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Machine learning links seed composition, glucosinolates and viability of oilseed rape after 31 years of long-term storage

Published online by Cambridge University Press:  12 July 2018

Manuela Nagel
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
Katharina Holstein
Affiliation:
Fraunhofer Institute for Factory Operation and Automation (IFF), Magdeburg, Germany
Evelin Willner
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
Andreas Börner
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
Corresponding
E-mail address:

Abstract

Seed longevity is influenced by many factors, a widely discussed one of which is the seed lipid content and fatty acid composition. Here, linear and non-linear regressions based on machine learning were applied to analyse germinability and seed composition of a set of 42 oilseed rape (Brassica napus L.) accessions grown under the same single environment and at the same time following a period of up to 31 years storage at 7°C. Mean viability was halved after 27.0 years of storage, but this figure concealed a major influence of genotype. There was also wide variation with respect to fatty acid composition, particularly with respect to oleic, α-linolenic, eicosenoic and erucic acid. Linear regression (rL) revealed significant correlation coefficients between normal seedling appearance and the content of α-linolenic acid (+0.52) and total oil (+0.59). Multivariate regression using artificial neural networks including a radial basis function (RBF), a multilayer perceptron (MLP) and a partial least square (PLS) recognized underlying structures and revealed high significant correlation coefficients (rM) for oil content (+0.87), eicosenoic acid (+0.75), stearic acid (+0.73) and lignoceric acid (+0.97). Oil content or a combination of oleic, α-linolenic, arachidic, eicosenoic and eicosadienoic acids and glucosinolates resulted in highest model fitting parameters R2 of 0.90 and 0.88, respectively. In addition, the glucosinolate content, predominantly in the Brassicaceae family and ranging from 4.6 to 79.5 µM, was negatively correlated with viability (rL = ‒0.43). Summarizing, oil content, some fatty acids and glucosinolates contribute to variations in average half-life (15.2 to 50.7 years) of oilseed rape seeds. In contrast to linear regression, multivariate regression using artificial neural networks revealed high associations for combinations of parameters including underestimated minor fatty acids such as arachidic, stearic and eicosadienoic acids. This indicates that genetic and seed composition factors contribute to seed longevity. In addition, multivariate regressions might be a successful approach to predict seed viability based on fatty acids and seed oil content.

