Liquid Chromatography Mass Spectrometry-based High-Throughput, Unbiased Profiling of Upland and Lowland Rice Varieties Cultivated in Sabah

Hui Jun Ang, Ken Heng Mak, Mok Sam Lum

Transactions on Science and Technology, 7(3-2), 137 - 146.

Back to main issue

Oryza sativa L. commonly known as rice is one of the most cultivated cereal worldwide which sustained over 50% of the world’s population. Malaysian rice cultivated in 2 systems namely lowland (irrigated rice) and upland (rain-fed rice). Rice varieties adapted different growth systems differ substantially from each other agronomic traits. It is challenging to distinguish from each other’s using their morphological characteristics. Therefore, we aimed to propose a high-resolution mass spectrometry-based high-throughput, unbiased approach to distinguish rice species (upland or lowland cultivation) using the chemotaxonomy approach using whole rice (including barns). From our preliminary results, orthogonal partial least square discriminant analysis (OPLS-DA), a supervised pattern-recognition technique, successfully discriminates the differently cultivated rice species with R2X, R2Y, and Q2 as 0.309, 0.914, and 0.871, respectively. Dendrogram demonstrates rice species were discerned from another. There are some plant-related metabolites and phospholipids species significantly differed between the cultivated rice species. Among the identified metabolites, the upland whole rice demonstrated a higher ratio of linoleic acid esters and glycerolipids including diacylglycerol lipids (DG), monoacylglycerol lipds (MG), and phosphocholine lipids (PC) compared to lowland whole rice. Interestingly, triacylglycerolipids were reduced in the upland as compared to lowland whole rice. It is suspected the rice expressed different levels of lipids contents play essential roles in rice germinations at adopted lands. Throughout such an approach, a systematic, scientific, evident-based approach could be established and proved an insight for the researcher to distinguish rice species and avoid nutrition facts exaggeration of specific rice species over the others.

KEYWORDS: Upland rice; Lowland rice; OPLS-DA; High-throughput; Unbiased; Profiling

