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Introduction

Professor Lloyd W. Sumner of the Samuel Roberts Noble Foundation, believes that the large-scale profiling of plant metabolites (metabolomics) is advancing our fundamental understanding of plant biochemistry. His goal is to discover novel metabolites and gene functions, and obtain an advanced mechanistic understanding of plant responses to biotic, abiotic, and environmental stimuli. However, the current depth-of-coverage (~10-20%) is still a major limitation, and there is a critical need to better define the metabolic composition of plants.

Professor Kazuki Saito of the RIKEN Center for Sustainable Resource Science expects strategies based on hyphenated techniques such as LC-MS-SPE-NMR and FT-ICR-MS to enable high-throughput structure elucidation [1]. This will pave the way to identifying the vast number of yet unknown plant secondary metabolites, and ultimately complete the picture of entire plant metabolomes. In this Application Note we highlight workflows recently implemented in Profs. Sumner´s and Saito´s laboratories addressing the need to identify unknown compounds in plant metabolomics.

Keywords Instrumentation and Software

Metabolomics impact

Unknown Identification solariX

LC-MS-SPE-NMR Avance NMR

FT-ICR-MS

Authors

Lloyd W. Sumner1, Zhentian Lei1, Dennis Fine1, Daniel Wherritt1, David V. Huhman1, Wiebke Timm2, Gabriela Zurek2, Aiko Barsch2, Kota Kera3, Hidezuki Suzuki3, Ryo Nakabayashi4, Kazuki Saito4,5

1Plant Biology Division, The Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401 USA; 2Bruker Daltonik GmbH, Bremen, Germany; 3Laboratory of Applied Industrial Technology, Department of Biotechnology Research, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu, Chiba, 292-0818, Japan; 4Metabolomic Function Research Group, RIKEN Center of Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; 5Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan

Application Note # LCMS-85

Plant Metabolomics Research Highlight: Prof. Lloyd W. Sumner combines UHPLC-MS-SPE-NMR and Prof. Kazuki Saito employs FT-ICR-MS in novel strategies to identify unknown plant metabolites

databases (PubChem, Chem Spider, MassBank, etc.) for putative metabolite identifications. Confirmation of the putative identifications is performed by purchasing and co-characterizing authentic standards whenever possible or practical, or alternatively, using NMR. • The impact QTOF-MS is also coupled with automated solid-phase extraction (SPE) for higher-throughput purification and concentration of unknown plant metabolites (see Figure 1). Metabolites targeted for annotation are repetitively collected from multiple UHPLC-MS separations onto the same SPE cartridge, enabling the recovery of larger quantities of material required for NMR analysis. Targeted recovery amounts are typically 1-10 µg. Dried, targeted analytes isolated on SPE cartridges are eluted with deuterated solvents for further NMR analyses. Structural identifications are then made from combined UHPLC-MS/MS and NMR data.

• A first validation study of the UHPLC-MS-SPE-NMR setup using a mixture of authentic standards provided high-quality 1H spectra, even from sub-microgram amounts (see Figure 2).

• Compounds isolated from M. truncatula A17 root extracts have been successfully purified and analyzed by the established setup, as shown for several selected examples in Figure 3.

In conclusion, we have assembled a powerful UHPLC-MS-SPE-NMR system for identifying metabolites and annotating metabolite profiles – increasing the depth of coverage and biological content of plant metabolomics experiments.

‘Sequencing’ the first plant metabolome of the model legume Medicago truncatula

Professor Lloyd Sumner

The long-term goal of this metabolomics project is to systematically identify all metabolites present at concen-trations greater than 1 µM in Medicago truncatula; a close relative of alfalfa. This effort is currently being supported by the National Science Foundation IOS#1139489, NSF DBI #1126719 and the Noble Foundation. We are tackling the systematic identification of plant metabolites using a combination of technologies.

