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Metabolomics

, 14:148 | Cite as

Crop metabolomics: from diagnostics to assisted breeding

  • Saleh Alseekh
  • Luisa Bermudez
  • Luis Alejandro de Haro
  • Alisdair R. Fernie
  • Fernando CarrariEmail author
Review Article
Part of the following topical collections:
  1. Plant metabolomics and lipidomics

Abstract

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.

Keywords

Crop plant breeding Metabolic traits Mass spectrometry Nuclear magnetic resonance spectroscopy Translational metabolomics 

Notes

Acknowledgements

Work in our laboratories is funded in part by ANPCyT, CONICET, INTA, UBA (Argentina); CAPES and USP (Brazil); Max Planck Society (Germany) and the European Union Horizon 2020 Research and Innovation Programme (Grant Agreement Number 679796).

Authors Contributions

SA, LAdH and LB surveyed and discussed scientific literature and participated in writing the manuscript. LAdH and LB designed and drawn the illustrative figure. ARF wrote and edited the manuscript and FC wrote the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abdelrahman, M., Burritt, D. J., & Tran, L. P. (2017) The use of metabolomic quantitative trait locus mapping and osmotic adjustment traits for the improvement of crop yields under environmental stresses. In Seminars in Cell & Developmental Biology. Cambridge: Academic PressGoogle Scholar
  2. Aflitos, S., Schijlen, E., de Jong, H., de Ridder, D., Smit, S., Finkers, R., Wang, J., Zhang, G., Li, N., Mao, L., Bakker, F., Dirks, R., Breit, T., Gravendeel, B., Huits, H., Struss, D., Swanson-Wagner, R., van Leeuwen, H., van Ham, R. C., Fito, L., Guignier, L., Sevilla, M., Ellul, P., Ganko, E., Kapur, A., Reclus, E., de Geus, B., van de Geest, H., Hekkert, T. L., van Haarst, B., Smits, J., Koops, L., Sanchez-Perez, A., van Heusden, G., Visser, A. W., Quan, R., Min, Z., Liao, J., Wang, L., Wang, X., Yue, G., Yang, Z., Xu, X., Schranz, N., Smets, E., Vos, E., Rauwerda, R., Ursem, J., Schuit, R., Kerns, C., van den Berg, M., Vriezen, J., Janssen, W., Datema, A., Jahrman, E., Moquet, T., Bonnet, F., J. and Peters, S. (2014) Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. The Plant Journal: For Cell and Molecular Biology, 80, 136–148.Google Scholar
  3. Ahmad, R., Jamil, S., Shahzad, M., Zörb, C., Irshad, U., Khan, N., et al. (2017). Metabolic profiling to elucidate genetic elements due to salt stress. Clean - Soil, Air, Water.  https://doi.org/10.1002/clen.201600574.CrossRefGoogle Scholar
  4. Ainalidou, A., Tanou, G., Belghazi, M., Samiotaki, M., Diamantidis, G., Molassiotis, A., & Karamanoli, K. (2016). Integrated analysis of metabolites and proteins reveal aspects of the tissue-specific function of synthetic cytokinin in kiwifruit development and ripening. Journal of Proteomics, 143, 318–333.PubMedGoogle Scholar
  5. Albrecht, U., Fiehn, O., & Bowman, K. (2016). Metabolic variations in different citrus rootstock cultivars associated with different responses to Huanglongbing. Plant Physiol Biochem, 107, 33–44.PubMedGoogle Scholar
  6. Alseekh, S., & Fernie, A. R. (2018). Metabolomics 20 years on: What have we learned and what hurdles remain? The Plant Journal: For Cell and Molecular Biology, 94, 933–942.Google Scholar
  7. Amiour, N., Imbaud, S., Clement, G., Agier, N., Zivy, M., Valot, B., Balliau, T., Armengaud, P., Quillere, I., Canas, R., Tercet-Laforgue, T., & Hirel, B. (2012). The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. Journal of Experimental Botany, 63, 5017–5033.PubMedGoogle Scholar
  8. Bai, C., Twyman, R. M., Farré, G., Sanahuja, G., Christou, P., Capell, T., & Zhu, C. (2011). A golden era-pro-vitamin A enhancement in diverse crops. In Vitro Cellular and Developmental Biology—Plant.  https://doi.org/10.1007/s11627-011-9363-6.
  9. Baldina, S., Picarella, M. E., Troise, A. D., Pucci, A., Ruggieri, V., Ferracane, R., Barone, A., Fogliano, V., & Mazzucato, A. (2016). Metabolite profiling of italian tomato landraces with different fruit types. Frontiers in Plant Science, 7, 664.PubMedPubMedCentralGoogle Scholar
  10. Beatty, P., Klein, M., Fischer, J., Lewis, I., Muench, D., & Good, A. (2016). Understanding plant nitrogen metabolism through metabolomics and computational approaches. Plants, 5(4), 39.  https://doi.org/10.3390/plants5040039.CrossRefPubMedCentralGoogle Scholar
  11. Benard, C., Bernillon, S., Biais, B., Osorio, S., Maucourt, M., Ballias, P., Deborde, C., Colombie, S., Cabasson, C., Jacob, D., Vercambre, G., Gautier, H., Rolin, D., Genard, M., Fernie, A. R., Gibon, Y., & Moing, A. (2015). Metabolomic profiling in tomato reveals diel compositional changes in fruit affected by source-sink relationships. Journal of Experimental Botany, 66, 3391–3404.PubMedPubMedCentralGoogle Scholar
  12. Benevenuto, R. F., Agapito-Tenfen, S. Z., Vilperte, V., Wikmark, O. G., van Rensburg, P. J., & Nodari, R. O. (2017). Molecular responses of genetically modified maize to abiotic stresses as determined through proteomic and metabolomic analyses. PLoS ONE, 12, e0173069.PubMedPubMedCentralGoogle Scholar
  13. Bino, R. J., Hall, R. D., Fiehn, O., Kopka, J., Saito, K., Draper, J., et al. (2004). Potential of metabolomics as a functional genomics tool. Trends in Plant Science, 9(9), 418–425.  https://doi.org/10.1016/j.tplants.2004.07.004.CrossRefPubMedGoogle Scholar
  14. Bolger, A., Scossa, F., Bolger, M. E., Lanz, C., Maumus, F., Tohge, T., Quesneville, H., Alseekh, S., Sorensen, I., Lichtenstein, G., Fich, E. A., Conte, M., Keller, H., Schneeberger, K., Schwacke, R., Ofner, I., Vrebalov, J., Xu, Y., Osorio, S., Aflitos, S. A., Schijlen, E., Jimenez-Gomez, J. M., Ryngajllo, M., Kimura, S., Kumar, R., Koenig, D., Headland, L. R., Maloof, J. N., Sinha, N., van Ham, R. C., Lankhorst, R. K., Mao, L., Vogel, A., Arsova, B., Panstruga, R., Fei, Z., Rose, J. K., Zamir, D., Carrari, F., Giovannoni, J. J., Weigel, D., Usadel, B., & Fernie, A. R. (2014). The genome of the stress-tolerant wild tomato species Solanum pennellii. Nature Genetics, 46, 1034–1038.PubMedGoogle Scholar
