Establishment of a Direct-Injection Electron Ionization-Mass

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Establishment of a direct-injection electron ionization–mass spectrometry metabolomics method and its application to lichen profiling Hisahiro Kai, Kaoru Kinoshita, Hiroshi Harada, Yoshihiro Uesawa, Akihiro Maeda, Ryuichiro Suzuki, Yoshihito Okada, Kunio Takahashi, and Koji Matsuno Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 16 May 2017 Downloaded from http://pubs.acs.org on May 17, 2017

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Analytical Chemistry

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Establishment of a direct-injection electron ionization–mass spectrometry metabolomics

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method and its application to lichen profiling

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Hisahiro Kai †,*, Kaoru Kinoshita ‡, Hiroshi Harada §, Yoshihiro Uesawa ||, Akihiro Maeda †,

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Ryuichiro Suzuki ¶, Yoshihito Okada ††, Kunio Takahashi ‡, Koji Matsuno †

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†

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University of Health and Welfare, 1714-1 Yoshino-machi, Nobeoka, Miyazaki 882-8508,

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Japan

Department of Pharmaceutical Health Sciences, School of Pharmaceutical Sciences, Kyushu

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‡

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2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan

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§

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Japan

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||

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Kiyose, Tokyo 204-8588, Japan

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¶

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Josai University, 1-1 Keyakidai, Sakado, Saitama 350-0295, Japan

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††

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2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan

Department of Pharmacognosy and Phytochemistry, Meiji Pharmaceutical University,

Natural History Museum and Institute, Chiba, 955-2 Aoba-cho, Chuo-ku, Chiba 260-8682,

Department of Clinical Pharmaceutics, Meiji Pharmaceutical University, 2-522-1, Noshio,

Department of Pharmacognosy and Natural Medicines, Faculty of Pharmaceutical Sciences,

Department of Natural Medicine and Phytochemistry, Meiji Pharmaceutical University,

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* Corresponding Author; Hisahiro Kai, Ph. D.

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Phone: +81-982-23-5704

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Fax: +81-982-23-5705

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E-mail: [email protected]

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ABSTRACT

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Direct-injection electron-ionization–mass spectrometry (DI-EI-MS) is a multivariate

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analysis method useful for characterizing biological materials. We demonstrated the use of

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DI-EI-MS for metabolic profiling using several closely related lichen species: Cladonia

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krempelhuberi, C. gracilis, C. pseudogymnopoda, and C. ramulosa. The methodology

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involves conversion of total ion chromatograms to integrated chromatograms and assessment

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of reproducibility. The qualitative DI-EI-MS method was used to profile the major and/or

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minor constituents in extracts of lichen samples. It was possible to distinguish each lichen

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sample by altering the electron energy in DI-EI-MS and examining the resulting data using

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one-way analysis of variance. Previously undetectable peaks which are easy to fragment

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could be revealed by varying the electron energy. Our results suggest that metabolic profiling

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using DI-EI-MS would be useful for discriminating between subgroups within the same

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species. This is the first study to report the use of DI-EI-MS in a metabolomics application.

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KEYWORDS: DI-EI-MS, metabolomics, lichen, electron energy, one-way analysis

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Although physico-chemical methods such as MS and NMR spectroscopy are powerful

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tools for molecular analyses and structure elucidation, their application has been generally

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limited to samples of purified single synthetic or natural compounds. Use of these techniques

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to analyze mixtures of natural materials, including biological fluids such as blood and urine,

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foods, beverages, and intermediate compounds of chemical reaction assays, is increasing,

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however. MS and NMR are also being increasingly used to evaluate the quality of crude drugs

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and traditional medicines.1,2 Due to the unparalleled sensitivity, resolution, and analytical

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range of MS, coupled with advances in liquid chromatography–MS (LC-MS), gas

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chromatography–MS (GC-MS), and capillary electrophoresis–MS (CE-MS), MS is now

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widely utilized in metabolomics studies.3,4,5

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Electron ionization–MS (EI-MS) is a very popular method due to its exceptionally wide

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application range with regard to sample materials. As EI-MS usually produces many fragment

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peaks in addition to molecular ion species, spectral data are generally more complex in

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comparison with other ionization methods.6,7 However, such complexity could be exploited to

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serve as a ‘fingerprint’ that reveals the unique characteristics of a particular sample due to the

