Article pubs.acs.org/ac
SpinCouple: Development of a Web Tool for Analyzing Metabolite Mixtures via Two-Dimensional J‑Resolved NMR Database Jun Kikuchi,*,†,‡,∥ Yuuri Tsuboi,† Keiko Komatsu,† Masahiro Gomi,† Eisuke Chikayama,†,§ and Yasuhiro Date†,‡ †
RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan § Department of Information Systems, Niigata University of International and Information Studies, 3-1-1 Mizukino, Nishi-ku, Niigata-shi, Niigata 950-2292, Japan ∥ Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan ‡
S Supporting Information *
ABSTRACT: A new Web-based tool, SpinCouple, which is based on the accumulation of a two-dimensional (2D) 1H−1H J-resolved NMR database from 598 metabolite standards, has been developed. The spectra include both J-coupling and 1H chemical shift information; those are applicable to a wide array of spectral annotation, especially for metabolic mixture samples that are difficult to label through the attachment of 13C isotopes. In addition, the userfriendly application includes an absolute-quantitative analysis tool. Good agreement was obtained between known concentrations of 20-metabolite mixtures versus the calibration curve-based quantification results obtained from 2D-Jres spectra. We have examined the web tool availability using nine series of biological extracts, obtained from animal gut and waste treatment microbiota, fish, and plant tissues. This web-based tool is publicly available via http://emar.riken.jp/spincpl.
A
SpinAssign,27,28 BMRB,29 HMDB,30 TOCCATA,31 and BML.32 These databases enable highly accurate metabolic studies. In particular, SpinAssign is developed similarly to BMRB, and its properties are inherited by our new database, SpinCouple. In SpinCouple, we have accumulated 598 standards of 2D-Jres NMR spectra and have made them freely available on the web. Therefore, we expect that our numerous databases will enable annotation of diverse samples and that many users will be attracted to the advantages of the 2D-Jres approach, which includes short acquisition time and applicability to numerous NMR instruments. Moreover, metabolic changes in time-course samples can be quantitatively analyzed. Such time-varying quantification should reveal the homeostatic dynamics not only of human beings but also of their surrounding environments such as external ecosystems.33 For this purpose, we developed a web tool that performs quantitative analysis, enabling the absolute quantification of commonly observed major metabolites.
lthough many techniques exist for studying the metabolome,1−4 the annotation and quantification of metabolites remains problematic. Metabolomics integrates various data provided by nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). NMR spectroscopy is a particularly effective, nondestructive method for detecting the components of biochemical mixtures.5−7 Advancements in NMR spectroscopy have been accompanied by rapid detection of metabolic biomarkers for various organisms based on nontargeted analysis of relative variations of metabolites.8,9 For these reasons, NMR spectroscopy is used in the medical and pharmacological sciences,10−14 the evaluation of agricultural and fishery products,15,16 environmental sciences,17,18 and basic biology.19−22 However, the spectra typically used in metabolomic studies, namely, one-dimensional (1D) 1H NMR spectra, often contain overlapped peaks, which are difficult to deconvolve. To overcome this obstacle, the indirect axis is expanded along the second dimension. Two-dimensional (2D) 1 H−1H J-resolved (Jres) NMR spectroscopy23 is an alternative tool24−26 used when the 13C and 15N isotopic labeling becomes arduous. Most importantly, databases enable subsequent researchers to identify metabolites from complex spectra and evaluate the functions of those metabolites. Various databases have been developed for NMR-based metabolomics research, including © XXXX American Chemical Society
Received: June 19, 2015 Accepted: December 1, 2015
A
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 1. Scheme for development and use of SpinCouple. (A) Metabolite annotation: (1) Standard chemical compounds in metabolic pathways are selected for database construction. (2) A 2D-Jres spectrum is recorded for each of the selected chemical compounds. (3) Peaks in the 2D-Jres spectrum (δ1H, J) are accumulated in a standard database. (4) A 2D-Jres experiment is conducted to obtain peak-pick lists for a user biological sample. (5) The 2D-Jres peaks from the user biological sample are queried within user-defined tolerances for the F2 and F1 axes by the SpinCouple program; the metabolite annotation is then computed. (B) Metabolite quantification: (1) Standard chemical compound solutions with different concentrations are prepared. (2) A 2D-Jres spectrum is recorded for each of the differently diluted standard solutions, and the peak-intensity information is accumulated in a standard database as a calibration curve for quantification. (3) A 2D-Jres experiment is conducted to obtain the peakpick lists for a user biological sample. (4) The 2D-Jres peaks from the user biological sample with intensity information are queried within userdefined tolerances for the F2 and F1 axes by the SpinCouple program; the metabolite annotation is then computed, along with a calibration curvebased quantification based on DSS intensity.