  15. Bucher, R., Veyel, D., Willmitzer, L., Krattinger, S., Keller, B., & Bigler, L. (2017). Combined GC- and UHPLC-HR-MS based metabolomics to analyze durable anti-fungal resistance processes in cereals. CHIMIA International Journal for Chemistry, 71(4), 156–159.  https://doi.org/10.2533/chimia.2017.156.CrossRefGoogle Scholar
  16. Cai, G., Yang, Q., Chen, H., Yang, Q., Zhang, C., Fan, C., & Zhou, Y. (2016). Genetic dissection of plant architecture and yield-related traits in Brassica napus. Scientific Reports, 6, 21625.  https://doi.org/10.1038/srep21625.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Cañas, R. A., Yesbergenova-Cuny, Z., Simons, M., Chardon, F., Armengaud, P., Quillere, I., Cukier, C., Gibon, Y., Limami, A. M., Nicolas, S., Brule, L., Lea, P. J., Maranas, C. D., & Hirel, B. (2017). Exploiting the genetic diversity of maize using a combined metabolomic, enzyme activity profiling, and metabolic modeling approach to link leaf physiology to kernel yield. The Plant Cell, 29, 919–943.PubMedPubMedCentralGoogle Scholar
  18. Cebulj, A., Cunja, V., Mikulic-Petkovsek, M., & Veberic, R. (2017). Importance of metabolite distribution in apple fruit. Scientia Horticulturae, 214, 214–220.Google Scholar
  19. Chambers, A. H., Pillet, J., Plotto, A., Bai, J., Whitaker, V. M., & Folta, K. M. (2014). Identification of a strawberry flavor gene candidate using an integrated genetic-genomic-analytical chemistry approach. BMC Genomics, 15, 217.  https://doi.org/10.1186/1471-2164-15-217.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Chen, W., Gao, Y., Xie, W., Gong, L., Lu, K., Wang, W., Li, Y., Liu, X., Zhang, H., Dong, H., Zhang, W., Zhang, L., Yu, S., Wang, G., Lian, X., & Luo, J. (2014). Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nature Genetics, 46, 714–721.PubMedGoogle Scholar
  21. Chrobok, D., Law, S. R., Brouwer, B., Linden, P., Ziolkowska, A., Liebsch, D., Narsai, R., Szal, B., Moritz, T., Rouhier, N., Whelan, J., Gardestrom, P., & Keech, O. (2016). Dissecting the metabolic role of mitochondria during developmental leaf senescence. Plant Physiology, 172, 2132–2153.PubMedPubMedCentralGoogle Scholar
  22. Copley, T. R., Duggavathi, R., & Jabaji, S. (2017). The transcriptional landscape of Rhizoctonia solani AG1-IA during infection of soybean as defined by RNA-sEq. PLoS ONE, 12, e0184095.PubMedPubMedCentralGoogle Scholar
  23. Cortina, P. R., Santiago, A. N., Sance, M. M., Peralta, I. E., Carrari, F., & Asis, R. (2018). Neuronal network analyses reveal novel associations between volatile organic compounds and sensory properties of tomato fruits. Metabolomics, 14, 57.Google Scholar
  24. Cuadros-Inostroza, A., Giavalisco, P., Hummel, J., Eckardt, A., Willmitzer, L., & Pena-Cortes, H. (2010). Discrimination of wine attributes by metabolome analysis. Analytical Chemistry, 82, 3573–3580.PubMedGoogle Scholar
  25. D’Angelo, M., Zanor, M. I., Sance, M., Cortina, P. R., Boggio, S. B., Asprelli, P., Carrari, F., Santiago, A. N., Asis, R., Peralta, I. E., & Valle, E. M. (2018). Contrasting metabolic profiles of tasty Andean varieties of tomato fruit in comparison with commercial ones. Journal of the Science of Food and Agriculture, 98, 4128–4134.PubMedGoogle Scholar
  26. Davies, K. M., & Espley, R. V. (2013). Opportunities and challenges for metabolic engineering of secondary metabolite pathways for improved human health characters in fruit and vegetable crops. New Zealand Journal of Crop and Horticultural Science, 41(3), 154–177.  https://doi.org/10.1080/01140671.2013.793730.CrossRefGoogle Scholar
  27. Desnoues, E., Gibon, Y., Baldazzi, V., Signoret, V., Génard, M., & Quilot-Turion, B. (2014). Profiling sugar metabolism during fruit development in a peach progeny with different fructose-to-glucose ratios. BMC Plant Biology, 14(1), 336.  https://doi.org/10.1186/s12870-014-0336-x-.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Di Paola Naranjo, R. D., Otaiza, R. D., Saragusti, S., Baroni, A. C., Carranza, V., Peralta, AdelV., I. E., et al (2016). Hydrophilic antioxidants from Andean tomato landraces assessed by their bioactivities in vitro and in vivo. Food Chemistry, 206, 146–155.  https://doi.org/10.1016/j.foodchem.2016.03.027.CrossRefPubMedGoogle Scholar
  29. Diepenbrock, C. H., Kandianis, C. B., Lipka, A. E., Magallanes-Lundback, M., Vaillancourt, B., Gongora-Castillo, E., Wallace, J. G., Cepela, J., Mesberg, A., Bradbury, P. J., Ilut, D. C., Mateos-Hernandez, M., Hamilton, J., Owens, B. F., Tiede, T., Buckler, E. S., Rocheford, T., Buell, C. R., Gore, M. A., & DellaPenna, D. (2017). Novel loci underlie natural variation in vitamin E levels in maize grain. The Plant Cell, 29, 2374–2392.PubMedPubMedCentralGoogle Scholar
  30. Domingos, S., Fino, J., Cardoso, V., Sanchez, C., Ramalho, J. C., Larcher, R., Paulo, O. S., Oliveira, C. M., & Goulao, L. F. (2016). Shared and divergent pathways for flower abscission are triggered by gibberellic acid and carbon starvation in seedless Vitis vinifera L. BMC Plant Biology, 16, 38.PubMedPubMedCentralGoogle Scholar
  31. FAO. Food and Agriculture Organisation. (2009). How to feed the world in 2050. Insights from an Expert Meeting at FAO, 2050(1), 1–35.  https://doi.org/10.1111/j.1728-4457.2009.00312.x.CrossRefGoogle Scholar
  32. FAO. Food and Agriculture Organisation. (2017). The future of food and agriculture: Trends and challenges. http://www.fao.org/3/a-i6583e.pdf.