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large number of ions of differing mass and intensity, each of which provides partial structural

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information. In addition, the reproducibility of EI-MS analyses is quite high, both in the case

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of analyses of single compounds and multi-component systems. In other words, a mass

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spectrum of a mixture is simply equivalent to the sum of the spectra of all of the individual

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constituents. For this reason, the components of a mixture must be elucidated from the data

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contained in one complex spectrum. Complex EI-MS spectra can provide information

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regarding the inherent properties (e.g., bioactivity) of a single material, and in some cases, the

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properties of components of mixtures. A complex EI-MS spectrum represents a fingerprint

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that can be used to identify a material and define its characteristics. EI-MS is a widely utilized

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analytical technique. Thus, direct-injection EI-MS (DI-EI-MS) is an ideal methodology for

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use in quality control analyses of crude drugs or other materials containing a large number of -3ACS Paragon Plus Environment


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constituents.

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Lichens are symbiotic associations of algae and fungi found mainly in terrestrial habitats all

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over the world. Lichens produce many characteristic phenolic compounds, such as depsides,

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depsidones, and dibenzofurans, which are thought to be biosynthesized by the fungal

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component.8 Our group previously reported the structures of various secondary metabolites

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isolated from cultured lichen mycobionts.9,10,11,12 Using LC-MS/MS, we identified a variety of

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compounds produced by Cladonia species. The genus Cladonia is one of the most

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well-known and species-rich lichen genera, with ca. 400 species13 known worldwide. The

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taxonomy of Cladonia and related genera is based primarily on the morphology and

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secondary chemistry of the macroscopic primary thallus and podetia.14,15 Because of subtle or

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non-clear differences in morphological characters between species, it is difficult for

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non-taxonomists to identify these lichens. An established DI-EI-MS metabolomics method

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could prove useful in studies of the chemotaxonomy of lichens and their classification.

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In the present study, we developed a DI-EI-MS method combining characterization of

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samples with multivariate data analysis. We demonstrated the proposed method by analyzing

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various well-known lichen species within the genus Cladonia using a combined DI-EI-MS

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and statistical approach. This is the first report describing a metabolomics method based on

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DI-EI-MS.

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EXPERIMENTAL METHODS

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Extraction of lichens. Cladonia krempelhuberi was identified and provided by Mr. T. Anzai.

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Cladonia gracils (Harada no. 9098), C. pseudogymnopoda (Harada no. 9382), and C.

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ramulosa (Harada no. 9260) were provided by one of the authors, H. Harada, duplicated from

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specimens deposited at the Natural History Museum and Institute, Chiba, Japan. Voucher

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specimens are deposited at the Department of Natural Medicines and Phytochemistry, Meiji

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Pharmaceutical University, Tokyo, Japan. Samples of C. krempelhuberi (132.8 mg), C. gracils

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(101.9 mg), C. pseudogymnopoda (157.0 mg), and C. ramulosa (158.9 mg) were individually

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extracted with MeOH (approximately 5 to 7.5 mL) at 70°C for 3 h. The resulting extract was

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evaporated under vacuum, yielding 11.4, 7.1, 12.8, and 4.4 mg of residue from C.

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krempelhuberi, C. gracils, C. pseudogymnopoda, and C. ramulosa, respectively. After drying,

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DMSO was added to provide for a final residue concentration of 5 mg/mL.

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EI-MS analysis.

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heating of a wire filament through which electric current flows. In the present study, the

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electrons were accelerated at 70-20 eV in the region between the filament and the entrance to

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the ion source block. EI-MS was conducted using a double-focusing mass spectrometer (JMS

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GC-mate II; JEOL, Tokyo, Japan) equipped with a heated direct-insertion sample probe. The

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MS detector parameters used were as follows: interface temperature, 320°C; ion-source

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temperature, 280°C; ionization mode, EI; electron energy, 70-20 eV; scan speed, 0.3 s/scan;

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inter-delay, 0.2 s; scan range, m/z 50-500; accelerating voltage, 2500 V; ionization current

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(emission), 0.3 mA; variable temperature duration, 0.5-2.5 min. A 1-µL aliquot of test sample

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in dimethyl sulfoxide (DMSO) was injected into the DI-EI-MS system. A microsyringe was

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used to load a drop (1 µL) of each extract solution onto a glass target, which was then loaded

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into the direct-insertion sample probe (MS-DIP25). Each sample was analyzed by EI-MS

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three times for each independent test. Integration analysis covered the range of approximately

In the EI source, electrons are produced through thermionic emission by



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scans 60-360 of the TIC. The TIC was monitored for 2.5 min, and all of the fragment ions

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between 0.5 and 2.5 min were added. Reproducibility was confirmed by repeating these

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procedures three times.