■
content38 and muscle (yellowfin goby),39 gut of young Japanese amberjack, termite midgut content,21 water from a paddy field,40 diatom algae, Welsh onion,41 and human feces from a volunteer.42 All extracts were maintained at pH 7.0 by use of the aforementioned 0.1 M KPi buffer system. NMR Spectroscopy. All 2D-Jres NMR spectra (magnitudemode gradient-enhanced J-resolved with presaturation, named as jresgpprqf) were collected at 298 K on a Bruker Avance II DRU 700 NMR spectrometer operating at 700.15 MHz and equipped with a 1H inverse cryogenic probe with Z-axis gradients. Parameters of NMR measurements were as follows: time domain data size was 16 384 for f 2 and 32 for f1, spectral width (hertz) was 12 500 for f 2 and 50 for f1, and number of scans was 8 with 2 s recycle delay for standard compounds. Spectra were processed with the software TopSpin (Bruker Biospin, Rheinstetten, Germany) with sine-bell window function, zero-fillings to 128 points, tilt correction, and symmetrization. Another program, complete molecular confidence (CMC)-assist (Bruker Biospin, Rheinstetten, Germany), was used to process the coupling patterns (i.e., Jcouplings) and automatically compare the spectra and structures (Figure S1). The peaks were identified by use of
MATERIALS AND METHODS Purified Standards. Spectral data for 155 samples were obtained from the Birmingham Metabolite Library (BML, http://www.bml-nmr.org/). Moreover, to increase the database information, spectral data for 442 standard samples were measured under constant conditions by NMR spectroscopy. These samples were dissolved in deuterium oxide containing a chemical shift buffer [100 mM, pH 7.4 (direct measurement of pH meter + 0.4), 1.0 mM 2,2-dimethyl-2-silapentane-5sulfonate (DSS)]34 prepared from 1 M aqueous potassium phosphate stock solutions (KH2PO4 and K2HPO4). Furthermore, to evaluate the quantification performance of the SpinCouple tool, we constructed calibration curves relative to the internal 1.0 mM DSS signal for 38 commonly observed major metabolites (Table S1). Among them, 20 standards were mixed in known-concentration sample sets (Table S2) and were used in absolute quantification experiments based on 2DJres NMR spectra, as described in the subsequent sections. Biochemical Mixtures. The accuracy and uniqueness of the metabolite annotations in our database were evaluated on nine series of biological samples. The samples were collected from anaerobic fermenters35−37 in our laboratory, fish gut B
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Figure 2. Schematic showing SpinCouple web tool. (A) An example of biological extract (termite gut content) of 2D-Jres spectrum (black contour, methyl expanded region), overlaid with BML (blue) and SpinCouple (red) database peaks. (B) An example of a query form and (C) results for the query using SpinCouple (query peaks are highlighted in yellow on the methyl region expanded 2D-Jres spectrum).
were constructed (Figure S4). The SpinCouple tool shows that these databases include an abundance of useful information, including 1H chemical shifts with assigned metabolites, PubChem, KEGG43 IDs, and especially, J-coupling patterns and values. Therefore, these data are useful for annotating biological mixtures and for performing subsequent analyses. PHP interprets user queries, connects to the database, and retrieves a result. The result is converted into HTML data and sent to a web browser. JavaScript on the web browser processes the data and provides a convenient view of the results and a rapid response. Figure 2 shows a schematic diagram of the user interface of SpinCouple on the web. The database provides various data outputs from the numerical values of 1H chemical shifts and Jcouplings, as well as quantification relative to internal 1 mM DSS signal intensity. The search method is easy and userfriendly in that the 1H chemical shifts and J-values are tabulated in separate left and right columns, respectively, and users can manipulate the tolerance for both values. The data set of 1H chemical shifts and J-couplings was configured for the range between 0 and 12 ppm and between −25 and 25 Hz, respectively. Uniqueness and Accuracy of Metabolite Annotation Using Biochemical Mixtures. Here uniqueness refers to the extent to which a peak in the reference chemical-shift database does not overlap with other nearby peaks in the reference chemical-shift database.28 Uniqueness for a reference database peak is defined as 1/C, where C denotes the number of matches around the reference peak when it is queried to the database
an automated algorithm embedded in the software and refined manually.