  33. Feng, J., Long, Y., Shi, L., Shi, J., Barker, G., & Meng, J. (2012). Characterization of metabolite quantitative trait loci and metabolic networks that control glucosinolate concentration in the seeds and leaves of Brassica napus. The New Phytologist, 193, 96–108.PubMedGoogle Scholar
  34. Flamini, R., De Rosso, M., & Bavaresco, L. (2015) Study of grape polyphenols by liquid chromatography-high-resolution mass spectrometry (UHPLC/QTOF) and suspect screening analysis. Journal of Analytical Methods in Chemistry, 2015, 350259.Google Scholar
  35. Freund, D. M., & Hegeman, A. D. (2017). Recent advances in stable isotope-enabled mass spectrometry-based plant metabolomics. Current Opinion in Biotechnology, 43, 41–48.PubMedGoogle Scholar
  36. Fukushima, A., & Kusano, M. (2014). A network perspective on nitrogen metabolism from model to crop plants using integrated “omics” approaches. Journal of Experimental Botany, 65(19), 5619–5630.  https://doi.org/10.1093/jxb/eru322.CrossRefPubMedGoogle Scholar
  37. Garbowicz, K., Liu, Z., Alseekh, S., Tieman, D., Taylor, M., Kuhalskaya, A., Ofner, I., Zamir, D., Klee, H. J., Fernie, A. R., & Brotman, Y. (2018) Quantitative trait loci analysis identifies a prominent gene involved in the production of fatty-acid-derived flavor volatiles in tomato. Molecular Plant. S1674-2052(18)30190-4.Google Scholar
  38. Gargallo-Garriga, A., Ayala-Roque, M., Sardans, J., Bartrons, M., Granda, V., Sigurdsson, B. D., Leblans, N. I. W., Oravec, M., Urban, O., Janssens, I. A., & Penuelas, J. (2017) Impact of soil warming on the plant metabolome of icelandic grasslands. Metabolites, 7.Google Scholar
  39. Ghaffari, M. R., Shahinnia, F., Usadel, B., Junker, B., Schreiber, F., Sreenivasulu, N., & Hajirezaei, M. R. (2016). The metabolic signature of biomass formation in barley. Plant & Cell Physiology, 57, 1943–1960.Google Scholar
  40. Gong, L., Chen, W., Gao, Y., Liu, X., Zhang, H., Xu, C., Yu, S., Zhang, Q., & Luo, J. (2013). Genetic analysis of the metabolome exemplified using a rice population. Proceedings of the National Academy of Sciences of the United States of America, 110, 20320–20325.PubMedPubMedCentralGoogle Scholar
  41. Harrigan, G. G., Venkatesh, T. V., Leibman, M., Blankenship, J., Perez, T., Halls, S., Chassy, A. W., Fiehn, O., Xu, Y., & Goodacre, R. (2016). Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait. Metabolomics, 12, 82.PubMedPubMedCentralGoogle Scholar
  42. Hatoum, D., Annaratone, C., Hertog, M.L.A.T.M., Geeraerd, A. H., & Nicolai, B. M. (2014). Targeted metabolomics study of ‘Braeburn’ apples during long-term storage. Postharvest Biology and Technology, 96, 33–41.Google Scholar
  43. Hatoum, D., Hertog, M. L. A. T. M., Geeraerd, A. H., & Nicolai, B. M. (2016). Effect of browning related pre- and postharvest factors on the ‘Braeburn’ apple metabolome during CA storage. Postharvest Biology and Technology, 111, 106–116.  https://doi.org/10.1016/j.postharvbio.2015.08.004.CrossRefGoogle Scholar
  44. Hu, C., Shi, J., Quan, S., Cui, B., Kleessen, S., Nikoloski, Z., Tohge, T., Alexander, D., Guo, L., Lin, H., Wang, J., Cui, X., Rao, J., Luo, Q., Zhao, X., Fernie, A. R., & Zhang, D. (2014). Metabolic variation between japonica and indica rice cultivars as revealed by non-targeted metabolomics. Scientific Reports, 4, 5067.PubMedPubMedCentralGoogle Scholar
  45. Hu, C., Tohge, T., Chan, S.-A., Song, Y., Rao, J., Cui, B., et al. (2016). Identification of conserved and diverse metabolic shifts during rice grain development. Scientific Reports, 6, 20942.  https://doi.org/10.1038/srep20942.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Jiang, K., Liberatore, K. L., Park, S. J., Alvarez, J. P., & Lippman, Z. B. (2013). Tomato yield heterosis is triggered by a dosage sensitivity of the florigen pathway that fine-tunes shoot architecture. PLoS Genetics, 9, e1004043.  https://doi.org/10.1371/journal.pgen.1004043.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Justes, E., Mary, B., & Meynard, J. M. (1997). Evaluation of a nitrate test indicator to improve the nitrogen fertilisation of winter wheat crops, diagnostic procedures for crop N management. Proceedings of a workshop held in Poitiers, France, 22–23 November, 1995 Paris, France. Institut National de la Recherche Agronomique (INRA) (pp. 93–110).Google Scholar
  48. Kaplan, F., Kopka, J., Haskell, D. W., Zhao, W., Schiller, K. C., Gatzke, N., Sung, D. Y., & Guy, C. L. (2004). Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiology, 136, 4159–4168.PubMedPubMedCentralGoogle Scholar
  49. Kelly, G., Moshelion, M., David-Schwartz, R., Halperin, O., Wallach, R., Attia, Z., Belausov, E., & Granot, D. (2013). Hexokinase mediates stomatal closure. The Plant Journal: For Cell and Molecular Biology, 75, 977–988.Google Scholar
  50. Kim, J. M., To, T. K., Matsui, A., Tanoi, K., Kobayashi, N. I., Matsuda, F., Habu, Y., Ogawa, D., Sakamoto, T., Matsunaga, S., Bashir, K., Rasheed, S., Ando, M., Takeda, H., Kawaura, K., Kusano, M., Fukushima, A., Endo, T. A., Kuromori, T., Ishida, J., Morosawa, T., Tanaka, M., Torii, C., Takebayashi, Y., Sakakibara, H., Ogihara, Y., Saito, K., Shinozaki, K., Devoto, A., & Seki, M. (2017). Acetate-mediated novel survival strategy against drought in plants. Nature Plants, 3, 17097.PubMedGoogle Scholar
  51. Kong, L., Xie, Y., Hu, L., Si, J., & Wang, Z. (2017). Excessive nitrogen application dampens antioxidant capacity and grain filling in wheat as revealed by metabolic and physiological analyses. Scientific Reports, 7, 43363.PubMedPubMedCentralGoogle Scholar