In our standard profiling method, we harvest roots and aerial tissues of M. truncatula. These samples are imme-diately frozen in liquid nitrogen, lyophilized, and ground to a fine powder. Compounds are extracted from 10 mg of this material using a triphasic solvent system starting with an initial methanol extraction. Half of the methanol extract is removed for UHPLC-QTOF-MS profiling using the Bruker impact ultrahigh resolution (UHR)-Q-TOF mass spectrometer, which delivers resolution of > 30,000, even for low molecular weight compounds. Water and chloroform are added to the remaining methanol extract, mixed thoroughly, and the separated phases are isolated. Polar metabolites are partitioned in the H2O/methanol phase and nonpolar and lipophilic metabolites are partitioned into the nonpolar CHCI3 phase. Both phases are dried, resus-pended and derivatized in a pyridine solution containing MSTFA before GC-MS analysis.

We use UHPLC-MS-SPE to purify and concentrate targeted compounds for higher throughput NMR analyses. One-and two-dimensional NMR analyses are performed on a Bruker AVANCE 600 MHz NMR equipped with a 1.7 mm TCI cryoprobe.

• Creation of custom spectral databases: • Our first approach to systematic identification is the co-characterization of authentic standards and the creation of custom spectral databases. The majority of this library includes flavonoids, isoflavonoids, and their glycoside conjugates. NMR and MS/MS mass spectral libraries are currently being generated in collaboration with Bruker using an impact QTOF-MS and an AVANCE 600 MHz NMR.

• Identification of unknown compounds:

• Accurate mass measurements and isotopic ratios are used to predict putative elemental formulas for all observed, but unknown, components within the metabolic profile. Subsequently, molecular formulas are used to search large Web-based chemical

Figure 1: A) Setup of the UHPLC-UV-MS-SPE and NMR systems. The system incorporates a Waters Acquity Iclass UHPLC coupled to a Bruker impact QTOF-MS via a Bruker BNMI splitter that partitions the UHPLC eluent 95% to the SPE and 5% to the Q-TOF-MS. Mass or UV can be used to trigger SPE concentration and isolation. SPE-trapped compounds are eluted and collected in 1.7 mm NMR tubes or common vials using a Gilson 215 liquid sample handler. NMR data are then collected using a Bruker AVANCE 600 MHz NMR equipped with a 1.7 mm TCI cryoprobe. B) The setup in Prof. Sumner’s laboratory.

UHPLC-UV-MS-SPE-NMR-Setup for ID of low-abundance unknown metabolites

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3,28-diglucoside medicagenic acid (authentic standard)

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Figure 2: A) UHPLC-MS-SPE-NMR validation was first performed using a mixture of authentic compounds. B) The mixture was repetitively injected, eluting compounds SPE-purified and recoveries determined. C) Recoveries varied, but averaged about 62.6%, and quality 1H spectra were obtained from as little as 900 ng material.

UHPLC-MS-SPE-NMR validation

LC/Q-TOF SPE profile of flavone/saponin mix

~ 900 ng umbelliferone 1H NMR

Reference Spectrum of authentic umbelliferone

~900 ng umbelliferone 1H NMR

formic acid

600 MHz, in CD3CN

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from SPE cartridge

umbelliferoneO OHO

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Figure 3: Comparison of A) an authentic ononin standard with malyonyl ononin isolated from M. truncatula A17 roots. The C8 protons are shifted downfield due to the anisotropic effect of the malonate carbonyl. B) Authentic glycosylated medicagenic acid standard with malonyl glycosylated medicagenic acid isolated from M. truncatula A17 roots. The C6 protons of one sugar are shifted downfield due to the anisotropic effect of the malonate carbonyl.

NMR spectra of compounds isolated from Medicago truncatula A17 root extracts vs. reference standards

A B

Identifying unknowns in onions using a novel “S-omics” workflow

Professor Kazuki Saito and Dr. Ryo Nakabayashi

Our laboratory took advantage of the ultrahigh resolution and mass accuracy provided by the solariX FT-ICR-MS platform (Figure 4) to establish a new workflow for unknown identification [2]. By taking into account isotopic fine structure information, a workflow for detecting and identifying sulfur-containing metabolites in a plant metabolomics environment was established. Using the theoretical mass difference of 1.9958 m/z between 32S-containing monoisotopic peaks and their 34S-substituted counterparts, we can screen for sulfur-containing compounds in complex plant metabolite extracts such as onion bulbs (Allium cepa) (see Figure 5 and [1,2]).