■
RESULTS New Database Construction for 2D-Jres Spectra. The advantage of 2D-Jres NMR-based experiment is its quite short acquisition time. It requires approximately 15 min. Furthermore, the obtained coupling information is useful to elucidate the chemical structure. The J-coupling pattern and intensity information are obtained with the CMC-assist program. The outputs of the CMC-assist program include not only multiplet analysis, integration, 1H number determination, and J-coupling but also structural assignments, consistency statements, and concentration. We have accumulated these reports on detailed assignment information consisting over 10 000 standard peak lists (Figure S2). Web Interface Design. The SpinCouple tool provides batch annotations of a large number of metabolites against user NMR peaks, based on our original 1H chemical shift and 1 H−1H spin-coupling database. The database contains chemical shift J-values and intensities obtained under accurately standardized measurement conditions; these data are thus adequate for batch annotations and quantification of metabolite concentrations (Figure 1). The SpinCouple tool was developed in HTML, PHP, JavaScript, and SQLite. Because some metabolites exhibited substantial differences in signal intensities among the same metabolites between the BML database and our SpinCouple database, we decided against mixing these databases (Figure S3). Therefore, three tables with metadata C
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Table 1. 1H Chemical Shift and J-Coupling Accuracy and Uniqueness of Metabolites Detected in Various 2D-Jres Spectra of Nine Types of Biological Extractsa biological extract no. of peaks accuracy (ppm) accuracy of SDb (ppm) accuracy (Hz) accuracy of SD (Hz) uniqueness
A
B
C
D
E
F
G
H
I
avg
154 0.0027 0.0021 0.14 0.12 0.083
307 0.0020 0.0029 0.18 0.17 0.112
203 0.0019 0.0008 0.16 0.20 0.090
354 0.0021 0.0072 0.18 0.23 0.098
188 0.0017 0.0036 0.17 0.21 0.080
348 0.0029 0.0073 0.27 0.21 0.104
118 0.0062 0.0065 0.14 0.11 0.120
116 0.0027 0.0038 0.13 0.14 0.058
265 0.0015 0.0045 0.27 0.25 0.073
0.0024 0.0052 0.20 0.21 0.091
a
(A) Fermented sludge from young Japanese amberjack, (B) termite (mid gut), (C) human feces, (D) young Japanese amberjack (gut), (E) yellowfin goby (gut), (F) yellowfin goby (muscle), (G) water from paddy field, (H) diatom (Skeltonema costatum), (I) viscous substance of Welsh onion. bSD = standard deviation.