  52. Korkina, L. G. (2007). Phenylpropanoids as naturally occurring antioxidants: From plant defense to human health. Cellular and Molecular Biology (Noisy-le-Grand, France), 53(1), 15–25.Google Scholar
  53. Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A. (2017). Metabolomics for plant improvement: Status and prospects. Frontiers in Plant Science, 8, 1302.PubMedPubMedCentralGoogle Scholar
  54. Kusano, M., Fukushima, A., Kobayashi, M., Hayashi, N., Jonsson, P., Moritz, T., Ebana, K., & Saito, K. (2007). Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 855, 71–79.PubMedGoogle Scholar
  55. Kusano, M., Yang, Z., Okazaki, Y., Nakabayashi, R., Fukushima, A., & Saito, K. (2015). Using metabolomic approaches to explore chemical diversity in rice. Molecular Plant, 8, 58–67.PubMedGoogle Scholar
  56. Lakshmanan, M., Lim, S.-H., Mohanty, B., Kim, J. K., Ha, S.-H., & Lee, D.-Y. (2015). Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis. Plant Physiology, 169, 01379.  https://doi.org/10.1104/pp.15.01379. 2015.CrossRefGoogle Scholar
  57. Lancien, M., Gadal, P., & Hodges, M. (2000). Enzyme redundancy and the importance of 2-oxoglutarate in higher plant ammonium assimilation. Plant Physiology, 123(3), 817–824.  https://doi.org/10.1104/pp.123.3.817.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Li, B., Zhang, Y., Mohammadi, S. A., Huai, D., Zhou, Y., & Kliebenstein, D. J. (2016a). An integrative genetic study of rice metabolism, growth and stochastic variation reveals potential C/N partitioning loci. Scientific Reports, 6, 30143.PubMedPubMedCentralGoogle Scholar
  59. Li, M., Li, D., Feng, F., Zhang, S., Ma, F., & Cheng, L. (2016b). Proteomic analysis reveals dynamic regulation of fruit development and sugar and acid accumulation in apple. Journal of Experimental Botany, 67, 5145–5157.PubMedGoogle Scholar
  60. Lin, T., Zhu, G., Zhang, J., Xu, X., Yu, Q., Zheng, Z., Zhang, Z., Lun, Y., Li, S., Wang, X., Huang, Z., Li, J., Zhang, C., Wang, T., Zhang, Y., Wang, A., Zhang, Y., Lin, K., Li, C., Xiong, G., Xue, Y., Mazzucato, A., Causse, M., Fei, Z., Giovannoni, J. J., Chetelat, R. T., Zamir, D., Stadler, T., Li, J., Ye, Z., Du, Y., & Huang, S. (2014). Genomic analyses provide insights into the history of tomato breeding. Nature Genetics, 46, 1220–1226.PubMedGoogle Scholar
  61. Lipka, A. E., Gore, M. A., Magallanes-Lundback, M., Mesberg, A., Lin, H., Tiede, T., Chen, C., Buell, C. R., Buckler, E. S., Rocheford, T., & DellaPenna, D. (2013) Genome-wide association study and pathway-level analysis of tocochromanol levels in maize grain. G3 (Bethesda, Md.), 3, 1287–1299.Google Scholar
  62. Lisec, J., Romisch-Margl, L., Nikoloski, Z., Piepho, H. P., Giavalisco, P., Selbig, J., Gierl, A., & Willmitzer, L. (2011). Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. The Plant Journal: For Cell and Molecular Biology, 68, 326–336.Google Scholar
  63. Liu, M. Y., Burgos, A., Ma, L., Zhang, Q., Tang, D., & Ruan, J. (2017). Lipidomics analysis unravels the effect of nitrogen fertilization on lipid metabolism in tea plant (Camellia sinensis L.). BMC Plant Biology, 17, 165.PubMedPubMedCentralGoogle Scholar
  64. Llorente, B., Alonso, G. D., Bravo-Almonacid, F., Rodriguez, V., Lopez, M. G., Carrari, F., Torres, H. N., & Flawia, M. M. (2011). Safety assessment of nonbrowning potatoes: Opening the discussion about the relevance of substantial equivalence on next generation biotech crops. Plant Biotechnology Journal, 9, 136–150.PubMedGoogle Scholar
  65. López, M. G., Zanor, M. I., Pratta, G. R., Stegmayer, G., Boggio, S. B., Conte, M., Bermúdez, L., Leskow, C., Rodríguez, C., Picardi, G. R., Zorzoli, L. A., Fernie, R., Milone, A. R., Asís, D., Valle, R., E.M. and Carrari, F. (2015). Metabolic analyses of interspecific tomato recombinant inbred lines for fruit quality improvement. Metabolomics, 11, 1416–1431.Google Scholar
  66. Matsuda, F., Okazaki, Y., Oikawa, A., Kusano, M., Nakabayashi, R., Kikuchi, J., Yonemaru, J., Ebana, K., Yano, M., & Saito, K. (2012). Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. The Plant Journal: For Cell and Molecular Biology, 70, 624–636.Google Scholar
  67. Meyer, R. C., Steinfath, M., Lisec, J., Becher, M., Witucka-Wall, H., Torjek, O., Fiehn, O., Eckardt, A., Willmitzer, L., Selbig, J., & Altmann, T. (2007). The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America, 104, 4759–4764.PubMedPubMedCentralGoogle Scholar
  68. Mikulic-Petkovsek, M., Schmitzer, V., Slatnar, A., Weber, N., Veberic, R., Stampar, F., Munda, A., & Koron, D. (2013). Alteration of the content of primary and secondary metabolites in strawberry fruit by Colletotrichum nymphaeae infection. Journal of Agricultural and Food Chemistry, 61, 5987–5995.PubMedGoogle Scholar
  69. Misra, B. B., Acharya, B. R., Granot, D., Assmann, S. M., & Chen, S. (2015). The guard cell metabolome: Functions in stomatal movement and global food security. Frontiers in Plant Science, 6, 334.PubMedPubMedCentralGoogle Scholar
  70. Misra, B. B., Assmann, S. M., & Chen, S. (2014). Plant single-cell and single-cell-type metabolomics. Trends in Plant Science, 19, 637–646.PubMedGoogle Scholar
  71. Misyura, M., Guevara, D., Subedi, S., Hudson, D., McNicholas, P. D., Colasanti, J., & Rothstein, S. J. (2014). Nitrogen limitation and high density responses in rice suggest a role for ethylene under high density stress. BMC Genomics, 15, 681.PubMedPubMedCentralGoogle Scholar
  72. Moghissi, A. A., Pei, S., & Liu, Y. (2016). Golden rice: Scientific, regulatory and public information processes of a genetically modified organism. Critical Reviews in Biotechnology.  https://doi.org/10.3109/07388551.2014.993586.CrossRefPubMedGoogle Scholar
