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Comment on “METLIN: A Technology Platform for Identifying Knowns and Unknowns” Geoffrey C. Kite Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03613 • Publication Date (Web): 09 Oct 2018 Downloaded from http://pubs.acs.org on October 10, 2018
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Analytical Chemistry
Comment on “METLIN: A Technology Platform for Identifying Knowns and Unknowns” Geoffrey C. Kite Analytical Methods, Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE
Anal. Chem. 2018, 90 (5), pp 3156–3164. DOI: 10.1021/acs.analchem.7b04424
The METLIN tandem mass spectrometry database is undoubtedly a significant resource for the annotation (identification) of small molecules in biological extracts following analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS)1. The need for such libraries of tandem MS/MS spectra has been driven in part by the rising use of metabolomics methods in biomarker discovery2. Most frequently this involves the analysis of small molecules in mammalian − often human − blood and urine resulting in platforms such as the Human Metabolome Database specifically supporting such research3. Plant metabolomics, often focussing on secondary metabolites, has generally used the workflows and resources developed for primary metabolites4,5 with comparatively few additional plant-specific resources having been created6,7 even though the analysis of plant secondary metabolites presents particular and different challenges. My comment on METLIN is whether the parameters used to acquire MS/MS spectra for this resource require revision to accommodate the needs of plant metabolomics. Many plant small molecules can be considered as ‘multi-unit’ compounds; for example, glycosides such as flavonoid glycosides, terpenoid glycosides (saponins) and phenylethanoid glycosides. To annotate such compounds to a high degree of confidence by tandem MS/MS is perhaps beyond current expectations, given the number of possible positional isomers and isomeric subunits, but the annotation of certain subunits of these plant metabolites, such as the aglycone of a flavonoid O-glycoside, should be achievable by reference to tandem MS/MS libraries. METLIN records tandem MS/MS spectra at low, medium and high collision energies, as is typical in untargeted metabolomics, with the goal of achieving maximum fragment coverage. Specifically, METLIN uses energies of 10, 20 and 40 eV on their platform and this works very well for many small molecules, but possibly not for larger multi-unit plant secondary metabolites. For example, in the tandem MS/MS spectra of [M + H]+ (at m/z 611) of rutin, a diglycoside of quercetin and one of the most frequently encountered plant flavonoid O-glycosides, the aglycone ion (at m/z 303, protonated quercetin) is hardly fragmented even in the high energy (40 eV) spectrum. Fragments detected below m/z 303 appear to be from the attached sugars, not the fragments of quercetin. Such a spectrum could only be used to support an annotation as a quercetin O-glycoside from the exact mass of the aglycone, since there is no confirmation from the diagnostic A- and B-ring aglycone fragments ions at m/z 153 and 1378. In the high energy METLIN spectrum of querctirin, a monoglycoside of quercetin, these A- and B-ring aglycone fragments are evident, albeit at low relative abundance. This suggests that for flavonoid O-glycosides having two or more sugar units, the