Identification of unknowns typically starts with generating the correct molecular formula from accurate mass information. By comparing m/z values of S-containing unknown compounds in plants labeled with 13C to those with a natural carbon abundance, the number of carbon atoms contained in the molecule could readily be calculated. Based on this information, we determined the elemental composition of 22 S-containing metabolites in onion extracts [2].

In addition to accurate mass, the isotopic pattern provides important information for molecular formula generation. Bruker´s SmartFormula software [3] tool restricts elemental formula candidates by comparing theoretical isotopic pattern with measured patterns and calculating a matching score, called mSigma. In addition to usually providing < 1ppm mass accuracy, another advantage of FT-ICR-MS instruments is the possibility to resolve the isotopic fine structure. With a MS resolution of > 250,000 FWHM, the isotopic peak cluster is decomposed into the individual elements, for example, M+1 resolves into two peaks, one for 15N and another for 13C.

A novel, recently-developed “SmartFormulaXR” algorithm can take the isotopic fine structure into account for mSigma calculation, enabling automated unambiguous formula generation. In a preliminary study performed by Dr. Nakabayashi in collaboration with Bruker, the 22 compounds identified in [2] were reevaluated using this novel algorithm. As shown in Figure 5 C, using a 1ppm mass accuracy window, the SmartFormulaXR algorithm ranked the correct formula candidate #1 in all 22 cases.

The strategy for “Sulfur-omics” – using a solariX FT-ICR-MS to screen for S-containing compounds in complex plant metabolite extracts – can likewise be adapted for identifying other herteroatom containing metabolites. We believe this method is a powerful tool for addressing one of the biggest challenges in metabolomics – the identification of unknown compounds.

Conclusion

Mass spectrometry and NMR are the two major platforms used for unknown identification in metabolomics research because they provide complementary information for de novo identification.

Recent developments in hardware and software help to target the major challenge of characterizing the large number of yet unknown molecules. As outlined in this article, hyphenating an impact QTOF-MS with an AVANCE 600 MHz NMR in an UHPLC-MS-SPE-NMR setup enabled Prof. Lloyd Sumner’s group to precisely isolate and identify even minor low-abundance compounds from complex mixtures. By making use of the superior resolution and mass accuracy of FT-ICR-MS, Prof. Saito’s group have paved the way for the discovery and identification of yet unknown heteroatom-containing plant metabolites. Combined with the novel SmartFormulaXR algorithm, which makes use of isotopic fine structure information for unambiguous formula generation, his approach represents a straightforward workflow for identification of unknowns.

solariX LC-Qq-FT-ICR-MS

Prof. Saito´s research group at RIKEN Center for Sustainable Resource Science

Figure 4: Prof. Kazuki Saito´s research group at RIKEN Center for Sustainable Resource Science with the solariX FT-ICR-MS in the background.

A novel „S-omics“ workflow using solariX LC-FT-ICR-MS

382.110101+

383.113471+

384.105901+

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C₁₃H₂₃N₃O₆S₂, M+nH, 382.11010

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382 383 384 385 386 387 m/z

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C₁₃H₂₃N₃O₆S₂, M+nH, 382.11010

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Resolved 34S isotope in [M+2]

C13H24N3O6S2

34S

18O 13C2

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nonlabeled_onion_p_9_01_404.d: Isotope Cluster Analy sis: d m/z 1.9958±0.003, I/I 0.045±50%

Simulated isotopic pattern for C13H24N3O6S2 exhibits fine structure at a resolution of 380.000.