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Statistical analysis.

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software (SAS Institute Inc., Cary, NC) for multivariate and univariate statistical analysis.

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Ions in the mass range m/z 50-500 for each sample were used for the statistical analyses. The

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m/z of each peak was represented as an integral value, rounding down to one decimal place.

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Variables were standardized with a mean of 0 and standard deviation of 1. All statistical

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analyses were carried out using JMP Pro12.2 software to identify the features contributing to

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group separation.

The resulting EI-MS data sets were imported into JMP Pro12.2

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RESULTS

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Natural biological samples are ideal materials for use in developing metabolomics

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methods due to the many constituents they contain. In a previous study, we used methanol

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(MeOH) to extract samples of Sophora flavescens Aiton for NMR-based metabolomics

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analyses.16 In the present study, MeOH was also used for extraction because it is a highly

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polar solvent. We prepared a MeOH extract of a lichen sample and then re-dissolved the dried

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extract residue with DMSO to an appropriate concentration for MS analysis. The

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reproducibility of the proposed DI-EI-MS–based metabolomics method was assessed by

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repeated analysis of the same sample. Cladonia krempelhuberi Vain. was selected for

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preliminary development of the method because it is a well-studied organism that contains

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many constituent metabolites. The reproducibility of total ion chromatograms (TICs) of

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analyses of C. krempelhuberi MeOH extracts is illustrated in Figure 1. A trough in the TIC

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was observed at scan number 90, 88, and 91 in the three trials. Only DMSO peaks were

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observed between scan 1 and the trough. The TIC and early integrated mass spectra of DMSO

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are shown in the Supporting Information (Figures S1 and S2). Early integrated mass spectra

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of lichen extracts are also shown in the Supporting Information (Figure S3). These results

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indicated that it is necessary to remove the background area, which was between scan 1 and

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the trough (integrated area (a) of Figure 1 equal to the integrated area (a) of Figure S1).

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Lichen extract peaks began to appear after the trough, at which point the solvent (DMSO) was

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volatilized and gasification and ionization were initiated. Lichen extract ion peaks were

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observed from the trough point to the end of the analysis. Samples were processed from the

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TIC trough point to the end of the scan, and the resulting data were integrated, as illustrated in

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Figure 1. The integrated mass spectrum for each TIC is shown Figure 2. The aim of integrated

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analysis was to account for variation in the point of maximum TIC intensity for each extract.

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The data shown in Figure 2A, B, and C were compatible with the data shown in Figure 1A, B,

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and C, respectively. For example, all three spectra show peaks at m/z 73, 103, 121, 163, 199, -7ACS Paragon Plus Environment


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227, 253, 313, and 368. In addition, all three spectra show the same base peak, at m/z 73.

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These results confirm the reproducibility of the proposed DI-EI-MS method.

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Next, we used the established DI-EI-MS metabolomics method to classify various

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Cladonia lichen samples. Figure 3 shows TICs for extracts of four morphologically similar

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lichen species: C. krempelhuberi, C. gracilis (L.) Willd. subsp. turbinata (Ach.) Ahti, C.

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pseudogymnopoda Asah., and C. ramulosa (With.) J.R. Laundon. A single maxima peak (0.86

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min.) and complex peak (1.12-1.32 min.) were observed in the TICs of C. krempelhuberi

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extracts (Figure 3A). A single maxima peak (0.89 min.) was observed in the TICs of C.

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gracilis extracts (Figure 3B). By contrast, two maxima peaks (1.04 and 1.37 min.) were

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observed in the TIC of the C. pseudogymnopoda extract (Figure 3C), and three maxima peaks

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(0.90, 1.08 and 1.43 min.) were observed in the TIC of the C. ramulosa extract (Figure 3D). A

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particular region of each TIC was subjected to integrated analysis in order to facilitate

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distinguishing the samples. The peak appearing immediately prior to the first trough in each

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of the TICs was attributed to the solvent front. The region of each TIC spanning

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approximately scans 60-360 was integrated for analysis, and the integrated spectra are shown

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in Figure 4. Each integrated mass spectrum exhibited characteristic peaks. For example, a

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peak at m/z 368 with a relative intensity of 14% was observed in the C. krempelhuberi

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integrated spectrum (Figure 4A), as well as those of the other lichen extracts. Most of the

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high-intensity peaks in the C. gracilis integrated spectrum were below m/z 100 (Figure 4B).