  73. Moschen, S., Di Rienzo, J. A., Higgins, J., Tohge, T., Watanabe, M., Gonzalez, S., Rivarola, M., Garcia-Garcia, F., Dopazo, J., Hopp, H. E., Hoefgen, R., Fernie, A. R., Paniego, N., Fernandez, P., & Heinz, R. A. (2017). Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Molecular Biology, 94, 549–564.PubMedGoogle Scholar
  74. Nagpala, E. G., Guidarelli, M., Gasperotti, M., Masuero, D., Bertolini, P., Vrhovsek, U., & Baraldi, E. (2016). Polyphenols variation in fruits of the susceptible strawberry cultivar alba during ripening and upon fungal pathogen interaction and possible involvement in unripe fruit tolerance. Journal of Agricultural and Food Chemistry, 64(9), 1869–1878.  https://doi.org/10.1021/acs.jafc.5b06005.CrossRefPubMedGoogle Scholar
  75. Nakabayashi, R., & Saito, K. (2015). Integrated metabolomics for abiotic stress responses in plants. Current Opinion in Plant Biology, 24, 10–16.PubMedGoogle Scholar
  76. Nardozza, S., Boldingh, H. L., Osorio, S., Hohne, M., Wohlers, M., Gleave, A. P., MacRae, E. A., Richardson, A. C., Atkinson, R. G., Sulpice, R., Fernie, A. R., & Clearwater, M. J. (2013). Metabolic analysis of kiwifruit (Actinidia deliciosa) berries from extreme genotypes reveals hallmarks for fruit starch metabolism. Journal of Experimental Botany, 64, 5049–5063.PubMedPubMedCentralGoogle Scholar
  77. Nielsen, L. J., Stuart, P., Picmanova, M., Rasmussen, S., Olsen, C. E., Harholt, J., Moller, B. L., & Bjarnholt, N. (2016). Dhurrin metabolism in the developing grain of Sorghum bicolor (L.) Moench investigated by metabolite profiling and novel clustering analyses of time-resolved transcriptomic data. BMC Genomics, 17, 1021.PubMedPubMedCentralGoogle Scholar
  78. Obata, T., & Fernie, A. R. (2012). The use of metabolomics to dissect plant responses to abiotic stresses. Cellular and Molecular Life Sciences: CMLS, 69, 3225–3243.PubMedGoogle Scholar
  79. Obata, T., Witt, S., Lisec, J., Palacios-Rojas, N., Florez-Sarasa, I., Yousfi, S., Araus, J. L., Cairns, J. E., & Fernie, A. R. (2015). Metabolite profiles of maize leaves in drought, heat, and combined stress field trials reveal the relationship between metabolism and grain yield. Plant Physiology, 169, 2665–2683.PubMedPubMedCentralGoogle Scholar
  80. Ogawa, T., Kashima, K., Yuki, Y., Mejima, M., Kurokawa, S., Kuroda, M., Okazawa, A., Kiyono, H., & Ohta, D. (2017). Seed metabolome analysis of a transgenic rice line expressing cholera toxin B-subunit. Scientific Reports, 7, 5196.PubMedPubMedCentralGoogle Scholar
  81. Ogbaga, C. C., Stepien, P., Dyson, B. C., Rattray, N. J., Ellis, D. I., Goodacre, R., & Johnson, G. N. (2016). Biochemical analyses of sorghum varieties reveal differential responses to drought. PLoS ONE, 11, e0154423.PubMedPubMedCentralGoogle Scholar
  82. Oikawa, A., Otsuka, T., Nakabayashi, R., Jikumaru, Y., Isuzugawa, K., Murayama, H., Saito, K., & Shiratake, K. (2015). Metabolic profiling of developing pear fruits reveals dynamic variation in primary and secondary metabolites, including plant hormones. PLoS ONE, 10, e0131408.PubMedPubMedCentralGoogle Scholar
  83. Okazaki, Y., Otsuki, H., Narisawa, T., Kobayashi, M., Sawai, S., Kamide, Y., Kusano, M., Aoki, T., Hirai, M. Y., & Saito, K. (2013). A new class of plant lipid is essential for protection against phosphorus depletion. Nature Communications, 4, 1510.PubMedPubMedCentralGoogle Scholar
  84. Okazaki, Y., & Saito, K. (2016). Integrated metabolomics and phytochemical genomics approaches for studies on rice. GigaScience, 5, 11.PubMedPubMedCentralGoogle Scholar
  85. Owens, B. F., Lipka, A. E., Magallanes-Lundback, M., Tiede, T., Diepenbrock, C. H., Kandianis, C. B., Kim, E., Cepela, J., Mateos-Hernandez, M., Buell, C. R., Buckler, E. S., DellaPenna, D., Gore, M. A., & Rocheford, T. (2014). A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics, 198, 1699–1716.PubMedPubMedCentralGoogle Scholar
  86. Pan, Z., Zeng, Y., An, J., Ye, J., Xu, Q., & Deng, X. (2012). An integrative analysis of transcriptome and proteome provides new insights into carotenoid biosynthesis and regulation in sweet orange fruits. Journal of Proteomics, 75, 2670–2684.PubMedGoogle Scholar
  87. Patrick, J. W., Botha, F. C., & Birch, R. G. (2013). Metabolic engineering of sugars and simple sugar derivatives in plants. Plant Biotechnology Journal.  https://doi.org/10.1111/pbi.12002.CrossRefPubMedGoogle Scholar
  88. Peng, M., Ying, P., Liu, X., Li, C., Xia, R., Li, J., & Zhao, M. (2017). Genome-wide identification of histone modifiers and their expression patterns during fruit abscission in litchi. Frontiers in Plant Science, 8, 639.PubMedPubMedCentralGoogle Scholar
  89. Perez-Fons, L., Wells, T., Corol, D. I., Ward, J. L., Gerrish, C., Beale, M. H., Seymour, G. B., Bramley, P. M., & Fraser, P. D. (2014). A genome-wide metabolomic resource for tomato fruit from Solanum pennellii. Scientific Reports, 4, 3859.PubMedPubMedCentralGoogle Scholar
  90. Powell, J. P., & Reinhard, S. (2016). Measuring the effects of extreme weather events on yields. Weather and Climate Extremes, 12, 69–79.  https://doi.org/10.1016/j.wace.2016.02.003.CrossRefGoogle Scholar
  91. Pretty, J., & Bharucha, Z. P. (2014). Sustainable intensification in agricultural systems. Annals of Botany, 114(8), 1571–1596.  https://doi.org/10.1093/aob/mcu205.CrossRefPubMedPubMedCentralGoogle Scholar