Type
Research Paper
Copyright
Copyright © Cambridge University Press 2018 

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References

Andre, M (2003) Multivariate analysis and classification of the chemical quality of 7-aminocephalosporanic acid using near-infrared reflectance spectroscopy. Analytical Chemistry 75, 34603467.CrossRefGoogle ScholarPubMed
Bailly, C (2004) Active oxygen species and antioxidants in seed biology. Seed Science Research 14, 93107.CrossRefGoogle Scholar
Bettey, M, Finch-Savage, WE, King, GJ and Lynn, JR (2000) Quantitative genetic analysis of seed vigour and pre-emergence seedling growth traits in Brassica oleracea. New Phytologist 148, 277286.CrossRefGoogle Scholar
Bewley, JD, Bradford, KJ, Hilhorst, HWM and Nonogaki, H (2013) Seeds: Physiology of Development, Germination and Dormancy, 3rd edition. New York: Springer.CrossRefGoogle Scholar
Borges, A, Abreu, AC, Ferreira, C, Saavedra, MJ, Simoes, LC and Simoes, M (2015) Antibacterial activity and mode of action of selected glucosinolate hydrolysis products against bacterial pathogens. Journal of Food Science and Technology-Mysore 52, 47374748.CrossRefGoogle ScholarPubMed
Clark, SR, Baune, BT, Schubert, KO, Lavoie, S, Smesny, S, Rice, SM, Schäfer, MR, Benninger, F, Feucht, M, Klier, CM, McGorry, PD and Amminger, GP (2016) Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers. Translational Psychiatry 6, e897.CrossRefGoogle ScholarPubMed
Crapiste, GH, Brevedan, MIV and Carelli, AA (1999) Oxidation of sunflower oil during storage. Journal of the American Oil Chemists Society 76, 14371443.CrossRefGoogle Scholar
Cybenko, G (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS) 2, 303314.CrossRefGoogle Scholar
de Jong, TJ, Isanta, MT and Hesse, E (2013) Comparison of the crop species Brassica napus and wild B. rapa: characteristics relevant for building up a persistent seed bank in the soil. Seed Science Research 23, 169179.CrossRefGoogle Scholar
Elliott, RH, Franke, C and Rakow, GFW (2008) Effects of seed size and seed weight on seedling establishment, vigour and tolerance of Argentine canola (Brassica napus) to flea beetles, Phyllotreta spp. Canadian Journal of Plant Science 88, 207217.CrossRefGoogle Scholar
Ellis, RH and Roberts, EH (1980) Improved equations for the prediction of seed longevity. Annals of Botany 45, 1330.CrossRefGoogle Scholar
Falk, J and Munné-Bosch, S (2010) Tocochromanol functions in plants: antioxidation and beyond. Journal of Experimental Botany 61, 15491566.CrossRefGoogle ScholarPubMed
Hall, RD (2011) Biology of plant metabolomics. Annual Plant Reviews 43, 407420.Google Scholar
Harwood, JL (1997) Plant lipid metabolism, in Dey, PM and Harborne, JB (eds), Plant Biochemistry. San Diego, CA: Academic Press.Google Scholar
Hoekstra, FA (2005) Differential longevities in desiccated anhydrobiotic plant systems. Integrative and Comparative Biology 45, 725733.CrossRefGoogle ScholarPubMed
Hwang, JE, Ahn, JW, Kwon, SJ, Kim, JB, Kim, SH, Kang, SY and Kim, DS (2014) Selection and molecular characterization of a high tocopherol accumulation rice mutant line induced by gamma irradiation. Molecular Biology Reports 41, 76717681.CrossRefGoogle ScholarPubMed
ISTA (2014) International Rules for Seed Testing. Bassersdorf, Switzerland: International Seed Testing Association.Google Scholar
Johnson, RA and Wichern, DW (2007) Applied Multivariate Statistical Analysis (6th edition). New York, Pearson Book.Google Scholar
Komba, CG, Brunton, BJ and Hampton, JG (2007) Effect of seed size within seed lots on seed quality in kale. Seed Science and Technology 35, 244248.CrossRefGoogle Scholar
Kranner, I, Minibayeva, FV, Beckett, RP and Seal, CE (2010) What is stress? Concepts, definitions and applications in seed science. New Phytologist 188, 655673.CrossRefGoogle ScholarPubMed
Krzanowski, WJ (2000) Principles of Multivariate Analysis: A User's Perspective. New York, Oxford University Press.Google Scholar
Lemaitre, RN, Fretts, AM, Sitlani, CM, Biggs, ML, Mukamal, K, King, IB, Song, X, Djoussé, L, Siscovick, DS, McKnight, B, Sotoodehnia, N, Kizer, JR and Mozaffarian, D (2015) Plasma phospholipid very-long-chain saturated fatty acids and incident diabetes in older adults: the Cardiovascular Health Study. The American Journal of Clinical Nutrition 101, 10471054.CrossRefGoogle ScholarPubMed