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By contrast, the C. pseudogymnopoda spectrum exhibited several exclusive peaks (Figure 4C),

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such as the base peak occurring at m/z 69 rather than m/z 73, as observed in the other spectra.

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Other major peaks with relative intensity >30% were observed at m/z 199, 227, 230, 253, and

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258. Major peaks observed in the C. ramulosa integrated spectrum were m/z 199 and 227,

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with relative intensity >20% (Figure 4D). From these results, it was possible to differentiate

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all four lichen samples.

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In general, because the pattern and intensity of fragment ion peaks change with variations -8ACS Paragon Plus Environment

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in the electron energy in DI-EI-MS, different spectra can be generated for comparison with

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spectra collected under fixed standard conditions, providing a variety of perspectives for

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mixture analyses. Therefore, mixtures containing different components can be more readily

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distinguished based on differences in the fragment pattern. We varied the electron energy to

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determine the most suitable conditions for differentiating lichen species based on metabolic

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components. When a single pure compound is measured for determining its molecular weight,

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the electron energy is commonly set at 70 eV. We collected integrated mass spectra of an

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extract of C. pseudogymnopoda at electron energies of 70, 50, 30, and 20 eV (Figure 5).

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Variation of the electron energy resulted in characteristic changes in the intensity of two

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fragment ions, at m/z 368 and 199. However, the lichen species could also be distinguished by

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statistically analyzing the differences in a number of lower-intensity peaks. Minor and

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undistinguishable peaks were extracted for use in classification based on the absolute ion

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intensity at each m/z integer value. The results of one-way analysis of variance of the peaks at

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m/z 199, 230, 264, and 368 are shown in Figure 6. Conspicuous high-intensity peaks were

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observed in the C. pseudgymnopoda spectra at m/z 199 and 230 (Figure 6A and B) and in the

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C. kremperuphuberi spectra at m/z 264 and 368 (Figure 6C and D). When the ion voltage was

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high, the absolute ion intensity was high in the spectra for each sample. Table 1 showed the

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significantly different of the median difference for C. kremperuphuberi or C.

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pseudgymnopoda except for 20 eV. However, at an ion voltage of 50 or 30 eV, the intensity of

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the ions at m/z 199, 264, and 368 in the C. ramulosa spectra increased (Figure 6A, C, and D),

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demonstrating that low-intensity ions can be clearly distinguished by varying the electron

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energy. An electron energy of 70 eV is thus not always the best setting for multivariate

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analysis and sample characterization. The ability to clearly distinguish four different lichen

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species within the same genus demonstrates the utility of the DI-EI-MS method. In contrast to

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the above results, lowering the electron energy increased the difficulty of distinguishing one

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of the specimens. As such, it may be necessary in some cases to analyze a larger number of -9ACS Paragon Plus Environment


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specimens in order to increase the statistical power of the analysis and permit distinguishing

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between species.

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Figure 7 shows the principal component analysis score plots of the DI-EI-MS data for

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lichen MeOH extracts. These data were collected for m/z 50-500 at four different electron

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energies (70, 50, 30, and 20 eV) for each extract. Components 2 and 3 enabled the

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classification of lichen species. Component 2 enabled discrimination between C.

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kremberihuber and the other samples. Combining the data for components 2 and 3

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approximated the plots for both C. pseudogymnopoda and C. ramulosa. These results suggest

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that the constituents of C. pseudogymnopoda and C. ramulosa are very similar. However, to

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our knowledge, no reports of the constituents of C. pseudogymnopoda or C. ramulosa have

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been published.