  92. Price, E. J., Bhattacharjee, R., Lopez-Montes, A., & Fraser, P. D. (2017). Metabolite profiling of yam (Dioscorea spp.) accessions for use in crop improvement programmes. Metabolomics, 13, 144.PubMedPubMedCentralGoogle Scholar
  93. Qi, X., Xu, W., Zhang, J., Guo, R., Zhao, M., Hu, L., Wang, H., Dong, H., & Li, Y. (2017). Physiological characteristics and metabolomics of transgenic wheat containing the maize C4 phosphoenolpyruvate carboxylase (PEPC) gene under high temperature stress. Protoplasma, 254, 1017–1030.PubMedGoogle Scholar
  94. Quadrana, L., Almeida, J., Asis, R., Duffy, T., Dominguez, P. G., Bermudez, L., Conti, G., Correa da Silva, J. V., Peralta, I. E., Colot, V., Asurmendi, S., Fernie, A. R., Rossi, M., & Carrari, F. (2014). Natural occurring epialleles determine vitamin E accumulation in tomato fruits. Nature Communications, 5, 3027.PubMedGoogle Scholar
  95. Quan, X., Zeng, J., Han, Z., & Zhang, G. (2017). Ionomic and physiological responses to low nitrogen stress in Tibetan wild and cultivated barley. Plant Physiology and Biochemistry, 111, 257–265.  https://doi.org/10.1016/j.plaphy.2016.12.008.CrossRefPubMedGoogle Scholar
  96. Ramalingam, A., Kudapa, H., Pazhamala, L. T., Weckwerth, W., & Varshney, R. K. (2015). Proteomics and metabolomics: Two emerging areas for legume improvement. Frontiers in Plant Science, 6, 1116.PubMedPubMedCentralGoogle Scholar
  97. Ranjbar Sistani, N., Kaul, H. P., Desalegn, G., & Wienkoop, S. (2017) Rhizobium impacts on seed productivity, quality, and protection of Pisum sativum upon disease stress caused by Didymella pinodes: Phenotypic, proteomic, and metabolomic traits. Frontiers in Plant Science, 8, 1961.Google Scholar
  98. Rao, J., Cheng, F., Hu, C., Quan, S., Lin, H., Wang, J., Chen, G., Zhao, X., Alexander, D., Guo, L., Wang, G., Lai, J., Zhang, D., & Shi, J. (2014). Metabolic map of mature maize kernels. Metabolomics, 10, 775–787.Google Scholar
  99. Raun, W., Solie, J. B., Johnson, G. V., Stone, M., Mullen, R. W., Freeman, K. W., Thomason, W., & Lukina, E. V. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94, 815–820.Google Scholar
  100. Redestig, H., Kusano, M., Ebana, K., Kobayashi, M., Oikawa, A., Okazaki, Y., Matsuda, F., Arita, M., Fujita, N., & Saito, K. (2011). Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics. BMC Systems Biology, 5, 176.PubMedPubMedCentralGoogle Scholar
  101. Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J., Technow, F., Sulpice, R., et al. (2012a). Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics, 44(2), 217–220.  https://doi.org/10.1038/ng.1033.
  102. Riedelsheimer, C., Lisec, J., Czedik-Eysenberg, A., Sulpice, R., Flis, A., Grieder, C., Altmann, T., Stitt, M., Willmitzer, L., & Melchinger, A. E. (2012b). Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proceedings of the National Academy of Sciences of the United States of America, 109, 8872–8877.Google Scholar
  103. Rossi, M., Bermudez, L., & Carrari, F. (2015). Crop yield: Challenges from a metabolic perspective. Current Opinion in Plant Biology, 25, 79–89.PubMedGoogle Scholar
  104. Safronov, O., Kreuzwieser, J., Haberer, G., Alyousif, M. S., Schulze, W., Al-Harbi, N., Arab, L., Ache, P., Stempfl, T., Kruse, J., Mayer, K. X., Hedrich, R., Rennenberg, H., Salojarvi, J., & Kangasjarvi, J. (2017). Detecting early signs of heat and drought stress in Phoenix dactylifera (date palm). PLoS ONE, 12, e0177883.PubMedPubMedCentralGoogle Scholar
  105. Sauvage, C., Segura, V., Bauchet, G., Stevens, R., Do, P. T., Nikoloski, Z., Fernie, A. R., & Causse, M. (2014). Genome-wide association in tomato reveals 44 candidate loci for fruit metabolic traits. Plant Physiology, 165, 1120–1132.PubMedPubMedCentralGoogle Scholar
  106. Sayre, R., Beeching, J. R., Cahoon, E. B., Egesi, C., Fauquet, C., Fellman, J., et al. (2011). The BioCassava plus program: Biofortification of cassava for sub-saharan Africa. Annual Review of Plant Biology.  https://doi.org/10.1146/annurev-arplant-042110-103751.CrossRefPubMedGoogle Scholar
  107. Schauer, N., Semel, Y., Balbo, I., Steinfath, M., Repsilber, D., Selbig, J., Pleban, T., Zamir, D., & Fernie, A. R. (2008). Mode of inheritance of primary metabolic traits in tomato. The Plant Cell, 20, 509–523.PubMedPubMedCentralGoogle Scholar
  108. Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., Pleban, T., Perez-Melis, A., Bruedigam, C., Kopka, J., Willmitzer, L., Zamir, D., & Fernie, A. R. (2006). Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology, 24, 447–454.PubMedGoogle Scholar
  109. Shelden, M. C., Dias, D. A., Jayasinghe, N. S., Bacic, A., & Roessner, U. (2016). Root spatial metabolite profiling of two genotypes of barley (Hordeum vulgare L.) reveals differences in response to short-term salt stress. Journal of Experimental Botany, 67, 3731–3745.PubMedPubMedCentralGoogle Scholar
  110. Shen, Q., Fu, L., Dai, F., Jiang, L., Zhang, G., & Wu, D. (2016). Multi-omics analysis reveals molecular mechanisms of shoot adaption to salt stress in Tibetan wild barley. BMC Genomics, 17, 889.PubMedPubMedCentralGoogle Scholar
  111. Shimojima, M., Madoka, Y., Fujiwara, R., Murakawa, M., Yoshitake, Y., Ikeda, K., Koizumi, R., Endo, K., Ozaki, K., & Ohta, H. (2015). An engineered lipid remodeling system using a galactolipid synthase promoter during phosphate starvation enhances oil accumulation in plants. Frontiers in Plant Science, 6, 664.PubMedPubMedCentralGoogle Scholar
  112. Son, H.-S., Hwang, G.-S., Kim, K. M., Ahn, H.-J., Park, W.-M., Van Den Berg, F., et al. (2009). Metabolomic studies on geographical grapes and their wines using 1H NMR analysis coupled with multivariate statistics. Journal of Agricultural and Food Chemistry, 57(4), 1481–1490.  https://doi.org/10.1021/jf803388w.CrossRefPubMedGoogle Scholar