Ma, L, Zhu, FG, Li, ZW, Zhang, JF, Li, X, Dong, JL and Wang, T (2015) TALEN-based mutagenesis of lipoxygenase LOX3 enhances the storage tolerance of rice (Oryza sativa) seeds. PLoS One 10), e0143877. https://doi.org/10.1371/journal.pone.0143877CrossRefGoogle ScholarPubMed
Moody, J and Darken, CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Computation 1, 281294.CrossRefGoogle Scholar
Nagel, M, Behrens, A-K and Börner, A (2013) Effects of Rht dwarfing alleles on wheat seed vigour after controlled deterioration. Crop and Pasture Science 64, 857864.CrossRefGoogle Scholar
Nagel, M and Börner, A (2010) The longevity of crop seeds stored under ambient conditions. Seed Science Research 20, 112.CrossRefGoogle Scholar
Oenel, A, Fekete, A, Krischke, M, Faul, SC, Gresser, G, Havaux, M, Mueller, MJ and Berger, S (2017) Enzymatic and non-enzymatic mehanisms contribute to lipid oxidation during seed aging. Plant and Cell Physiology 58, 925933.CrossRefGoogle Scholar
Ponquett, RT, Smith, MT and Ross, G (1992) Lipid autoxidation and seed ageing: putative relationships between seed longevity and lipid stability. Seed Science Research 2, 5154.CrossRefGoogle Scholar
Priestley, DA, Cullinan, VI and Wolfe, J (1985) Differences in seed longevity at the species level. Plant Cell and Environment 8, 557562.CrossRefGoogle Scholar
Priestley, DA and Leopold, AC (1979) Absence of lipid oxidation during accelerated aging of soybean seeds. Plant Physiology 63, 726729.CrossRefGoogle ScholarPubMed
Riewe, D, Wiebach, J and Altmann, T (2017) Structure annotation and quantification of wheat seed oxidized lipids by high resolution LC-MS/MS. Plant Physiology 175, 600618.Google ScholarPubMed
Rivas-Ubach, A, Sardans, J, Pérez-Trujillo, M, Estiarte, M and Peñuelas, J (2012) Strong relationship between elemental stoichiometry and metabolome in plants. Proceedings of the National Academy of Sciences of the USA 109, 41814186.CrossRefGoogle ScholarPubMed
Rojas, R (1996) Neural Networks: A Systematic Introduction. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Rücker, B and Röbbelen, G (1996) Impact of low linolenic acid content on seed yield of winter oilseed rape (Brassica napus L.). Plant Breeding 115, 226230.CrossRefGoogle Scholar
Sattler, SE, Gilliland, LU, Magallanes-Lundback, M, Pollard, M and DellaPenna, D (2004) Vitamin E is essential for seed longevity, and for preventing lipid peroxidation during germination. Plant Cell 16, 14191432.CrossRefGoogle ScholarPubMed
Shalev-Shwartz, S and Ben-David, S (2014) Understanding Machine Learning: From Theory to Algorithms. New York: Cambridge University Press.CrossRefGoogle Scholar
VSN International (2013) GenStat for Windows (17th edition). Hemel Hempstead, UK.Google Scholar
Walters, C, Wheeler, LM and Grotenhuis, JM (2005) Longevity of seeds stored in a genebank: species characteristics. Seed Science Research 15, 120.CrossRefGoogle Scholar
Waterworth, WM, Bray, CM and West, CE (2015) The importance of safeguarding genome integrity in germination and seed longevity. Journal of Experimental Botany 66, 35493558.CrossRefGoogle ScholarPubMed
Wittkop, B, Snowdon, RJ and Friedt, W (2009) Status and perspectives of breeding for enhanced yield and quality of oilseed crops for Europe. Euphytica 170, 131140.CrossRefGoogle Scholar
Wold, S, Sjöström, M and Eriksson, L (2001) PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109130.CrossRefGoogle Scholar
Woodfield, HK, Sturtevant, D, Borisjuk, L, Munz, E, Guschina, IA, Chapman, K and Harwood, JL (2017) Spatial and temporal mapping of key lipid species in Brassica napus seeds. Plant Physiology 173, 19982009.CrossRefGoogle ScholarPubMed
Worley, B and Powers, R (2013) Multivariate analysis in metabolomics. Current Metabolomics 1, 92107.Google ScholarPubMed
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Machine learning links seed composition, glucosinolates and viability of oilseed rape after 31 years of long-term storage
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