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Plant Metabolomics: Sustainable Approach Towards Crop Productivity

  • Javid Ahmad Parray
  • Mohammad Yaseen Mir
  • Nowsheen Shameem
Chapter

Abstract

Metabolomics signifies a rapidly growing and interdisciplinary field of science by combination of biochemistry, analytical chemistry, bioinformatics, medicine etc. Metabolomics allows achieving a sophisticated level of information about biological systems and holding great promise for development of novel diagnostic tests and therapies including personalized medicine. Notwithstanding its powerful analytical and computational systems integration still there remains many challenges pertaining to metabolic and analytical challenges. Metabolomics combined with other technologies permits us to resolve key issues of agronomic performance that remained unsettled hitherto. Metabolomics is also developing into a valuable tool that can be used to monitor and assess gene function, and to characterize post-genomic processes from a broad perspective Many efforts can be focused to crop plants that have detailed info on performance in varied environments These challenges are largely caused by the high degree of chemical diversity among metabolite pools as well as the complexity of spatial and temporal distribution within living tissues. In this chapter role of metabolomics for improving various agricultural crops including GMO varieties are discussed in detail besides various networking approaches as well. The role of plant bioactive substances for stimulating the soil microbial communities is also elaborated in concluding section.

Keywords

Metabolome Genomic approach Priming food crops 

References

  1. Abandani, R. R., & Ramezani, M. (2012). Thephysiological effects on some traits of osmopriming germination of maize (Zea mays L.), rice (Oryzasativa L.) and cucumber (Cucumissativus L). International Journal of Agronomy, 413–148.Google Scholar
  2. Afendi, F. M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K., et al. (2012). KNApSAcK family databases: Integrated metabolite–plant species databases for multifaceted plant research. Plant & Cell Physiology, 53, e1.CrossRefGoogle Scholar
  3. Aharoni, A., Giri, A. P., Verstappen, F. W. A., Bertea, C. M., Sevenier, R., Sun, Z., et al. (2004). Gain and loss of fruit flavor compounds produced by wild and cultivated strawberry species. Plant Cell, 16, 3110–3131.PubMedPubMedCentralCrossRefGoogle Scholar
  4. Ainalidou, A., Tanou, G., Belghazi, M., Samiotaki, M., Diamantidis, G., Molassiotis, A., et al. (2015). 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.  https://doi.org/10.1016/j.jprot.2016.02.013.CrossRefGoogle Scholar
  5. Beard, D. A., et al. (2002). Energy balance for analysis of complex metabolic networks. Biophysical Journal, 83, 79–86.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Breitling, R., Li, Y., Tesson, B. M., Fu, J., Wu, C., Wiltshire, T., Gerrits, A., Bystrykh, L. V., de Haan, G., Su, A. I., & Jansen, R. C. (2008). Genetical genomics: Spotlight on QTL hotspots. PLoS Genetics, 4, e1000232.  https://doi.org/10.1371/journal.pgen.1000232.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Burgos, A., Szymanski, J., Seiwert, B., Degenkolbe, T., Hannah, M. A., Giavalisco, P., & Willmitzer, L. (2011). Analysis of short-term changes in the Arabidopsis thaliana glycerolipidome in response to temperature and light. The Plant Journal, 66, 656–668.PubMedCrossRefPubMedCentralGoogle Scholar
  8. Caasi-Lit, M. T., Tanner, G. J., Nayudu, M., & Whitecross, M. I. (2007). Isovitexin–2’-O-beta [6-O-E-p-coumaroylglucopyranoside] from UV-B irradiated leaves of rice, Oryza sativa L. inhibits fertility of Helicoverpa armigera. Photochemistry and Photobiology, 83, 1167–1173.PubMedCrossRefPubMedCentralGoogle Scholar
  9. Cebulj, A., Cunja, V., Mikulic-Petkovsek, M., & Veberic, R. (2017). Importance of metabolite distribution in apple fruit. Scientia Horticulturae, 214, 214–220.  https://doi.org/10.1016/j.scienta.2016.11.048.CrossRefGoogle Scholar
  10. Chen, W., Wang, W., Peng, M., Gong, L., Gao, Y., Wan, J., et al. (2016a). Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nature Communications, 7, 12767.  https://doi.org/10.1038/ncomms12767.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chen, Y., Xu, J., Zhang, R., & Abliz, Z. (2016b). Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS. Bioanalysis, 8, 981–997.  https://doi.org/10.4155/bio-2015-0010.CrossRefPubMedGoogle Scholar