  113. Sonawane, P. D., Pollier, J., Panda, S., Szymanski, J., Massalha, H., Yona, M., Unger, T., Malitsky, S., Arendt, P., Pauwels, L., Almekias-Siegl, E., Rogachev, I., Meir, S., Cardenas, P. D., Masri, A., Petrikov, M., Schaller, H., Schaffer, A. A., Kamble, A., Giri, A. P., Goossens, A., & Aharoni, A. (2016). Plant cholesterol biosynthetic pathway overlaps with phytosterol metabolism. Nature Plants, 3, 16205.PubMedGoogle Scholar
  114. Sonnewald, U., & Fernie, A. R. (2018). Next-generation strategies for understanding and influencing source-sink relations in crop plants. Current Opinion in Plant Biology, 43, 63–70.PubMedGoogle Scholar
  115. Stitt, M., & Schulze, D. (1994). Does Rubisco control the rate of photosynthesis and plant growth? An exercise in molecular ecophysiology. Plant, Cell & Environment, 17, 465–487.  https://doi.org/10.1111/j.1365-3040.1994.tb00144.x.CrossRefGoogle Scholar
  116. Stoop, J. M. H., Williamson, J. D., & Mason Pharr, D. (1996). Mannitol metabolism in plants: A method for coping with stress. Trends in Plant Science, 1, 139–144.Google Scholar
  117. Sun, M., Yang, Z., & Wawrik, B. (2018). Metabolomic fingerprints of individual algal cells using the single-probe mass spectrometry technique. Frontiers in Plant Science, 9, 571.PubMedPubMedCentralGoogle Scholar
  118. Sweetlove, L. J., Beard, K. F., Nunes-Nesi, A., Fernie, A. R., & Ratcliffe, R. G. (2010). Not just a circle: Flux modes in the plant TCA cycle. Trends in Plant Science, 15, 462–470.PubMedGoogle Scholar
  119. Tatsis, E. C., & O’Connor, S. E. (2016). New developments in engineering plant metabolic pathways. Current Opinion in Biotechnology.  https://doi.org/10.1016/j.copbio.2016.04.012.CrossRefPubMedGoogle Scholar
  120. The Tomato Genome Consortium. (2012). The tomato genome sequence provides insights into fleshy fruit evolution. Nature, 485, 635–641.Google Scholar
  121. Tieman, D., Zhu, G., Resende, M. F. Jr., Lin, T., Nguyen, C., Bies, D., Rambla, J. L., Beltran, K. S., Taylor, M., Zhang, B., Ikeda, H., Liu, Z., Fisher, J., Zemach, I., Monforte, A., Zamir, D., Granell, A., Kirst, M., Huang, S., & Klee, H. (2017). A chemical genetic roadmap to improved tomato flavor. Science, 355, 391–394.PubMedGoogle Scholar
  122. Tohge, T., & Fernie, A. R. (2015). Metabolomics-inspired insight into developmental, environmental and genetic aspects of tomato fruit chemical composition and quality. Plant & Cell Physiology, 56, 1681–1696.Google Scholar
  123. Tohge, T., Scossa, F., & Fernie, A. R. (2015). Integrative approaches to enhance understanding of plant metabolic pathway structure and regulation. Plant Physiology, 169(3), 1499–1511.PubMedPubMedCentralGoogle Scholar
  124. Topfer, N., Kleessen, S., & Nikoloski, Z. (2015). Integration of metabolomics data into metabolic networks. Frontiers in Plant Science, 6, 49.PubMedPubMedCentralGoogle Scholar
  125. Turner, M., Heuberger, A., Kirkwood, J., Collins, C., Wolfrum, C., Broeckling, E., Prenni, C., J. and Jahn, C. (2016). Non-targeted metabolomics in diverse sorghum breeding lines indicates primary and secondary metabolite profiles are associated with plant biomass accumulation and photosynthesis. Frontiers in Plant Science, 7, 953.PubMedPubMedCentralGoogle Scholar
  126. Tuttle, J. R., Nah, G., Duke, M. V., Alexander, D. C., Guan, X., Song, Q., et al. (2015). Metabolomic and transcriptomic insights into how cotton fiber transitions to secondary wall synthesis, represses lignification, and prolongs elongation. BMC Genomics, 16(1), 1–28.  https://doi.org/10.1186/s12864-015-1708-9.CrossRefGoogle Scholar
  127. Uddling, J., Gelang-Alfredsson, J., Karlsson, P. E., Selldén, G., & Pleijel, H. (2008). Source–sink balance of wheat determines responsiveness of grain production to increased [CO2] and water supply. Agriculture, Ecosystems and Environment, 127, 215–222.Google Scholar
  128. Upadhyaya, P., Tyagi, K., Sarma, S., Tamboli, V., Sreelakshmi, Y., & Sharma, R. (2017). Natural variation in folate levels among tomato (Solanum lycopersicum) accessions. Food Chemistry, 217, 610–619.  https://doi.org/10.1016/j.foodchem.2016.09.031.CrossRefPubMedGoogle Scholar
  129. Venkatesh, T. V., Chassy, A. W., Fiehn, O., Flint-Garcia, S., Zeng, Q., Skogerson, K., & Harrigan, G. G. (2016). Metabolomic assessment of key maize resources: GC-MS and NMR profiling of grain from B73 hybrids of the nested association mapping (NAM) founders and of geographically diverse landraces. Journal of Agricultural and Food Chemistry, 64, 2162–2172.PubMedGoogle Scholar
  130. Vimolmangkang, S., Zheng, H., Peng, Q., Jiang, Q., Wang, H., Fang, T., et al. (2016). Assessment of sugar components and genes involved in the regulation of sucrose accumulation in peach fruit. Journal of Agricultural and Food Chemistry, 64(35), 6723–6729.  https://doi.org/10.1021/acs.jafc.6b02159.CrossRefPubMedGoogle Scholar
  131. Vital, C. E., Giordano, A., de Almeida Soares, E., Williams, R., Mesquita, T. C., Vidigal, R. O., de Santana Lopes, P. M. P., Pacheco, A., Rogalski, T. G., M., de O. Ramos, H.J. and Loureiro, M. E. (2017). An integrative overview of the molecular and physiological responses of sugarcane under drought conditions. Plant Molecular Biology, 94, 577–594.PubMedGoogle Scholar
  132. Wang, H., Xu, S., Fan, Y., Liu, N., Zhan, W., Liu, H., Xiao, Y., Li, K., Pan, Q., Li, W., Deng, M., Liu, J., Jin, M., Yang, X., Li, J., Li, Q., & Yan, J. (2018). Beyond pathways: Genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses. Plant Biotechnology Journal, 16, 1464–1475.PubMedPubMedCentralGoogle Scholar