Download this PDF file

  1. Begum, M. A., Shi, X., Tan, Y., Zhou, W., Hannun, Y., Obeid, L., Mao, C. & Zhu, Z. R. 2016. Molecular characterization of rice OsLCB2a1 gene and functional analysis of its role in insect resistance. Frontiers in Plant Science, 7, 1789.
  2. Chen, W., Gong, L., Guo, Z., Wang, W., Zhang, H., Liu, X., Yu, S., Xiong, L. & Luo, J. 2013. A novel integrated method for large-scale detection, identification and quantification of widely targeted metabolites: Application in the study of rice metabolomics. Molecular Plant, 6(6), 1769-1780.
  3. Fischer, S. & Sana, T. 2007. Metabolomic profiling of bacterial leaf blight in rice. ( Agilent Technologies, Inc. Printed in the U.S.A. February 14, 2007.
  4. Gleye, C., Raynaud, S., Fourneau, C., Laurens, A., Laprévote, O., Serani, L., Fournet, A. & Hocquemiller, R. 2000. Cohibins C and D, two important metabolites in the biogenesis of acetogenins from Annona muricata and Annona nutans. Journal of Natural Products, 63(9), 1192-1196.
  5. Goff, S. A., Ricke, D., Lan, T. H., Presting, G., Wang, R., Dunn, M., Glazebrook, J., Sessions, A., Oeller, P., Varma, H., Hadley, D., Hutchison, D., Martin, C., Katagiri, F. B., Lange, M., Moughamer, T., Xia, Y., Budworth, P., Zhong, J., Miguel, T., Paszkowski, U., Zhang, S., Colbert, M., Sun, W., Chen, L., Cooper, B., Park, S., Wood, T. C., Mao, L., Quail, P., Wing, R., Dean, R., Yu, Y., Zharkikh, A., Shen, R., Sahasrabudhe, S., Thomas, A., Cannings, R., Gutin, A., Pruss, D., Reid, J., Tavtigian, S., Mitchell, J., Eldredge, G., Scholl, T., Miller, R. M., Bhatnagar, S., Adey, N., Rubano, T., Tusneem, N., Robinson, R., Feldhaus, J., Macalma, T., Oliphant, A. & Briggs, S. 2002. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science, 296(5565), 92-100.
  6. Hildreth, K., Kodani, S. D., Hammock, B. D. & Zhao, L. 2020. Cytochrome P450-derived linoleic acid metabolites EpOMEs and DiHOMEs: A review of recent studies. The Journal of Nutritional Biochemistry,
  7. Killiny, N. & Nehela, Y. 2020. Citrus Polyamines: Structure, Biosynthesis, and Physiological Functions. Plants, 9, 426.
  8. Ling, Y. S., Lim, L. R., Yong, Y. S., Tamin, O. & Puah, P. Y. 2020. MS-based metabolomics revealing Bornean Sinularia sp. extract dysregulated lipids triggering programmed cell death in hepatocellular carcinoma. Natural Products Research, 34(12), 1796-1803.
  9. Liu, L., Waters, D. L. E., Rose, T. J., Bao, J. & King, G. J. 2013. Phospholipids in rice: Significance in grain quality and health benefits: A review. Food Chemistry, 139, 1133–1145.
  10. Oikawa, A., Matsuda, F., Kusano, M., Okazaki, Y. & Saito, K. 2008. Rice Metabolomics. Rice, 1, 63–71.
  11. Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. 2010. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry based molecular profile data. BMC Bioinformatics, 11, 395.
  12. Puah, P. Y., Lee, D. J. H., Mak, K. H., Ang, H. J., Chen, H. C., Moh, P. Y., Fong, S. Y. & Ling, Y. S. 2019. Extractable impurities from fluoropolymer-based membrane filters – interference in high-throughput, untargeted analysis. RSC Advances, 9, 31918-31927.
  13. Roberts, L. D., Souza, A. L., Gerszten, R. E. & Clish, C. B. 2012. Current Protocols in Molecular Biology. John Wiley & Sons, Inc.
  14. Sasaki, T., Matsumoto, T., Yamamoto, K., Sakata, K., Baba, T., Katayose, Y., Wu, J., Niimura, Y., Cheng, Z., Nagamura, Y., Antonio, B. A., Kanamori, H., Hosokawa, S., Masukawa, M., Arikawa, K., Chiden, Y., Hayashi, M., Okamoto, M., Ando, T., Aoki, H., Arita, K., Hamada, M., Harada, C., Hijishita, S., Honda, M., Ichikawa, Y., Idonuma, A., Iijima, M., Ikeda, M., Ikeno, M., Ito, S., Ito, T., Ito, Y., Ito, Y., Iwabuchi, A., Kamiya, K., Karasawa, W., Katagiri, S., Kikuta, A., Kobayashi, N., Kono, K., Machita, K., Maehara, T., Mizuno, H., Mizubayashi, T., Mukai, Y., Nagasaki, H., Nakashima, M., Nakama, Y., Nakamichi, Y., Nakamura, M., Namiki, N., Negishi, M., Ohta, I., Ono, N., Saji, S., Sakai, K., Shibata, M., Shimokawa, T., Shomura, A., Song, J., Takazaki, Y., Terasawa, K., Tsuji, K., Waki, K., Yamagata, H., Yamane, H., Yoshiki, S., Yoshihara, R., Yukawa, K., Zhong, H., Iwama, H., Endo, T., Ito, H., Ho, J. H., Kim, H., Eun, M.Y., Yano, M., Jiang, J. & Gojobori, T. 2002. The genome sequence and structure of rice chromosome 1. Nature, 420, 312–316.
  15. Sharmila, M., Rajeswari, M. & Jayashree, I. 2017. GC-MS analysis of bioactive compounds in the whole plant of ethanolic extract of Ludwigia perennis L. Research Article, 46(1), 124-128.
  16. Xia, J. & Wishart, D.S. 2016. Using metaboanalyst 3.0 for comprehensive metabolomics data analysis. Current Protocol in Bioinformatics, 55,14.10.1-14.10.91. doi: 10.1002/cpbi.11
  17. Ma, Y., Kind, T., Vaniya, A., Gennity, I., Fahrmann, J.F. & Fiehn, O. 2015. An in silico MS/MS library for automatic annotation of novel FAHFA lipids. Journal of Cheminformatics, 7(53). DOI 10.1186/s13321-015-0104-4
  18. Ling, Y. S., Liang, H. J., Chung, M. H., Lin, M. H., & Lin, C. Y. 2014. NMR- and MS-based metabolomics: Various organ responses following naphthalene intervention. Molecular Biosystems, 10, 1918-1931.
  19. Lin, Z., Zhang, X., Wang, Z., Jiang, Y., Liu, Z., Alexander, D., Li, G., Wang, S. & Ding, Y. 2017. Metabolomic analysis of pathways related to rice grain chalkiness by a notched-belly mutant with high occurrence of white-belly grains. BMC Plant Biology, 17, 39.
  20. Mizushina, Y., Nakanishi, R., Kuriyama, I., Kamiya, K., Satake, T., Shimazaki, N., Koiwai, O., Uchiyama, Y., Yonezawa, Y., Takemura, M., Sakaguchi, K., & Yoshida, H. 2006. β-Sitosterol-3-O-β-d-glucopyranoside: A eukaryotic DNA polymerase λ inhibitor. Journal of Steroid Biochemistry and Molecular Biology, 99, 100–107.