Abstract
Introduction
In plant metabolomics, metabolite contents are often normalized by sample weight. However, accurate weighing of very small samples, such as individual Arabidopsis thaliana seeds (approximately 20 µg), is difficult, which may lead to irreproducible results.
Objectives
We aimed to establish alternative normalization methods for seed-grain-based comparative metabolomics of A. thaliana.
Methods
Arabidopsis thaliana seeds were assumed to have a prolate spheroid shape. Using a microscope image of each seed, the lengths of major and minor axes were measured by fitting a projected 2-dimensional shape of each seed as an ellipse. Metabolic profiles of individual diploid or tetraploid A. thaliana seeds were measured by our highly sensitive protocol (“widely targeted metabolomics”) that uses liquid chromatography coupled with tandem quadrupole mass spectrometry. Mass spectrometric analysis of 1 µL of solution extract identified more than 100 metabolites. The data were normalized by various seed-size measures, including seed volume (single-grain-based analysis). For comparison, metabolites were extracted from 4 mg of diploid and tetraploid A. thaliana seeds and their metabolic profiles were analyzed by normalization of weight (weight-based analysis).
Results
A small number of metabolites showed statistically significant differences in the single-grain-based analysis compared to weight-based analysis. A total of 17 metabolites showed statistically different accumulation between ploidy types with similar fold changes in both analyses.
Conclusion
Seed-size measures obtained by microscopic imaging were useful for data normalization. Single-grain-based analysis enables evaluation of metabolism of each seed and elucidates the metabolic profiles of precious bioresources by using small amounts of samples.
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References
Ashraf, M., & Foolad, M. R. (2007). Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environmental and Experimental Botany, 59, 206–216. doi:10.1016/j.envexpbot.2005.12.006.
Breuer, C., et al. (2007). BIN4, a novel component of the plant DNA topoisomerase VI complex, is required for endoreduplication in Arabidopsis. The Plant Cell, 19, 3655–3668. doi:10.1105/tpc.107.054833.
Dilkes, B. P., & Comai, L. (2004). A differential dosage hypothesis for parental effects in seed development. The Plant Cell, 16, 3174–3180. doi:10.1105/tpc.104.161230.
Fujikura, U., Horiguchi, G., & Tsukaya, H. (2007). Genetic relationship between angustifolia3 and extra-small sisters highlights novel mechanisms controlling leaf size. Plant Signaling & Behavior, 2, 378–380.
Herridge, R. P., Day, R. C., Baldwin, S., & Macknight, R. C. (2011). Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery. Plant Methods, 7, 3. doi:10.1186/1746-4811-7-3.
Hirai, M. Y., et al. (2010). Toward genome-wide metabolotyping and elucidation of metabolic system: Metabolic profiling of large-scale bioresources. Journal of Plant Research, 123, 291–298. doi:10.1007/s10265-010-0337-2.
Hirai, M. Y., Fujiwara, T., Chino, M., & Naito, S. (1995). Effects of sulfate concentrations on the expression of a soybean seed storage protein gene and its reversibility in transgenic Arabidopsis thaliana. Plant & Cell Physiology, 36, 1331–1339.
Jander, G., et al. (2004). Application of a high-throughput HPLC-MS/MS assay to Arabidopsis mutant screening; evidence that threonine aldolase plays a role in seed nutritional quality. Plant Journal, 39, 465–475. doi:10.1111/j.1365-313X.2004.02140.x.
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2014). Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Research, 42, D199–D205. doi:10.1093/nar/gkt1076.
Kim, S., et al. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44, D1202–D1213. doi:10.1093/nar/gkv951.
Kozuka, T., Horiguchi, G., Kim, G. T., Ohgishi, M., Sakai, T., & Tsukaya, H. (2005). The different growth responses of the Arabidopsis thaliana leaf blade and the petiole during shade avoidance are regulated by photoreceptors and sugar. Plant and Cell Physiology, 46, 213–223. doi:10.1093/pcp/pci016.
Liu, Y. Q., et al. (2009). Isoflavone content and anti-acetylcholinesterase activity in commercial douchi (a traditional Chinese salt-fermented soybean food). Jarq-Japan. Agricultural Research Quarterly, 43, 301–307.
Miller, M., Zhang, C. Q., & Chen, Z. J. (2012). Ploidy and hybridity effects on growth vigor and gene expression in Arabidopsis thaliana hybrids and their parents. G3-Genes Genomes Genetics, 2, 505–513. doi:10.1534/g3.112.002162.
Ohtani, M., et al. (2016). Primary metabolism during biosynthesis of secondary wall polymers of protoxylem vessel elements. Plant Physiology, 172, 1612–1624. doi:10.1104/pp.16.01230.
Sakurai, T., et al. (2013). PRIMe Update: Innovative content for plant metabolomics and integration of gene expression and metabolite accumulation. Plant and Cell Physiology, 54, e5. doi:10.1093/pcp/pcs184.
Sawada, Y., et al. (2009a). Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants. Plant and Cell Physiology, 50, 37–47. doi:10.1093/pcp/pcn183.