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DISCUSSION

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Many analytical approaches focus on either a specific class of constituents or a single

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compound important for distinguishing between samples of natural materials. By employing

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multivariate analysis, is not necessary to initially separate and isolate compounds of interest

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using techniques such as column chromatography. To date, combining multivariate analysis

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with various spectroscopic techniques (e.g., MS, NMR) has been restricted primarily to plant

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metabolomics studies.17,18,19 NMR and MS fingerprinting approaches provide information

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useful for classifying species and determining their inter-relationships.20 The number of plant

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metabolomics papers published has increased as rapidly as the number of general

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metabolomics papers published has increased, comprising approximately 20% of the annual

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total of metabolomics-related papers.5

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In the case of plant metabolomics studies, only a portion of the constituent metabolites in

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a sample of interest are typically isolated and identified. Instead, most research focuses on -10ACS Paragon Plus Environment

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only the major components present in relatively high abundance and/or principle components

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that function as active compounds. Most minor components are not studied. Therefore, the

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exhaustive analysis of all components expressed could be considered more characteristic or

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inherent of a plant species than analyses only of components that are already known.

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DI-EI-MS is an ideal approach for metabolic profiling to classify or distinguish species.

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As shown in Figures 1 and 2, we confirmed the reproducibility of this approach by repeated

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analysis of the same sample. In addition, DI-EI-MS can reveal minute differences between

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closely related species. Overall, the morphologically similar lichen species examined in the

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present study exhibited similar mass spectra profiles. However, a number of fragment peaks

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differing in mass and/or intensity were observed (Figures 3 and 4). Fragmentation in

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DI-EI-MS usually occurs in a characteristic pattern; therefore, it can be difficult to determine

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from which compound a particular signal was derived in analyses of multi-component

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samples such as crude drugs. The particular signals assigned in a DI-EI-MS analysis must

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therefore be characteristic of the species or compound of interest. When distinction between

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species is the primary aim, it is important that characteristic signals appear regardless of the

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principle and/or secondary components in the sample material. Because multi-component

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analyses will produce a large number of fragment peaks, statistical analysis of the data can

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identify characteristic signals that can be used to distinguish and/or identify the species.

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In the present study, we established a DI-EI-MS method for use in MS-based metabolomics

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analyses and species characterization. We examined the effect of varying certain analytical

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conditions (such as ion source temperature) on the TIC in order to develop a methodology

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that would enable the elucidation of differences in mass spectra between morphologically

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similar lichen species. Prukała et al. reported the effect of differences in ESI-MS cone voltage

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(CV) in a study of a series of isomeric compounds. The total number of fragment ion peaks

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derived from each compound depends on the CV.21 Takayama et al. also compared the EI-MS

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patterns obtained at electron energies of 15-70 eV (Supporting Information, Figure S4).22 -11ACS Paragon Plus Environment


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These reports suggest that minor components could have revealed peaks without reducing

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fragments, relatively. In the same way, in the present study, we found that varying the electron

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energy in EI-MS enabled us to control the total number of fragment ions, thus markedly

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enhancing the ability to detect minor components (Figures 5, 6, and 7). This feature of the

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DI-EI-MS method could be useful for determining the species of a sample of interest. The

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method could be applied to the analysis of a variety of samples by conducting analyses over a

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range of a particular conditions, such as ion source temperature, for example. Distinctions

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between spectra may become clearer still by altering the extraction method or mass range.

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Even two samples of the same species or material of interest can vary with respect to

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detectable components. With respect to natural materials, these variations can result from

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differences in multiple factors in the natural environment. Differences in component

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composition can also result from differences in collection location, harvest time, and other

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factors. Although samples may be of the same species, because they are natural materials, it is

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necessary to statistically analyze and compare averaged data obtained from a large number of

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specimens. The DI-EI-MS method described here enables both qualitative and possible

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quantitative determinations of all components within a specimen of interest based on varying

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the analytical parameters to maximize fragment ion detection. In other words, each sample

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produces a characteristic complex spectral pattern.

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CONCLUSIONS

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We demonstrated the utility of a DI-EI-MS–based method for applications in

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metabolomics and natural materials characterization research. By averaging MS data for a

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number of samples and analyzing it statistically, we could detect signals characteristic of the

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sample of interest. In future studies, we will examine the effect of analyzing a greater number

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of specimens of the same type. Future studies will also focus on optimization of both the -12ACS Paragon Plus Environment

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extraction and analytical conditions. This is the first study reporting the combined use of

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DI-EI-MS and multivariate analysis to identify or characterize samples without the need for

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prior isolation of high-abundance constituents or bioactive compounds via GC-EI-MS or

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LC-MS.

