Simultaneous Profiling of Lysoglycerophospholipids in Rice (Oryza

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Simultaneous profiling of lysoglycerophospholipids in rice (Oryza sativa L.) using direct infusion-tandem mass spectrometry with multiple reaction monitoring Dong Kyu Lim, Changyeun Mo, Long Nguyen Phuoc, Giyoung Kim, and Sung Won Kwon J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b00148 • Publication Date (Web): 01 Mar 2017 Downloaded from http://pubs.acs.org on March 13, 2017

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Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Journal of Agricultural and Food Chemistry

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Simultaneous profiling of lysoglycerophospholipids in rice (Oryza sativa L.)

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using direct infusion-tandem mass spectrometry with multiple reaction

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monitoring

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Dong Kyu Lim†, Changyeun Mo‡, Nguyen Phuoc Long†, Giyoung Kim‡, Sung Won

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Kwon*,†,§

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National University, Seoul 08826, Republic of Korea

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Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul

National Institute of Agricultural Sciences, Rural Development Administration,

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Jeonju 54875, Republic of Korea

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§

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Republic of Korea

Plant Genomics and Breeding Institute, Seoul National University, Seoul 08826,

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*Corresponding author: Sung Won Kwon (Email address: [email protected])

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Abstract

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White rice is the final product after the hull and bran layers have been removed

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during the milling process. Although lysoglycerophospholipids (lysoGPLs) are only

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occupy a small proportion in white rice, they are essential for evaluating rice

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authenticity and quality. In this study, we developed a high-throughput and targeted

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lipidomics approach that involved direct infusion-tandem mass spectrometry with

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multiple reaction monitoring to simultaneously profile lysoGPLs in white rice. The

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method is capable of characterizing 17 lysoGPLs within one minute. In addition,

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unsupervised and supervised analyses exhibited a considerably large diversity of

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lysoGPL concentrations in white rice from different origins. In particular, a

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classification model was built using identified lysoGPLs that can differentiate white

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rice from Korea, China, and Japan. Among the discriminatory lysoGPLs, for the

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lysoPE(16:0) and lysoPE(18:2) compositions, there were relatively small within-

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group variations, and they were considerably different among the three countries. In

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conclusion, our proposed method provides a rapid, high-throughput, and

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comprehensive format for profiling lysoGPLs in rice samples.

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Keywords: Oryza sativa L., lysoglycerophospholipid, discriminatory marker, direct

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infusion-mass spectrometry, partial least squares discriminant analysis

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INTRODUCTION

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Phospholipids (PLs), an important category of lipids, are known to be involved in

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various cellular processes such as signal transduction, membrane transport, and

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proliferation 1. Currently, PLs can be classified into glycerophospholipids (GPLs) and

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sphingophospholipids, in which the basic difference is the backbone molecule. GPLs

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consist of various lipid species that share a unique molecular structure that includes

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a polar head, a common glycerol backbone, and fatty acid chains of different lengths

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together with units of unsaturation 2. The main GPLs include phosphatidylcholine

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(PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine

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(PS), and phosphatidylglycerol (PG), among others 3. Of note, typical GPLs have two

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different acyl chains, whereas their lyso forms consist of only one acyl chain in either

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the sn-1 or sn-2 position of the glycerol backbone 4.

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Recently, rice has been considered to be a more attainable source of dietary PLs 4-6.

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In addition, lysoglycerophospholipids (lysoGPLs), including lysoPCs and lysoPEs,

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are the main lipid components found in white rice and are considered to be a rice

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quality parameter that is associated with the texture, rheological property, storage

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stability, and glycemic index 7-8. However, the association between the lysoGPL

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concentration and environmental factors, such as fertility, growth temperature, and

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precipitation, has yet to be explored 4.

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Since the fragmentation patterns of PLs and lysoGPLs in both positive and negative

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ion modes of electrospray ionization-mass spectrometry (ESI-MS) are predictable,

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various MS-based techniques have been developed to characterize the composition

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of PLs in plant and biological fluids, including rice samples 2, 9-10. High-performance

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liquid chromatography (HPLC) coupled with MS is well-developed, and it is currently

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the method of choice for the analysis of rice PLs 4. However, despite its notable

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sensitivity and reproducibility, one major weakness of LC-MS-based methods is the

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time-consuming separation process, which requires approximately 15-60 minutes

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per sample analysis 2. However, a recently developed method called direct infusion-

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tandem mass spectrometry (DI-MS/MS) has several advantages over

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chromatography-coupled MS 11-14. DI-MS/MS pointedly and expeditiously provides

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concise data and comprehensive MS/MS spectra via multiple fragmentation

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techniques, which include product-ion analysis and neutral loss scan. Therefore, it is

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suitable for global determination of many lipid species. Most importantly, DI-MS/MS

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facilitates high-throughput analysis, which can eventually accelerate the

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comprehensive understanding of lipids in biological systems 15.