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collision energy in the high energy spectrum used by METLIN and other libraries such as WEIZMASS7, which uses a 10−30 eV ramp, is not high enough. This could be a particular problem when distinguishing glycosides of the two most frequently encountered isomeric flavonoid aglycones, luteolin and kaempferol. Experimental spectra for diglycosides of luteolin and kaempferol were not evident on METLIN so spectra of luteolin 3’,7-Odiglucoside and kaempferol 3-O-rutinoside were recorded using the ‘HCD’ MS/MS capability of a Thermo Scientific LTQ-Orbitrap XL mass spectrometer. On this instrument, a normalised collision energy of 30% applied to protonated rutin reproduced METLIN's high energy spectrum for rutin; i.e. fragments of the aglycone were lacking (Figure 1C). When this collision energy was applied to the diglycosides of luteolin and kaempferol there was also no fragmention of either of the protonated aglycones, both at m/z 287 (Figure 1A&B). Luteolin 3’,7-O-diglucoside has the same molecular mass as rutin (M = 610 Da) and kaempferol 3-O-rutinoside (M = 594 Da) is only 16 Da less, so the effects of collision energy normalisation were nullified in this experiment. Using a much increased normalised collision energy of 70% did generate diagnostic A- and B-ring aglycone fragments at m/z 153 and 135, respectively, for luteolin 3’,7-O-diglucoside and at m/z 153 and 121 for kaempferol 3-Orutinoside (Figure 1D&E). Diagnostic A- and B-ring quercetin fragments at m/z 153 and 137 were also generated in the spectrum of rutin at 70% normalised energy (Figure 1F), and this spectrum was in accordance with the HCD MS/MS spectra for protonated rutin lodged on mzCloud9. These higher energy spectra of flavonoid O-glycosides, with the diagnostic A- and B-ring fragments, would therefore have more value in library search or de-novo annotation workflows. As the number of plant metabolites on METLIN increases, there is an argument for ‘triaging’ multiunit plant molecules at collision energies higher than 40 eV to determine whether any further fragments generated are of sufficient diagnostic value to merit adding a higher energy spectrum to the METLIN resource. Such an approach could further increase the value of METLIN in plant metabolomics.
REFERENCES (1)
Guijas, C.; Montenegro-Burke, J. R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A. E.; et al. METLIN: A Technology Platform for Identifying Knowns and Unknowns. Anal. Chem. 2018, 90, 3156– 3164.
(2)
Trivedi, D. K.; Hollywood, K. A.; Goodacre, R. Metabolomics for the Masses: The Future of Metabolomics in a Personalized World. New Horizons Transl. Med. 2017, 3, 294–305.
(3)
Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; et al. HMDB 4.0: The Human Metabolome Database for 2018. Nucleic Acids Res. 2018, 46, D608–D617.
(4)
Kueger, S.; Steinhauser, D.; Willmitzer, L.; Giavalisco, P. High-Resolution Plant Metabolomics: From Mass Spectral Features to Metabolites and from Whole-Cell Analysis to Subcellular Metabolite Distributions. Plant J. 2012, 70, 39–50.
(5)
Perez De Souza, L.; Naake, T.; Tohge, T.; Fernie, A. R. From Chromatogram to Analyte to Metabolite. How to Pick Horses for Courses from the Massive Web Resources for Mass Spectral Plant Metabolomics. Giga Sci. 2017, 6, 1–20.
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(6)
Sawada, Y.; Nakabayashi, R.; Yamada, Y.; Suzuki, M.; Sato, M.; Sakata, A.; Akiyama, K.; Sakurai, T.; Matsuda, F.; Aoki, T.; et al. RIKEN Tandem Mass Spectral Database (ReSpect) for Phytochemicals: A Plant-Specific MS/MS-Based Data Resource and Database. Phytochemistry 2012, 82, 38–45.
(7)
Shahaf, N.; Rogachev, I.; Heinig, U.; Meir, S.; Malitsky, S.; Battat, M.; Wyner, H.; Zheng, S.; Wehrens, R.; Aharoni, A. The WEIZMASS Spectral Library for High-Confidence Metabolite Identification. Nat. Commun. 2016, 7, 12423.
(8)
Cuyckens, F.; Claeys, M. Mass Spectrometry in the Structural Analysis of Flavonoids. J. Mass Spectrom. 2004, 39, 461–461.
(9)
mzCloud https://www.mzcloud.org/ (accessed Aug 7, 2018).
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Figure 1. Tandem MS/MS spectra (zoomed in the region m/z 60−320) recorded at 30% (A−C) and 70% (D−E) normalised collision energy of [M+H]+ of luteolin 3’,7-O-diglucoside, m/z 611 (A, D), kaempferol 3-O-rutinoside, m/z 595 (B, E) and quercetin 3-O-rutinoside, rutin, m/z 611 (C, E). Data were recorded at high resolution but only nominal m/z reproduced for clarity.
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