipitate proteins separately in dual phase solvent extraction, if needed. Therefore, due to fractionations of samples, small metabolites as well as lipid molecules get assigned rapidly and accurately in separate fractions. Folch’s method for lipid extraction [66] and perchloric acid (PCA) method for extracting polar metabolites are widely applied in human and animal metabolomics [67]. Most of the dual phase extraction processes are carried out either using Folch’s method or its modified version [68, 69]. Dual phase methods provide option for separating both the aqueous and organic phases from same aliquot of sample. Despite of its advantages, major drawback of dual phase are loss of metabolites especially when sample amount is low. Extra precaution is needed during extractions and all dilution and volume should be taken into account to minimize the loss of metabolites. Class Selection and Isotopic Labeling Selective isotopic tagging of metabolites using 13C, 15N, and 31P increases the sensitivity of NMR spectroscopy. Isotopic tagging provides easy and unique way of identification of metabolites having low concentration. Ye et al. developed an isotopic tagging method, in which metabolites with carboxyl groups are chemically tagged with 15N ethanolamine [70] through the formation of amide bonds between ethanolamine (-15NH2) and carboxyl-containing (-COOH) metabolites. This method is capable of detecting over 100 metabolites at micro molar concentration in biological using 1H-15N HSQC NMR spectroscopy. Similarly, Tayyari et al. have developed a method for smart isotopic tagging of carboxylcontaining metabolites with 15N-cholamine which enables their enhanced detection by both NMR and MS [71]. On the similar concept of increasing NMR sensitivity and selectiv-


ity, Ye et al. have developed another method for metabolic profiling of amino-containing metabolites using 13 C-formylation followed by 1H-13C HSQC NMR spectroscopy [72]. This method constitutes two step reaction schemes. First, formation of 13C-N-formyloxysuccinimide using 13C-formic acid and N-hydroxysuccinimide in N,NDicyclohexylcarbodiimide contains tetrahydrofuran at room temperature. Then, supernatant containing 13C-Nformyloxysuccinimide from the step first is needed to be mixed with biofluid sample along with NaHCO3 at room temperature for 4 hrs. The resulting reaction mixture needs to be dried under vacuum condition followed by regular NMR analysis procedures. HSQC experiments provide a highly resolved spectrum of the newly introduced moiety (1H-13C). The structural variation in amino-containing metabolites led to a good dispersion of signals in both the dimensions. This method also provides a unique way of precise quantitation and identification of amino-containing metabolites at micro molar concentration. Work-Flow We have proposed a step wise protocol for metabolic assignments (Fig. 5) in NMR based metabolomics for biological samples like urine, serum, plasma, CSF, pericardial fluids, tissues, tissues extracts, semen, tears, saliva, abscesses, bronchoalveolar lavage fluid, peritoneal fluid, sweat, gastric juice, amniotic fluid, synovial fluids, jejuna aspirates, ascetic fluids, etc. All the assignment strategies are divided into four major groups as (1) pure NMR methods, (2) oher analytical steps, (3) computational approach and (4) isolation, purification and fractionation. It is not necessary to go through all the steps for assignments. All these steps could either be used alone or in combination depending upon the need of assignments, structural problems and availability of

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software, chemicals and methodological resources. The analyst should decide the best and optimal steps for unknown peak assignment. For example, for an unknown singlet resonance, it is better to use spiking method rather than 2D NMR or computational approach. Precious and those samples whose quantity is less should be kept properly in the freezer along with NMR tubes so that it can be further used for running additional NMR experiments for metabolic assignment. A good shimmed, well resolved NMR spectrum with optimum S/N ratio, acquisition and processing parameters is the first and necessary step for assignment. All the resonance lines in the 1D NMR spectra should be first screened using their chemical shift, splitting pattern and coupling constant information as reported in literature, biological pathways and major databases. For further confirmatory assignments, the analyst should first record homonuclear and heteronuclear 2D experiments for confirming the resonances by identifying their coupling partners, J-values, splitting patterns, chemical shift, etc. Singlets present in spectra are usually identified by proton and carbon chemical shift in combination with spiking experiments for more accurate assignments (Step-2). Spiking experiments may waste the sample, so, it is better to perform on a small aliquot of the sample. In case of high spectral congestion, heteronuclear experiments such as 13C, 31 P, 1H-13C HSQC for resolution, 2D JRES, selective excitation experiments, COSY, TOCSY, homonuclear and heteronuclear decoupling experiments can be performed for the desired specific or non-specific assignments. One and two-dimensional spectra of standard metabolites assist in faster assignment. The raw 1D and 2D NMR data from major databases can be obtained and Fourier transformed according to desired processing parameters and overlapped in multi-overlap window for faster assignments. Lat-

Fig. (5). Schematic presentation of proposed workflow for identification of metabolites in NMR-based metabolomics. The complete workflow is divided into four major steps [1] pure NMR methods, [2] other analytical steps, [3] computational approach for identification of metabolites and [4] isolation /purification /fractionation of a complex mixture following steps 1, 2, and 3. Lines and direction of the arrow shows the movement of workflow in a systematic manner.



