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

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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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†

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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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4

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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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Simultaneous Profiling of Lysoglycerophospholipids in Rice (Oryza

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