Sawada, Y., et al. (2009b). Omics-based approaches to methionine side chain elongation in Arabidopsis: Characterization of the genes encoding methylthioalkylmalate isomerase and methylthioalkylmalate dehydrogenase. Plant and Cell Physiology, 50, 1181–1190. doi:10.1093/pcp/pcp079.
Sawada, Y., et al. (2012). RIKEN tandem mass spectral database (ReSpect) for phytochemicals: A plant-specific MS/MS-based data resource and database. Phytochemistry, 82, 38–45. doi:10.1016/j.phytochem.2012.07.007.
Sawada, Y., & Aoki, T. (2014). Metabolomics. In S. Tabata & J. Stougaard. (Eds.), The Lotus japonicus Genome (pp. 171–182). Berlin: Springer.
Sawada, Y., & Hirai, M. Y. (2013). Integrated LC-MS/MS system for plant metabolomics. Computational and Structural Biotechnology Journal, 4, e201301011. doi:10.5936/csbj.201301011.
Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9, 671–675.
Spielman, M., Vinkenoog, R., Dickinson, H. G., & Scott, R. J. (2001). The epigenetic basis of gender in flowering plants and mammals. Trends in Genetics, 17, 705–711.
Sumner, L. W., Mendes, P., & Dixon, R. A. (2003). Plant metabolomics: Large-scale phytochemistry in the functional genomics era. Phytochemistry, 62, 817–836. doi:10.1016/S0031-9422(02)00708-2.
Sundaresan, V. (2005). Control of seed size in plants. Proceedings of the National Academy of Sciences of the United States of America, 102, 17887–17888. doi:10.1073/pnas.0509021102.
Tsugawa, H., et al. (2014b). MRM-DIFF: Data processing strategy for differential analysis in large scale MRM-based lipidomics studies. Frontiers in Genetics, 5, 471. doi:10.3389/fgene.2014.00471.
Tsugawa, H., Kanazawa, M., Ogiwara, A., & Arita, M. (2014a). MRMPROBS suite for metabolomics using large-scale MRM assays. Bioinformatics, 30, 2379–2380. doi:10.1093/bioinformatics/btu203.
Tsukaya, H., et al. (2015). Intraspecific comparative analyses of metabolites between diploid and tetraploid Arabidopsis thaliana and Pyrus communis. New Negatives in Plant Science, 1–2, 53–61. doi:10.1016/j.neps.2015.06.001.
Ueda, A., et al. (2004). Osmotic stress in barley regulates expression of a different set of genes than salt stress does. Journal of Experimental Botany, 55, 2213–2218. doi:10.1093/jxb/erh242.
Yost, R. A., & Enke, C. G. (1979). Triple quadrupole mass-spectrometry for direct mixture analysis and structure elucidation. Analytical Chemistry, 51, 1251A–1264A. doi:10.1021/Ac50048a002.
Zhang, X. Y., Hu, C. G., & Yao, J. L. (2010). Tetraploidization of diploid Dioscorea results in activation of the antioxidant defense system and increased heat tolerance. Journal of Plant Physiology, 167, 88–94. doi:10.1016/j.jplph.2009.07.006.
Acknowledgements
We thank Ms. Akane Sakata and Mr. Yutaka Yamada for sample preparation and information technology support, respectively. This work was supported by the Japan Society for the Promotion of Science (Grants-in-Aid for Creative Scientific Research and Scientific Research A), Ministry of Education, Culture, Sports, Science and Technology, Japan (Scientific Research on Priority Areas and Scientific Research on Innovative Areas), and Mitsubishi Foundation.
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Yuji Sawada, Hirokazu Tsukaya and Kensuke Kawade have contributed equally to this work.
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11306_2017_1211_MOESM2_ESM.pdf
Supplementary Figure 2—Boxplot for z-scores of log2-transformed data. Differences in the median values and distribution of metabolomic data between diploid (DP) and autotetraploid (TP) A. thaliana seeds were analyzed in single-grain-based and weight-based analyses to compare methods of normalization (PDF 79 KB)
11306_2017_1211_MOESM3_ESM.pdf
Supplementary Figure 3—Volcano plot based on weight-based data. The weight-normalized autotetraploid data were divided by those of diploid data and transformed into log2 values. The metabolites exhibiting statistically significant differences between diploid and autotetraploid seeds (Welch’s t test, p < 0.05) are shown as red dots with metabolite annotations. The other metabolites are shown as gray dots (PDF 293 KB)
11306_2017_1211_MOESM4_ESM.pdf
Supplementary Figure 4—Betaine and sucrose contents per grain and per weight. Sample No. corresponds to that in Supplementary Table S1. Experimental groups are indicated by color. Black, diploid and single-grain-based; red, tetraploid and single-grain-based; green, diploid and weight-based; blue, tetraploid and weight-based (PDF 8 KB)
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Sawada, Y., Tsukaya, H., Li, Y. et al. A novel method for single-grain-based metabolic profiling of Arabidopsis seed. Metabolomics 13, 75 (2017). https://doi.org/10.1007/s11306-017-1211-1
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DOI: https://doi.org/10.1007/s11306-017-1211-1