using the tolerance parameters verifying both 1H chemical shifts and J-couplings. Furthermore, accuracy refers statistically meaningful tolerance values for metabolite annotation. Nine biochemical mixture samples were chosen to examine uniqueness and accuracy. These samples included bacteria, plants, and animal extracts that encompass a wide variety of biodiversity. Thus, the result was used to assess the utility of the database. We propose query tolerances of 0.83 Hz for J-couplings and 0.018 ppm for 1H chemical shifts, based on a statistical 99.73% distribution (μ + 3σ). The average accuracy values (μ) for Jcouplings and 1H chemical shifts were 0.20 Hz and 0.0024 ppm, whereas standard deviation values (σ) for J-couplings and 1 H chemical shifts were 0.21 Hz and 0.0052 ppm, respectively (Table 1). Figure S5 shows a histogram of query hits using already-assigned biological extract data sets against more than 10 000 standard database peaks. Our proposed query tolerances (0.83 Hz and 0.018 ppm) appear to distribute to approximately 99.73%. Absolute Quantification. NMR has been used for absolute quantification for decades.44 Absolute quantification has been recognized as a versatile technique for analyzing NMR-based metabolomics.33,45−48 Therefore, we developed a supportive analysis tool that quantifies 38 commonly observed major metabolites (sugars, amino acids, and organic acids) through the same web browser interface. These major metabolite standards yielded calibration with power-law curve fitting (R2 > 0.99) using skyline projection intensities, across almost four orders of dynamic range concentration (1 μM−10 mM; see Figure S6). The linearity of quantification and detection limits were largely dependent on whether signals can be detected or not. If the number of scans is increased from 8 to 16, 64, and 256, limits of detection can be lowered at 1 μM. This quantification tool was evaluated on the 2D-Jres spectra of mixtures of 20 known metabolites (Table S2). A comparison between SpinCouple-estimated results and known concentrations showed good agreement (Figure 3). Therefore, we next attempted to quantify the above-described nine series of biochemical mixtures using SpinCouple. Absolute quantifications were estimated from the equation of the calibration curve, determined by the least-squares method. These quantitative values were input to a logarithmic calculation and are listed in Table 2. For example, we had previously assigned and reported amino acids in termite midgut contents. These amino acids were completely identified from relevant database contents, and 16 amino acids were absolutely quantified by SpinCouple. These amino acids dominate in other animal tissues such as fishes, whereas anaerobic microbiota and plant tissues tend to
Figure 3. Bar graphs showing demonstration of quantification of mixtures of 20 known standards. Abbreviations and mixture compositions mix1, mix2, mix3, and mix4 are listed in Table S2. Blue indicates value of concentration (millimolar), whereas red shows output quantified values calculated from SpinCouple. Metabolite quantification was calculated by a calibration curve-based method as described in the text. Error bars were estimated on the basis of fluctuations from multiple signals per one compound with duplicate experiments.
be rich in short-chain fatty acids and various saccharides, respectively.
■