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In the current paper, we developed a sophisticated analytical method for

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simultaneously characterizing main lysoGPLs in white rice samples using DI-ESI-

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MS/MS via the multiple reaction monitoring (MRM) approach. MRM has been utilized

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for high-throughput and quantitative analysis in proteomics and metabolomics due to

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its high sensitivity, specificity, and precision 16-17. The proposed method was

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implemented to analyze lysoGPLs in white rice from Korea, China, and Japan. Using

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this approach, we were able to characterize 17 lysoGPLs (6 lysoPCs, 7 lysoPEs, and

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4 lysoPGs) within one minute for each sample with high specificity and sensitivity.

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Furthermore, we also applied unsupervised hierarchical clustering, principal

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component analysis (PCA), and supervised partial least squares discriminant

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analysis (PLS-DA) to explore the divergence of the lysoGPL distribution in white rice

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samples originating from different countries.

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MATERIALS AND METHODS

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Materials and reagents

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Different white rice samples were randomly collected from local markets in Korea,

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China, and Japan. More details about the samples are available in Table 1. The

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collected samples were stored at −70°C until processed for analysis. Caffeine was

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purchased from Sigma-Aldrich (St Louis, MO, USA). All HPLC grade solvents,

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including acetonitrile, methanol, and isopropanol, were obtained from J. T. Baker

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(Avantor, Phillipsburg, NJ, USA). Polytetrafluoroethylene (PTFE) 0.20 µm pore

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syringe filters were purchased from Advantec (Tokyo, Japan).

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Sample preparation

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White rice samples from Korea, China, and Japan were freeze-dried in a freeze drier

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(Operon, Gimpo, Korea). Then, all samples were pulverized. The powdered samples

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were sieved using two sieves (250 µm and 125 µm) and extracted as previously

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described 7. Concisely, 150 mg of powder with 1 mg caffeine as the internal standard,

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was initially extracted with 6 mL of 75% isopropanol at 90°C for 2 h in a water bath.

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The individual extract was then centrifuged at 16,000 g for 5 min. Finally, 1 mL of

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supernatant was filtered using a PTFE syringe filter and transferred to a vial. Five

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mixtures that contained the same volume of every samples were used as quality

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control (QC) samples.

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DI-MS/MS and DI-MRM analysis conditions

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An Agilent triple-quadrupole mass spectrometry 6460 system (Agilent, CA, USA)

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equipped with an ESI ion source was employed to perform all experiments. The

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analyses were conducted in positive (lysoPCs) and negative (lysoPEs and lysoPGs)

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ion modes. A 50% acetonitrile flow with a velocity of 0.2 mL/min was maintained to 5 ACS Paragon Plus Environment

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reduce the contamination of the ion source during the sample injection process. The

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analysis time was approximately one min for each sample with an injection volume of

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5 µL. All experiments were performed using a random sequence to avoid technical

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bias. The DI-MS/MS product-ion mode was initially used for detecting the

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fragmentation patterns of targeted lysoGPL species, and the fragments with the

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highest intensities were selected for MRM transition setting. The parameters for the

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mass spectrometry were set as follows: scan time = 200 scans/sec, cell accelerator

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voltage = 7 V, fragmentor voltage = 135 V, gas flow = 11 L/min, gas temperature =

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325°C, nebulizer = 40 psi, and capillary voltage = 4 kV. Pure nitrogen (99.99%) was

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utilized as the collision, nebulizing, and drying gas for the system. A mass range of

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m/z 50 to m/z 1,000 was acquired at a collision energy of 20 eV for both positive and

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negative ion modes. The working conditions for DI-MRM followed the settings of DI-

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MS/MS. The information on the precursor ions and product ions from the MRM

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experiments is provided in Table 2. Only lipid species with relative standard

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deviations lower than 10% in the QC samples were used for statistical analysis. The

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lipid identification was confirmed using LipidBlast, METLIN, and our in-house library

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18-19

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Data processing and univariate statistical analysis

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DI-MRM-MS data of all targeted compounds were manually detected to guarantee

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that only true analytical spectra were further processed. All data were processed

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using Agilent Mass Hunter Workstation software version B.06.00. Relative standard

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deviations (RSDs) from the QC samples were employed to assess the precision. The

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RSD (%) of a specific feature was calculated by dividing the sample standard

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deviation for the sample mean and multiplying by 100. All processed data were

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furthered scaled using the Pareto scaling method prior to univariate and multivariate

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statistical analysis. Analysis of Variance (ANOVA) and Fisher’s LSD post-hoc test

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were performed. A p-value of < 0.05 and a false discovery rate (FDR) of < 0.1 were

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used as the level of statistical significance.