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ACKNOWLEDGEMENTS

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We thank Mr. Tadao Anzai (Zelg Co., Ltd.) for supplying the Cladonia krempelhuberi and

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Dr. Mitsuo Takayama (Yokohama City University) for supplying the EI-MS spectral data

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(Supporting Information, Figure S4).



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FIGURE LEGENDS

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Figure 1.

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extract.

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Double-headed dashed arrows (a) indicate areas removed from the integrated data. These

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areas do not contain sample fragments (Supporting Information, Figure S3). Double-headed

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solid arrows (b) denote the range of integrated data. The integrated mass spectra are shown in

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Figure 2A, B, and C show TICs for three independent measurements of the same sample for

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assessing reproducibility.

Total ion chromatograms (TICs) of Cladonia kremberihuberi methanol

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Figure 2. Integrated mass spectra of Cladonia kremberihuberi methanol extract.

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A, B, and C show integrated mass spectra of the total ion chromatograms shown in Figure 1A,

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B, and C, respectively.

14 15

Figure 3.

16

A, Cladonia kremberihuberi; B, C. gracilis; C, C. pseudogymnopoda; D, C. ramulosa.

17

Double-headed arrows denote range of integrated data. The integrated mass spectra are shown

18

in Figure 4A, B, C, and D and represent three independent TICs of the corresponding sample.

Total ion chromatograms (TICs) of lichen methanol extracts.

19 20

Figure 4.

21

A, Cladonia kremberihuberi; B, C. gracilis; C, C. pseudogymnopoda; D, C. ramulosa.

Integrated mass spectra of lichen methanol extracts.

22 23

Figure 5. Integrated mass spectra of Cladonia pseudogymnopoda extract acquired at

24

different electron energies.

25

A, 70 eV; B, 50 eV; C, 30 eV; D, 20 eV.

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1

Figure 6.

2

Specific ratio of mass and ionization at m/z 199 (A), 230 (B), 264 (C), and 368 (D). Each plot

3

shows the absolute ion intensity at each m/z value at the following electron energies: , 70

4

eV; ●, 50 eV; ●, 30 eV; and ●, 20 eV. G, Cladonia gracilis; K, C. kremberihuber; P, C.

5

pseudogymnopoda; R, C. ramulosa. Data are expressed as representative of three independent

6

experiments.

One-way analysis of variance of lichen data.

7 8

Figure 7.

9

samples.

Principal component analysis (PCA) based on DI-EI-MS data of lichen

10

●, Cladonia gracilis; ○, C. kremberihuber; ▲, C. pseudogymnopoda; △, C. ramulosa.

11

These data were collected for m/z 50-500 at four different electron energies (70, 50, 30, and

12

20 eV) for each lichen MeOH extract.

13 14



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1

REFERENCES

2

(1)

3

Johanningsmeier, S. D.; Harris, G. K.; Klevorn, C. M. Annu. Rev. Food Sci. Technol. 2016, 7 (1), 413–438.

4

(2)

Aretz, I.; Meierhofer, D. Int. J. Mol. Sci. 2016, 17 (5), 632.

5

(3)

Van Der Kooy, F.; Maltese, F.; Young, H. C.; Hye, K. K.; Verpoorte, R. Planta Med.

6 7

2009, 75 (7), 763–775. (4)

8 9

Gad, H. A.; El-Ahmady, S. H.; Abou-Shoer, M. I.; Al-Azizi, M. M. Phytochem. Anal. 2013, 24 (1), 1–24.

(5)

10

Ernst, M.; Silva, D. B.; Silva, R. R.; Vêncio, R. Z. N.; Lopes, N. P. Nat. Prod. Rep. 2014, 31 (6), 784–806.

11

(6)

Tureček, F.; Hanuš, V. Mass Spectrom. Rev. 1984, 3 (1), 85–152.

12

(7)

Biemann, K. Mass Spectrometry; McGraw-Hill: New York, 1962.

13

(8)

Culberson, C. F. Chemical and Botanical Guide to Lichen Products; The University of

14 15

North Carolina Press: Chapel Hill, NC, 1969. (9)

16 17

(10)

(11)

Kinoshita, K.; Yamamoto, Y.; Takatori, K.; Koyama, K.; Takahashi, K.; Kawai, K. I.; Yoshimura, I. J. Nat. Prod. 2005, 68 (12), 1723–1727.