DISCUSSION NMR-based metabolomics studies are now carried out at many institutions, and all of the data collected in a single study are frequently collected on an individual instrument at a single location. According to cross-site analytical validity studies, the interconvertibility of NMR data among different institutions is a distinct advantage of the NMR-based approach.49,50 Such wide data dissemination is essential for clinical applications such as metabolomics-derived biomarker discovery, evaluation of food quality, and environmental monitoring. Therefore, by freely distributing our NMR database over the web, we can assist a wide range of potential users. The present databases of 2D-Jres NMR spectra are also supplemented by BML data for 155 metabolite standards, and this database is known to be dynamic and useful in obtaining raw spectral data. However, the exhaustive analysis of actual survey data for identifications D
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
Table 2. Absolute Quantification of Major Metabolites Calculated by SpinCouple on the Basis of Peak-Picked Intensities of 2DJres Spectra for Nine Types of Biological Extracts quantificationa [μmol/mg (dry weight)] metaboliteb
A
B
C
D
E
F
G
H
I
Phe Glu Leu Trp Asp Pro Ala Thr Ile Asn Gln Gly Lys Ser Tyr Val BuA FoA LA MA Ac PrA SuA Glc Trh Frc Suc
0.0 93.1 0.0 0.0 0.0 0.0 25.5 0.0 0.0 0.0 67.5 0.0 0.0 0.0 0.0 0.0 7.7 0.0 3.2 0.0 220.5 195.6 1.7 0.0 22.8 0.0 0.0
155.8 419.7 372.9 31.9 259.1 43.7 412.5 310.2 283.6 132.5 179.5 370.0 184.4 304.9 96.9 346.3 0.0 0.0 0.0 0.0 14.8 0.0 1.0 174.3 0.0 0.0 0.0
24.9 39.5 33.3 8.8 20.9 0.0 62.1 28.0 25.4 1.5 12.3 33.0 19.7 37.4 15.2 31.4 121.4 15.2 6.9 0.0 470.8 124.5 34.8 285.3 0.0 7.5 0.0
122.3 370.7 247.9 0.0 0.0 202.8 192.5 123.7 39.6 24.0 144.0 170.7 259.9 204.3 69.3 104.7 11.6 48.6 2609.5 0.0 30.9 7.3 18.4 56.5 0.0 0.0 0.0
35.0 23.8 4.0 0.0 2.1 12.8 22.3 10.1 2.3 0.0 5.2 117.9 5.1 17.1 2.0 3.8 0.0 1.6 244.6 2.0 1.0 4.1 0.2 12.0 0.0 0.0 0.0
1.4 11.5 1.6 0.0 0.0 11.0 17.5 18.6 1.1 0.0 4.9 146.4 4.2 13.7 1.1 2.7 0.0 0.9 216.6 6.6 0.4 0.0 1.1 9.2 0.0 0.0 0.0
0.4 0.0 0.8 0.5 0.0 0.0 1.5 0.0 0.1 0.0 0.0 2.5 0.8 2.9 0.3 0.3 0.0 1.0 47.9 0.0 0.9 0.0 0.1 0.0 0.0 0.0 0.0
0.0 484.4 0.0 0.0 53.6 0.0 389.2 0.0 6.4 0.0 247.9 0.0 0.0 0.0 0.0 54.3 6.7 171.8 19.9 0.0 83.0 0.0 5.0 4388.0 0.0 0.0 0.0
95.7 37.0 3.2 0.0 0.0 0.0 104.0 0.0 3.0 6.6 73.7 173.0 2.5 29.8 1.5 7.3 0.0 0.0 4.8 247.0 9.5 0.0 2.2 1040.8 0.0 741.9 201.3
Extracts A−I are the same as in Table 1. Both supernatant of fermented sludge (extract A) and paddy field water (extract G) are sampled as liquids; therefore, these concentrations are given as micromoles per milliliter. bStandard amino acid abbreviations are used. BuA, butyrate; FoA, formate; LA, lactate; MA, malate; Ac, acetate; PrA, propionate; SuA, succinate; Glc, glucose; Trh, trehalose; Frc, fructose; Suc, sucrose. a
conventional 1D-NMR approach (Figure S7A). This improved quantification is a result of the capability of the 2D-Jres approach to resolve two or more metabolites overlapped within vicinal 1H−1H coupling widths, whereas the conventional 1DNMR approach cannot resolve such peaks (Figure S7B). Then, this fact is useful in the analysis of samples that are difficult to label isotopically, such as agriculture and fishery products, environmental, and human samples. Furthermore, the 2D-Jres spectra can bring great benefit for low-magnetic-field instruments,51 whose spectra are substantially affected by line broadening in conventional 1D 1H NMR experiments due to Jcoupling splits. We therefore expect that a large number of potential users that operate low- to high-magnetic-field instruments for analyzing foods, environmental, and human samples would be interested in accessing our annotation system and database of 2D-Jres NMR spectra.