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Multivariate Data Analysis

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The Pareto-scaled data were utilized for all multivariate analyses. Unsupervised

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hierarchical clustering and PCA were first performed to verify the segregation

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tendency of different groups. Thereafter, a prediction model was built using PLS-DA

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followed by a 10-fold cross-validation and 1,000-time permutation test. Any features

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that had a variable importance in projection (VIP) score of > 1, a p-value of < 0.05,

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and a false discovery rate (FDR) of < 0.1 in the univariate analysis were identified as

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discriminatory markers, which are mainly responsible for the separation among white

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rice samples from Korea, China, and Japan. A random forest classifier was also

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applied for classifying the samples. All analyses were performed using

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Metaboanalyst 3.0, which is an online platform for comprehensive metabolomics

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data analysis 20.

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RESULTS AND DISCUSSION

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Characterization and targeted lipid profiling of white rice lysoGPLs

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White rice is what remains after removing the hull and bran layer of rough rice during

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the milling process 21. The quantity of PLs in white rice is much lower than in the

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whole grain. However, the nutritional impact of PLs in white rice has been

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acknowledged because white rice is the most consumed form of rice 22. Furthermore,

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the PL composition, especially lysoGPLs (approximately 50% of starch lipids), is

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considered to be an important factor that affects rice quality 4. Several efforts have 7 ACS Paragon Plus Environment

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been made to analyze lysoGPLs in rice starch using thin layer chromatography, gas

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chromatography, and liquid chromatography coupled with mass spectrometry 7, 23.

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However, previous studies only focused on characterizing lysoPCs and lysoPEs.

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Additionally, there have been no reports on the divergence of lysoGPLs in white rice

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samples that originate from different countries. In this study, we not only

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characterized lysoPCs, lysoPEs, and lysoPGs but also investigated the diversity of

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lysoGPL components in white rice samples from Korea, China, and Japan. In

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particular, this study implemented the novel approach of direct infusion-multiple

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reaction monitoring mass spectrometry (DI-MRM-MS), which requires a low amount

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of sample and shortens the analysis time but provides concise data with high

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sensitivity and specificity.

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Since lysoPCs and lysoPEs are the two main lysoGPLs in white rice, our

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experiments focused on characterizing lipid species that belong to these two classes

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performed. As a dominant type of starch lipid located in rice endosperm, lysoPCs

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constitute approximately 13-24% of the total starch lipids 24. It is of importance to

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note that lysoPC(16:0) is ubiquitously present in the endosperm of rice, whereas

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lysoPC(18:0) is primarily located in the core of the endosperm. However,

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lysoPC(18:2) and lysoPC(18:1) are prominent in the outer region of the endosperm

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endosperm. lysoPEs have been thought to have a close association with the quality

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of rice, although its role was originally known for cell-mediated signaling and specific

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enzyme activation 26. In a recent report that evaluated the unintended effects of

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transgenic rice, lysoPEs appeared vulnerable to environmental changes 27. However,

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a previous study applied a non-targeted approach without using a proper extraction

. Additionally, characterization of the unknown lysoPGs in white rice was also

. Similar to lysoPCs, lysoPEs are also a major type of starch lipid in the rice

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method for lysoGPLs in rice 7. lysoPGs have been regarded as minor compounds in

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rice endosperms, and there has been no targeted analysis of lysoPGs in white rice.

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The predominant bound fatty acids in lysoGPLs include myristic acid (C14:0, 0.2%),

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palmitic acid (C16:0, 15.6%), palmitoleic acid (C16:1, 0.2%), stearic acid (C18:0,

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1.4%), oleic acid (C18:1, 39.4%), linoleic acid (C18:2, 40.6%), and linolenic acid

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(C18:3 1.5%) 22. Collectively, we targeted six important compounds that are reported

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as follows: lysoPC(14:0), lysoPC(16:0), lysoPC(16:1), lysoPC(18:0), lysoPC(18:1),

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and lysoPC(18:2)

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lysoPE(18:0), lysoPE(18:1), lysoPE(18:2), and lysoPE(18:3) were also selected. In

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particular, lysoPG(14:0), lysoPG(16:0), lysoPG(18:1), and lysoPG(18:2), which could

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be detected in our preliminary study, were also included in the current investigation.

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The preliminary study and the characterizations of lysophosphatidylinositol (lysoPI),

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lysophosphatidylserine (lysoPS), and lysophosphatidic acid (lysoPA) are provided in

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Supplementary file 1. Detailed information regarding the targeted lipid species are

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provided in Table 2. The instrument stability was estimated via the RSDs of the

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internal standard’s spectrum height (m/z of 195.1 to 138.1 in positive ion mode and

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m/z of 179.0 to 164.0 in negative ion mode) of all analyzed samples. The overall

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RSD was 1.323% in positive ion mode and 2.641% in negative ion mode.