Screening for S-containing compounds by mass difference of 1.9958 m/z

382.110101+

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C₁₃H₂₃N₃O₆S₂, M+nH, 382.11010

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382 383 384 385 386 387 m/z

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C₁₃H₂₃N₃O₆S₂, M+nH, 382.11010

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Resolved 34S isotope in [M+2]

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nonlabeled_onion_p_9_01_404.d: Isotope Cluster Analy sis: d m/z 1.9958±0.003, I/I 0.045±50%

Simulated isotopic pattern for C13H24N3O6S2 exhibits fine structure at a resolution of 380.000.

Screening for S-containing compounds by mass difference of 1.9958 m/z

Figure 5: Unknown ID in onion bulbs applying a novel “S-omics” workflow using solariX LC-FT-ICR-MS and SmartFormulaXR.A) Simulated isotopic pattern for C13H24N3O6S2 exhibits fine structure at a resolution of 380,000. B) Using the theoretical mass difference of 1.9958 m/z between 32S-containing monoisotopic peaks and their 34S-substituted counterparts enables screening for S-containing compounds in complex onion extract by LC-FT-ICR-MS. C) Table adapted from [2], 22 identified compounds were reevaluated using a novel SmartFormulaXR algorithm which takes isotopic fine structure into account for mSigma calculation.

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Molecular FormulaTheoretical

m/zMass Error

(ppm)SmartFormula XR

mSigmaRank

(5 ppm)Overall # of results

(5 ppm)SmartFormula

XR Rank (1 ppm)

C6H12NO3S 178.05324 -0.20 7.9 1 1 1

C9H17N2O5S 265.08527 -0.09 7.0 1 4 1

C12H22NO8S 340.10606 -0.03 5.7 1 5 1

C18H32NO13S 502.15889 -0.13 31.0 7 26 1

C18H34NO14S 520.16945 -0.14 23.6 6 17 1

C12H22NO8S 340.10606 0.01 28.7 1 5 1

C11H19N2O6S 307.09583 0.48 4.9 2 4 1

C6H11O2S2 147.04743 -0.07 26.7 1 1 1

C10H19N2O5S 279.10092 0.28 7.1 1 3 1

C14H24N3O8S 394.12786 0.62 2.6 1 10 1

C6H12NO2S2 194.03040 -0.41 72.1 1 1 1

C8H14NO3S2 236.04096 -0.04 21.6 1 3 1

C8H17N2O3S2 253.06751 0.19 10.7 3 4 1

C11H16N5O3S 298.09684 0.24 8.0 1 4 1

C6H14NO2S2 196.04605 -0.26 3.3 1 3 1

C6H11O2S2 179.01950 -0.22 6.1 1 3 1

C10H18N3O6S2 340.06315 -0.13 n.a. n.a. 9 1

C11H19N2O5S 291.10092 0.15 11.2 1 5 1

C13H22N3O6S2 380.09445 0.20 24.2 3 9 1

C13H24N3O6S2 382.11010 -0.06 6.9 1 8 1

C16H30N3O6S3 456.12912 -0.73 19.1 1 16 1

C6H15OS2 167.05588 -0.04 7.5 1 2 1

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References

[1] Nakabayashi & Saito 2013; Anal Bioanal Chem. 405(15):

5005-11

[2] Nakabayashi et al. 2013 Anal Chem. 85(3):1310-5

[3] Bruker Technical Note TN-26

Acknowledgement

Professor Lloyd W. SumnerAnalytical Biochemistry Plant Biology Division, The Samuel Roberts Noble Foundation Ardmore, OK, USA

“We are pushing the boundaries of metabolite identification in our lab with Bruker´s hyphenated UHPLC-UV-MS-SPE-NMR system. The combination of complementary MS and NMR technology will be used to accelerate the identification of unknown compounds; an integral part of achieving the aim of ‘sequencing’ the Medicago truncatula metabolome.”

Professor Kazuki SaitoGroup Director of the Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, Yokohama, Japan, Professor of the Graduate School of Pharmaceutical Sciences Chiba Univesity, Japan

“Strategies based on hyphenated techniques like LC-MS-SPE-NMR and FT-ICR-MS are going to enable higher-throughput structure elucidation in plant metabolomics.”