(12)

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Kinoshita, K.; Togawa, T.; Hiraishi, A.; Nakajima, Y.; Koyama, K.; Narui, T.; Wang, L. S.; Takahashi, K. J. Nat. Med. 2010, 64 (1), 85–88.

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Kinoshita, K.; Fukumaru, M.; Yamamoto, Y.; Koyama, K.; Takahashi, K. J. Nat. Prod. 2015, 78 (7), 1745–1747.

18 19

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(13)

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Kirk, P. M.; Cannon, P. F.; David, J. C.; Staplers, J. A. Dictionary of the Fungi, 9th Editio.; CABI Publishing: Wallingford, 2001.

25

(14)

Ahti, T. Flora Neotrop. Monogr. 2000, 78, 1–362.

26

(15)

Thomson, J. W. The lichen genus Cladonia in North America; University of Toronto -16ACS Paragon Plus Environment

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Press: Toronto, Ontario, Canada., 1968. (16) Suzuki, R.; Hasuike, Y.; Hirabayashi, M.; Fukuda, T.; Okada, Y.; Yoshiaki, S. Nat. Prod.

3

Commun. 2013, 8 (10), 1409–1412.

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Eisenreich, W.; Bacher, A. Phytochemistry 2007, 68 (22), 2799–2815.

5

(18)

Saito, K.; Matsuda, F. Annu. Rev. Plant Biol. 2010, 61 (1), 463–489.

6

(19)

Lv, H. Mass Spectrom. Rev. 2013, 32 (2), 118–128.

7

(20)

Safer, S.; Cicek, S. S.; Pieri, V.; Schwaiger, S.; Schneider, P.; Wissemann, V.; Stuppner,

8 9

H. Phytochemistry 2011, 72 (11–12), 1379–1389. (21)

10 11 12

Prukała, D.; Prukała, W.; Koczorowski, R.; Khmelinskii, I. V; Sikorska, E.; Sikorski, M. Rapid Commun. Mass Spectrom. 2008, 22 (3), 409–416.

(22)

Takayama, M.; Fukai, T.; Nomura, T.; Nojima, K. Int. J. Mass Spectrom. Ion Process. 1990, 96 (2), 169–179.

13 14 15 16 17 18 19 20 21 22 23 24 25 26 -17ACS Paragon Plus Environment


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1

Table 1.

Page 18 of 34

Significance of differences by one-way analysis of variance of lichen data.

2 m/z

Sample 1

Sample 2

p-value

199

P

K

0.0221

P

G

0.0328

P

R

0.0328

P

K

0.0221

P

G

0.0328

P

R

0.0565

K

G

0.0227

K

P

0.0227

K

R

0.0227

K

G

0.0227

K

P

0.0227

K

R

0.0227

230

264

368

3 4

The median difference between K and P were determined using the Steel test. These were not

5

included the data of 20 eV.

6


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A

TIC Intensity

a

B

TIC Intensity

a

C

b

b

TIC Intensity

a

b

Figure 1

-19-

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A

B

C

Figure 2

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A

TIC Intensity

B

TIC Intensity

C

D

TIC Intensity

TIC Intensity

Figure 3

-21-



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

A

B

C

D

Figure 4


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A

70 70 eV eV

B

50 50eV eV

C

30eV eV 30

D

20 20eV eV

Figure 5

-23-



A The absolute ion intensity

m/z 199

B The absolute ion intensity

m/z 230

C The absolute ion intensity

m/z 264

D

m/z 368

The absolute ion intensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Figure 6

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Figure 7

-25-



Graphical Abstract

TIC Intensity

Integration


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Finger-Printing Classification


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A

TIC Intensity

a

B

C

b

TIC Intensity

a

b

a

b

TIC Intensity

Figure 1

-19-



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

A

B

C

Figure 2

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A

TIC Intensity

B

TIC Intensity

C

D

TIC Intensity

TIC Intensity

Figure 3

-21-



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A

B

C

D

Figure 4


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A

70 70 eV eV

B

50 50eV eV

C

30eV eV 30

D

20 20eV eV

Figure 5

-23-



A The absolute ion intensity

m/z 199

B The absolute ion intensity

m/z 230

C The absolute ion intensity

m/z 264

D

m/z 368


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Figure 6

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Figure 7

-25-



Graphical Abstract

TIC Intensity

Integration


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Finger-Printing Classification


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Establishment of a Direct-Injection Electron Ionization-Mass

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