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Structure elucidation of the targeted lipids using DI-MS/MS

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Lysophohsphatidylcholine (lysoPC)

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Figure 1 and Supplementary file 2 show the product-ion mode DI-MS/MS spectra

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of the identified lysoPCs. Theoretically, every lysoPC species shares a common

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prominent fragment ion at m/z 184.1 [Phosphocholine]+, which is the specific head

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group of lysoPCs. In our experiment, most lysoPCs were revealed at m/z 184.1 as

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. For the same reason, lysoPE(14:0), lysoPE(16:0), lysoPE(16:1),

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the major spectrum. The spectrum at m/z 258.1 [M-fatty acid-OH]+ was also

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observed in four species, except lysoPC(16:1) and lysoPC(18:0), which were

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properly generated from the dissociation of the specific fatty acid from the precursor

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ion. LysoPC(16:0), lysoPC(18:0), lysoPC(18:1), and lysoPC(18:2) all had unique

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spectra generated due to the dissociation of [Phosphocholine]+ from their protonated

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precursor ions at m/z 313.3, 341.3, 339.3, and 337.3. [Phosphocholine]+ was later

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selected as a product ion for DI-MRM analysis. The presence of [M+H-H2O]+

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revealed additional information about the single acyl chain of each lysoPC. Finally,

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six lysoPCs were confirmed, as listed in Table 2. In addition, lysoPC(16:0) was

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determined to be the most abundant type of lysoPC. Their concentrations decline in

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the following order: lysoPC(18:2), lysoPC(18:1), lysoPC(14:0), lysoPC(18:0),

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lysoPC(16:1). Our findings are generally similar to a previous study, except for the

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deviation in concentration of lysoPC(16:0) 23.

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Lysophosphatidylethanolamine (lysoPE)

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The spectra of identified lysoPEs for product-ion mode DI-MS/MS are shown in

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Figure 1 and Supplementary file 3. Except for lysoPE(16:1), every lysoPE species

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exhibited a common prominent fragment ion at m/z 196.1, [M-H-fatty acid]-. The

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spectrum with the highest intensity for every lysoPE, except lysoPE(18:0), was [fatty

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acid-H]-. Therefore, [fatty acid-H]- was later selected as a product ion for DI-MRM

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analysis. The [fatty acid-H]- ions of lysoPE(14:0), lysoPE(16:0), lysoPE(16:1),

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lysoPE(18:0), lysoPE(18:1), lysoPE(18:2), and lysoPE(18:3) were [myristic acid-H]-,

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[palmitic acid-H]-, [palmitoleic acid-H]-, [stearic acid-H]-, [oleic acid-H]-, [linoleic acid-

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H]-, and [linolenic acid-H]-, respectively. Noticeably, there were two spectra that were

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properly comprised of other compounds in the lysoPE(18:0) spectra. Finally, seven

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lysoPEs were confirmed (Table 2). Similar to lysoPC(16:0), lysoPE(16:0) was 10 ACS Paragon Plus Environment

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determined to be the most abundant type of lysoPE. Their concentrations decline in

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the following order: lysoPE(18:2), lysoPE(18:1), lysoPE(14:0), lysoPE(18:3),

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lysoPE(18:0), lysoPE(16:1).

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Lysophosphatidylglycerol (lysoPG)

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Figure 1 and Supplementary file 4 show the specific fragmentation patterns of the

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lysoPGs. Every lysoPG has three unique spectra, namely, [fatty acid-H]-, m/z 153.1

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for [M-fatty acid-glycerol head group]-, and m/z 227.2 for [M-H-fatty acid]-. Finally,

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four lysoPGs were identified as listed in Table 2. LysoPG(16:0), similar to

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lysoPC(16:0) and lysoPE(16:0), were of the highest proportion in the analyzed

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samples. However, the concentration of lysoPG(18:2), lysoPG(18:1), and

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lysoPG(14:0) decline in this order.

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Variation of lysoGPLs in rice samples from different countries

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The unsupervised cluster analyses (Euclidean distance measurement, complete

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clustering algorithm) initially showed a relative lysoGPL divergence for the white rice

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samples from Korea, China, and Japan (Supplementary file 5). In particular, there

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were two compact clusters of white rice samples found from Korea (cluster 1) versus

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China and Japan (cluster 2) using the processed lysoPE data. Next, PCA and PLS-

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DA were conducted to investigate the divergence of the white rice samples from the

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three countries. In positive ion mode, a PLS-DA model from lysoPCs processed data

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was shown to be a good model for classifying white rice samples from different

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origins. The 2D score plots uses two most important components of PCA and PLS-

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DA, which are shown in Figure 2. A considerable overlap of 95% confident intervals

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(CIs) among the three groups was observed. The prediction accuracy, goodness-of-

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fit (R2), class prediction ability (Q2), and B/W-based p-value were 0.85, 0.82, 0.70,

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and