and annotations is unmanageable. Therefore, development of another web tool was necessary; our new database contains 598 metabolite standards, including the requisite 155 metabolite data (Table S3), showing significant improvement in the comprehensiveness of searching major metabolites (Figure S3). Thus, comprehensive analysis using the database is worthwhile, given the progressive increase in the number of metabolite profiles. One advantage of 2D-Jres is relatively good spectral separation derived from short acquisition time. As shown in Figure 3, the quantification results agreed well with the known concentrations of metabolites. The 2D-Jres approach enables the collection of higher-resolution spectra than conventional 1D-NMR. Furthermore, our calibration curve-based method can quantify metabolite signal intensities with variation in the internal DSS standard signal. Therefore, unexpected data fluctuations (originating from measurements and data processing) do not need to be considered. Instead, we note that the number of points (32), width (50), and zero-filling (128) in f1 axis were important to reproduce our absolute quantification data. Furthermore, a comparison of the results obtained by conventional 1D-NMR (using the Chenomx commercial software) with those obtained by the 2D-Jres approach (skyline projection) in conjunction with a known concentration of metabolite mixtures is shown in Figure S6. The 2D-Jres approach provided more accurate quantification than the
■
CONCLUSIONS We developed the SpinCouple web tool for metabolic annotation and assignment of peak data detected in 2D-Jres NMR spectra. To annotate biological metabolic mixtures using SpinCouple, we proposed query tolerances of 0.83 Hz for Jcoupling and 0.018 ppm for the 1H chemical shift, based on nine types of extracts with spectral assignments. In addition, we successfully quantified some amino acids, organic acids, and sugars in several biological samples using SpinCouple. Expansion of quantifiable metabolites is planned in the future. E
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry
(9) Holmes, E.; Loo, R. L.; Stamler, J.; Bictash, M.; Yap, I. K.; Chan, Q.; Ebbels, T.; De Iorio, M.; Brown, I. J.; Veselkov, K. A.; Daviglus, M. L.; Kesteloot, H.; Ueshima, H.; Zhao, L.; Nicholson, J. K.; Elliott, P. Nature 2008, 453, 396−400. (10) Fukuda, S.; Toh, H.; Hase, K.; Oshima, K.; Nakanishi, Y.; Yoshimura, K.; Tobe, T.; Clarke, J. M.; Topping, D. L.; Suzuki, T.; Taylor, T. D.; Itoh, K.; Kikuchi, J.; Morita, H.; Hattori, M.; Ohno, H. Nature 2011, 469, 543−547. (11) Furusawa, Y.; Obata, Y.; Fukuda, S.; Endo, T. A.; Nakato, G.; Takahashi, D.; Nakanishi, Y.; Uetake, C.; Kato, K.; Kato, T.; Takahashi, M.; Fukuda, N. N.; Murakami, S.; Miyauchi, E.; Hino, S.; Atarashi, K.; Onawa, S.; Fujimura, Y.; Lockett, T.; Clarke, J. M.; Topping, D. L.; Tomita, M.; Hori, S.; Ohara, O.; Morita, T.; Koseki, H.; Kikuchi, J.; Honda, K.; Hase, K.; Ohno, H. Nature 2013, 504, 446−450. (12) Fonville, J. M.; Maher, A. D.; Coen, M.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Anal. Chem. 2010, 82, 1811−1821. (13) Bertini, I.; Cacciatore, S.; Jensen, B. V.; Schou, J. V.; Johansen, J. S.; Kruhoffer, M.; Luchinat, C.; Nielsen, D. L.; Turano, P. Cancer Res. 2012, 72, 356−364. (14) Frost, G.; Sleeth, M. L.; Sahuri-Arisoylu, M.; Lizarbe, B.; Cerdan, S.; Brody, L.; Anastasovska, J.; Ghourab, S.; Hankir, M.; Zhang, S.; Carling, D.; Swann, J. R.; Gibson, G.; Viardot, A.; Morrison, D.; Louise Thomas, E.; Bell, J. D. Nat. Commun. 2014, 5, No. 3611. (15) Kim, H. K.; Saifullah; Khan, S.; Wilson, E. G.; Kricun, S. D.; Meissner, A.; Goraler, S.; Deelder, A. M.; Choi, Y. H.; Verpoorte, R. Phytochemistry 2010, 71, 773−784. (16) Kruger, N. J.; Troncoso-Ponce, M. A.; Ratcliffe, R. G. Nat. Protoc. 