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est NMR software (e.g. TopSpin, Bruker Biospin) has a number of new applications for analyzing complex spectra and marking the assigned peaks. Some of the databases can be used online for assignments and they ask to submit the spectra (ASCII format) or list of metabolites with chemical shifts. Such applications are limited due to drift in chemical shift caused by pH variation, ionic concentration, etc.

sensitivity solved the many challenging problems in structure elucidation. Implementation of the ultra-fast 2D experiments in metabolomics can save more experimental time. NMR hardware development like high field superconducting magnets, cryogenic probes, micro probes, gradient systems etc. drastically increases the application of NMR in medicine or plant metabolomics.

Computational approaches for metabolic assignment are now very popular and a number of new softwares are available for identification and quantification of metabolites. Such software uses a compound NMR spectral library for identification as well as curve fitting tools for quantification of metabolites. Software assisted assignment expedites the process of identification and quantification by reducing the data analysis time from days to a few minutes. This approach for assignment depends upon the availability of such software in the labs.

Apart from low sensitivity, NMR spectroscopy suffers from high spectral congestion in metabolomics samples like plant extracts, urine, tissues, etc. For such complex samples, chemists either use fractionation, solvent extraction or hyphenated chromatographic techniques. However, data obtained from hyphenated analytical methods like GC-MS, LCMS, LC-NMR-MS have wide applications in plant metabolomics, drug metabolite identification in pharmacokinetic studies etc. because of analytical sensitivity and separation of metabolites using chromatographic techniques. Chromatographic analysis of compounds having low boiling point, lack of chromospheres or that are too polar, is a challenging task, whereas NMR analysis of compounds with all the above-mentioned properties is usually simple and less time consuming. In NMR-based metabolomics of animals and human samples, organic and aqueous phase extraction provides enough resolution by separating lipids and small metabolites. Isotopic labeling and specific chemical derivatization of metabolites also help in selection of the class of metabolites which helps in increasing the resolution and the sensitivity of NMR experiments [73].

Some of the native biofluids and many biological samples have very complex spectra where signals of small molecules are masked by proteins, lipoproteins, lipids, etc. In such cases, fractionation of the class of metabolites using different solvent phases (organic, aqueous) is necessary for their accurate identification and quantification. Plant metabolomics samples are even more complex than the samples obtained from animals and humans. Therefore, fractionation of the plant extract is carried out using more than two solvent phases like water-methanol, hexane, ethyl acetate, chloroform, pure water, and a specific solvent extraction method for alkaloids and other medicinally important molecules. Small molecule metabolites, lipid and proteins present in animal and humans metabolomics samples. Aqueous (perchloric acid) and organic (chloroform-methanol) phase extraction of serum, tissue, and cells separate all the small molecules and lipids. All of the proteins content get precipitated in aqueous, organic and dual phase extraction methods [67]. All the above mentioned strategies are required for the assignment in complex biological samples. Analyst should decide on optimal use of extraction techniques to solve the specific structural problems, to save time and increase the authenticity of metabolic identification. CHALLENGES AND FUTURE PERSPECTIVES There are certain limitations of the NMR-based metabolomics that need to be taken into account while extracting maximum possible information from a metabolomics samples. Identification of metabolites in sample is a very crucial step and it should be very accurate before biological interpretation of the data. The most reliable way to identify metabolites confidently and unambiguously is to verify the identification by using multiple assignments strategies as listed in Figure 5. Structural elucidation of metabolites by NMR spectroscopy is more reliable when comparing with other techniques likes GC, HPLC, IR, UV and Mass. NMR provides options for simultaneously determination of structure and concentration of metabolites in mixture. This is one of the major advantages of NMR spectroscopy. Sensitivity of the NMR spectrometer is one of the major problems in identifying the low concentration molecules. Recent developments in high resolution two dimensional homo and hetero-nuclear NMR techniques with improved

CONCLUSION NMR-based metabolomics has a wide range of applications in understanding the variety of biological mechanisms in research, diagnostics, biomedical research, biology, medicine, etc. Careful optimization of qualitative and quantitative protocols for metabolomics provides highly accurate, reliable and authentic data for research as well as for making biological interpretation of the data with a high level of confidence. CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest. ACKNOWLEDGEMENTS Financial assistance from the Department of Science and Technology (DST), Govt. of India is gratefully acknowledged. REFERENCES [1] [2] [3]

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Received: July 28, 2014

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Revised: September 18, 2014

Accepted: September 23, 2014