  133. Wang, X., Zhu, W., Hashiguchi, A., Nishimura, M., Tian, J., & Komatsu, S. (2017). Metabolic profiles of flooding-tolerant mechanism in early-stage soybean responding to initial stress. Plant Molecular Biology, 94, 669–685.PubMedGoogle Scholar
  134. Wen, W., Jin, M., Li, K., Liu, H., Xiao, Y., Zhao, M., Alseekh, S., Li, W., de Abreu, E. L. F., Brotman, Y., Willmitzer, L., Fernie, A. R., & Yan, J. (2018). An integrated multi-layered analysis of the metabolic networks of different tissues uncovers key genetic components of primary metabolism in maize. The Plant Journal: For Cell and Molecular Biology, 93, 1116–1128.Google Scholar
  135. Wen, W., Li, K., Alseekh, S., Omranian, N., Zhao, L., Zhou, Y., Xiao, Y., Jin, M., Yang, N., Liu, H., Florian, A., Li, W., Pan, Q., Nikoloski, Z., Yan, J., & Fernie, A. R. (2015). Genetic determinants of the network of primary metabolism and their relationships to plant performance in a maize recombinant inbred line population. The Plant Cell, 27, 1839–1856.PubMedPubMedCentralGoogle Scholar
  136. Xu, S., Xu, Y., Gong, L., & Zhang, Q. (2016). Metabolomic prediction of yield in hybrid rice. The Plant Journal: For Cell and Molecular Biology, 88, 219–227.Google Scholar
  137. Yang, F., Xu, X., Wang, W., Ma, J., Wei, D., He, P., Pampolino, M. F., & Johnston, A. M. (2017). Estimating nutrient uptake requirements for soybean using QUEFTS model in China. PLoS ONE, 12, e0177509.PubMedPubMedCentralGoogle Scholar
  138. Yang, X., Feng, L., Zhao, L., Liu, X., Hassani, D., & Huang, D. (2018). Effect of glycine nitrogen on lettuce growth under soilless culture: A metabolomics approach to identify the main changes occurred in plant primary and secondary metabolism. Journal of the Science of Food and Agriculture, 98, 467–477.PubMedGoogle Scholar
  139. Yang, X., Nian, J., Xie, Q., Feng, J., Zhang, F., Jing, H., Zhang, J., Dong, G., Liang, Y., Peng, J., Wang, G., Qian, Q., & Zuo, J. (2016). Rice ferredoxin-dependent glutamate synthase regulates nitrogen-carbon metabolomes and is genetically differentiated between japonica and indica subspecies. Molecular Plant, 9, 1520–1534.PubMedGoogle Scholar
  140. Ye, X., & Beyer, P. (2000). Engineering the provitamin A (β-carotene) biosynthetic pathway into (carotenoid-free) rice endosperm. Science.  https://doi.org/10.1126/science.287.5451.303.CrossRefPubMedGoogle Scholar
  141. Yesbergenova-Cuny, Z., Dinant, S., Martin-Magniette, M. L., Quillere, I., Armengaud, P., Monfalet, P., Lea, P. J., & Hirel, B. (2016). Genetic variability of the phloem sap metabolite content of maize (Zea mays L.) during the kernel-filling period. Plant Science: An International Journal of Experimental Plant Biology, 252, 347–357.Google Scholar
  142. Ying, J. Z., Shan, J. X., Gao, J. P., Zhu, M. Z., Shi, M., & Lin, H. X. (2012). Identification of quantitative trait Loci for lipid metabolism in rice seeds. Molecular Plant, 5, 865–875.PubMedGoogle Scholar
  143. Yonekura-Sakakibara, K., & Saito, K. (2006). Review: Genetically modified plants for the promotion of human health. Biotechnology Letters.  https://doi.org/10.1007/s10529-006-9194-4.CrossRefPubMedGoogle Scholar
  144. Zhang, J., Luo, W., Zhao, Y., Xu, Y., Song, S., & Chong, K. (2016). Comparative metabolomic analysis reveals a reactive oxygen species-dominated dynamic model underlying chilling environment adaptation and tolerance in rice. The New Phytologist, 211, 1295–1310.PubMedGoogle Scholar
  145. Zhang, N., Venkateshwaran, M., Boersma, M., Harms, A., Howes-Podoll, M., den Os, D., Ane, J. M., & Sussman, M. R. (2012). Metabolomic profiling reveals suppression of oxylipin biosynthesis during the early stages of legume-rhizobia symbiosis. FEBS Letters, 586, 3150–3158.PubMedGoogle Scholar
  146. Zhang, Y., Butelli, E., Alseekh, S., Tohge, T., Rallapalli, G., Luo, J., et al. (2015). Multi-level engineering facilitates the production of phenylpropanoid compounds in tomato. Nature Communications.  https://doi.org/10.1038/ncomms9635.CrossRefPubMedPubMedCentralGoogle Scholar
  147. Zhao, Y., Li, Z., Liu, G., Jiang, Y., Maurer, H. P., Wurschum, T., Mock, H. P., Matros, A., Ebmeyer, E., Schachschneider, R., Kazman, E., Schacht, J., Gowda, M., Longin, C. F., & Reif, J. C. (2015). Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding. Proceedings of the National Academy of Sciences of the United States of America, 112, 15624–15629.PubMedPubMedCentralGoogle Scholar
  148. Zhu, G., Wang, S., Huang, Z., Zhang, S., Liao, Q., Zhang, C., Lin, T., Qin, M., Peng, M., Yang, C., Cao, X., Han, X., Wang, X., van der Knaap, E., Zhang, Z., Cui, X., Klee, H., Fernie, A. R., Luo, J., & Huang, S. (2018). Rewiring of the fruit metabolome in tomato breeding. Cell, 172, 249–261.e212.PubMedGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Max Planck Institute of Molecular Plant PhysiologyPotsdam-GolmGermany
  2. 2.Center of Plant System Biology and BiotechnologyPlovdivBulgaria
  3. 3.Instituto de BiotecnologíaInstituto Nacional de Tecnología Agropecuaria (IB-INTA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)CastelarArgentina
  4. 4.Facultad de AgronomíaUniversidad de Buenos AiresBuenos AiresArgentina
  5. 5.Departamento de Botânica, Instituto de BiociênciasUniversidade de São PauloSão PauloBrazil
  6. 6.Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET)Ciudad UniversitariaBuenos AiresArgentina

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