2008, 3, 1001−1012. (17) Ito, K.; Sakata, K.; Date, Y.; Kikuchi, J. Anal. Chem. 2014, 86, 1098−1105. (18) Wei, F.; Ito, K.; Sakata, K.; Date, Y.; Kikuchi, J. Anal. Chem. 2015, 87, 2819−2826. (19) Kikuchi, J.; Shinozaki, K.; Hirayama, T. Plant Cell Physiol 2004, 45, 1099−1104. (20) Kozlowska, J.; Vermeer, L. S.; Rogers, G. B.; Rehnnuma, N.; Amos, S. B.; Koller, G.; McArthur, M.; Bruce, K. D.; Mason, A. J. PLoS Pathog. 2014, 10, No. e1004104. (21) Tokuda, G.; Tsuboi, Y.; Kihara, K.; Saitou, S.; Moriya, S.; Lo, N.; Kikuchi, J. Proc. R. Soc. London, Ser. B 2014, 281, No. 20140990. (22) Ward, J. L.; Baker, J. M.; Llewellyn, A. M.; Hawkins, N. D.; Beale, M. H. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 10762−10767. (23) Kumar, A. J. Magn. Reson. 1969, 30, 227−249. (24) Huang, Y.; Cai, S.; Zhang, Z.; Chen, Z. Biophys. J. 2014, 106, 2061−2070. (25) Ludwig, C.; Viant, M. R. Phytochem. Anal. 2010, 21, 22−32. (26) Parsons, H. M.; Ludwig, C.; Viant, M. R. Magn. Reson. Chem. 2009, 47, S86−S95. (27) Chikayama, E.; Suto, M.; Nishihara, T.; Shinozaki, K.; Hirayama, T.; Kikuchi, J. PLoS One 2008, 3, No. e3805. (28) Chikayama, E.; Sekiyama, Y.; Okamoto, M.; Nakanishi, Y.; Tsuboi, Y.; Akiyama, K.; Saito, K.; Shinozaki, K.; Kikuchi, J. Anal. Chem. 2010, 82, 1653−1658. (29) Cui, Q.; Lewis, I. A.; Hegeman, A. D.; Anderson, M. E.; Li, J.; Schulte, C. F.; Westler, W. M.; Eghbalnia, H. R.; Sussman, M. R.; Markley, J. L. Nat. Biotechnol. 2008, 26, 162−164. (30) Wishart, D. S.; Jewison, T.; Guo, A. C.; Wilson, M.; Knox, C.; Liu, Y.; Djoumbou, Y.; Mandal, R.; Aziat, F.; Dong, E.; Bouatra, S.; Sinelnikov, I.; Arndt, D.; Xia, J.; Liu, P.; Yallou, F.; Bjorndahl, T.; Perez-Pineiro, R.; Eisner, R.; Allen, F.; Neveu, V.; Greiner, R.; Scalbert, A. Nucleic Acids Res. 2013, 41, D801−D807. (31) Bingol, K.; Zhang, F.; Bruschweiler-Li, L.; Bruschweiler, R. Anal. Chem. 2012, 84, 9395−9401. (32) Ludwig, C.; Easton, J.; Lodi, A.; Tiziani, S.; Manzoor, S.; Southam, A.; Byrne, J.; Bishop, L.; He, S.; Arvanitis, T.; Günther, U.; Viant, M. Metabolomics 2012, 8, 8−18. (33) Wishart, D. S. TrAC, Trends Anal. Chem. 2008, 27, 228−237. (34) Glasoe, P. K.; Long, F. A. J. Phys. Chem. 1960, 64, 188−190.
The developed program is a powerful and useful tool for researchers engaged in the field of metabolomics to identify and quantify metabolites in various biological and environmental systems.
■
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.5b02311. Seven figures showing representative signal assignment and metabolite structure, comparison of peak number, distributions of standard compounds, 1H chemical shift, J-couplings, and intensities from SpinCouple and BML data, entity relationship diagram for SpinCouple, histograms of query hits, representative calibration curves, quantification of known standard mixtures, and comparison of spectral decomposition results; three tables listing 38 standard compounds and their correlation coefficients, compositions of known 20-compound mixtures 1−4, and all compounds deposited in SpinCouple (PDF)
■
AUTHOR INFORMATION
Corresponding Author
*Phone/fax +81-455039439; e-mail
[email protected]. Author Contributions
The manuscript was written with equal contribution from all of the authors. All the authors have approved the final version of the manuscript. Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS We thank Koyuru Nakayama (Tokyo University of Science) for initial effort of this study. This research was supported in part by a Grant-in-Aid for Scientific Research (Grant 25513012) (to J.K.) and by the Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Technologies for creating nextgeneration agriculture, forestry and fisheries” funded by the Bio-oriented Technology Research Advancement Institution (NARO).
■
REFERENCES
(1) Kim, H. K.; Choi, Y. H.; Verpoorte, R. Nat. Protoc. 2010, 5, 536− 549. (2) Pungaliya, C.; Srinivasan, J.; Fox, B. W.; Malik, R. U.; Ludewig, A. H.; Sternberg, P. W.; Schroeder, F. C. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 7708−7713. (3) Nicholson, J. K.; Holmes, E.; Kinross, J. M.; Darzi, A. W.; Takats, Z.; Lindon, J. C. Nature 2012, 491, 384−392. (4) Gomez, J.; Brezmes, J.; Mallol, R.; Rodriguez, M. A.; Vinaixa, M.; Salek, R. M.; Correig, X.; Canellas, N. Anal. Bioanal. Chem. 2014, 406, 7967−7976. (5) Sellick, C. A.; Hansen, R.; Stephens, G. M.; Goodacre, R.; Dickson, A. J. Nat. Protoc. 2011, 6, 1241−1249. (6) Sekiyama, Y.; Chikayama, E.; Kikuchi, J. Anal. Chem. 2010, 82, 1643−1652. (7) Sekiyama, Y.; Chikayama, E.; Kikuchi, J. Anal. Chem. 2011, 83, 719−726. (8) Clayton, T. A.; Lindon, J. C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Baker, D.; Walley, R. J.; Everett, J. R.; Nicholson, J. K. Nature 2006, 440, 1073−1077. F
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
Article
Analytical Chemistry (35) Date, Y.; Iikura, T.; Yamazawa, A.; Moriya, S.; Kikuchi, J. J. Proteome Res. 2012, 11, 5602−5610. (36) Yamazawa, A.; Iikura, T.; Morioka, Y.; Shino, A.; Ogata, Y.; Date, Y.; Kikuchi, J. Metabolites 2014, 4, 36−52. (37) Yamazawa, A.; Iikura, T.; Shino, A.; Date, Y.; Kikuchi, J. Molecules 2013, 18, 9021−9033. (38) Asakura, T.; Sakata, K.; Yoshida, S.; Date, Y.; Kikuchi, J. PeerJ 2014, 2, No. e550. (39) Yoshida, S.; Date, Y.; Akama, M.; Kikuchi, J. Sci. Rep. 2014, 4, No. 7005. (40) Ogawa, D. M.; Moriya, S.; Tsuboi, Y.; Date, Y.; Prieto-da-Silva, A. R.; Radis-Baptista, G.; Yamane, T.; Kikuchi, J. PLoS One 2014, 9, No. e110723. (41) Date, Y.; Nakanishi, Y.; Fukuda, S.; Nuijima, Y.; Kato, T.; Umehara, M.; Ohno, H.; Kikuchi, J. Food Chem. 2014, 152, 251−260. (42) Misawa, T.; Date, Y.; Kikuchi, J. J. Proteome Res. 2015, 14, 1526−1534. (43) Kanehisa, M.; Goto, S.; Hattori, M.; Aoki-Kinoshita, K. F.; Itoh, M.; Kawashima, S.; Katayama, T.; Araki, M.; Hirakawa, M. Nucleic Acids Res. 2006, 34, D354−D357. (44) Simmler, C.; Napolitano, J. G.; McAlpine, J. B.; Chen, S. N.; Pauli, G. F. Curr. Opin. Biotechnol. 2014, 25, 51−59. (45) Da Silva, L.; Godejohann, M.; Martin, F. P.; Collino, S.; Burkle, A.; Moreno-Villanueva, M.; Bernhardt, J.; Toussaint, O.; GrubeckLoebenstein, B.; Gonos, E. S.; Sikora, E.; Grune, T.; Breusing, N.; Franceschi, C.; Hervonen, A.; Spraul, M.; Moco, S. Anal. Chem. 2013, 85, 5801−5809. (46) Chylla, R. A.; Hu, K.; Ellinger, J. J.; Markley, J. L. Anal. Chem. 2011, 83, 4871−4880. (47) Hao, J.; Astle, W.; De Iorio, M.; Ebbels, T. M. Bioinformatics 2012, 28, 2088−2090. (48) Hao, J.; Liebeke, M.; Astle, W.; De Iorio, M.; Bundy, J. G.; Ebbels, T. M. Nat. Protoc. 2014, 9, 1416−1427. (49) Dumas, M. E.; Maibaum, E. C.; Teague, C.; Ueshima, H.; Zhou, B.; Lindon, J. C.; Nicholson, J. K.; Stamler, J.; Elliott, P.; Chan, Q.; Holmes, E. Anal. Chem. 2006, 78, 2199−2208. (50) Viant, M. R.; Bearden, D. W.; Bundy, J. G.; Burton, I. W.; Collette, T. W.; Ekman, D. R.; Ezernieks, V.; Karakach, T. K.; Lin, C. Y.; Rochfort, S.; de Ropp, J. S.; Teng, Q.; Tjeerdema, R. S.; Walter, J. A.; Wu, H. Environ. Sci. Technol. 2009, 43, 219−225. (51) Mitchell, J.; Gladden, L. F.; Chandrasekera, T. C.; Fordham, E. J. Prog. Nucl. Magn. Reson. Spectrosc. 2014, 76, 1−60.
G
DOI: 10.1021/acs.analchem.5b02311 Anal. Chem. XXXX, XXX, XXX−XXX