  12. Chiu, K. Y., & Sung, J. M. (2002). Effect of priming temperature on storability of primed sh-2sweet corn seed. Crop Science, 42, 1996–2003.CrossRefGoogle Scholar
  13. Clark, L. J., Whalley, W. R., & Barraclough, P. B. (2001). How do roots penetrate strong soil? In Roots: The dynamic interface between plants and the earth: The 6th symposium of the international society of root research, Vol. 255, no. 1, 11–15 November 2001.Google Scholar
  14. Covert, M. W., et al. (2004). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429, 92–96.PubMedCrossRefGoogle Scholar
  15. Cuadros-Inostroza, A., Ruíz-Lara, S., González, E., Eckardt, A., Willmitzer, L., & Peña-Cortés, H. (2016). GC–MS metabolic profiling of cabernet sauvignon and merlot cultivars during grapevine berry development and network analysis reveals a stage- and cultivar-dependent connectivity of primary metabolites. Metabolomics, 12, 39.  https://doi.org/10.1007/s11306-015-0927-z.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Cuthbertson, D., Andrews, P. K., Reganold, J. P., Davies, N. M., & Lange, B. M. (2012). Utility of metabolomics toward assessing the metabolic basis of quality traits in apple fruit with an emphasis on antioxidants. Journal of Agricultural and Food Chemistry, 60, 8552–8560.  https://doi.org/10.1021/jf3031088.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Daygon, V., & Fitzgerald, M. (2013). Application of metabolomics for providing a new generation of selection tools for crop improvement. In Hot topics in metabolomics: Food and nutrition (Future Science Book series) (pp. 6–16). London: Future Science Ltd.  https://doi.org/10.4155/ebo.13.458.CrossRefGoogle Scholar
  18. Feist, A. M., Herrgård, M. J., Thiele, I., Reed, J. L., & Palsson, B. Ø. (2009). Reconstruction of biochemical networks in microorganisms. Nature Reviews Microbiology, 7(2), 129–143.  https://doi.org/10.1038/nrmicro1949. Epub 2008 Dec 31.CrossRefPubMedGoogle Scholar
  19. Fernandez, C., Monnier, Y., Santonja, M., Gallet, C., Weston, L. A., Prévosto, B., Saunier, A., Baldy, V., & Bousquet-Mélou, A. (2016a). The impact of competition and allelopathy on the trade-off between plant defense and growth in two contrasting tree species. Frontiers in Plant Sciences, 7, 594.  https://doi.org/10.3389/fpls.2016.00594.CrossRefGoogle Scholar
  20. Fernandez, O., Urrutia, M., Bernillon, S., Giauffret, C., Tardieu, F., Le Gouis, J., Langlade, N., Charcosset, A., Moing, A., & Gibon, Y. (2016b). Fortune telling: Metabolic markers of plant performance. Metabolomics, 12, 158.  https://doi.org/10.1007/s11306-016-1099-1.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Fernie, A. R., & Schauer, N. (2009). Metabolomics-assisted breeding: A viable option for crop improvement? Trends in Genetics, 25(1), 39–48.PubMedCrossRefGoogle Scholar
  22. Francke, C., Siezen, R. J., & Teusink, B. (2005 Nov). Reconstructing the metabolic network of a bacterium from its genome. Trends in Microbiology, 13(11), 550–558.PubMedCrossRefGoogle Scholar
  23. Hagel, J. M., Mandal, R., Han, B., Han, J., Dinsmore, D. R., Borchers, C. H., Wishart, D. S., & Facchini, P. J. (2015). Metabolome analysis of 20 taxonomically related benzylisoquinoline alkaloid-producing plants. BMC Plant Biology, 15, 220.  https://doi.org/10.1186/s12870-015-0594-2.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Hall, T., Healey, M., & Harrison, M. (2002). Fieldwork and disabled students: Discourses of exclusion and inclusion. Transactions of the Institute of British Geographers, 27(2), 213–231.CrossRefGoogle Scholar
  25. Harris, D., Joshi, A., Khan, P. A., Gothakar, P., & Sodhi, P. S. (1999). On-farm seed priming in semi-arid agriculture: Development and evaluation in corn, rice and chickpea in India using participatory methods. Experimental Agriculture, 35, 15–29.CrossRefGoogle Scholar
  26. Harris, D., Raghuwanshi, B. S., Gangwar, J. S., Singh, S. C., Joshi, K., Rashid, A., & Hollington, P. A. (2001). Evaluation by farmers of on-farm seed priming in wheat in India, Nepal and Pakistan. Experimental Agriculture, 37, 403–415.CrossRefGoogle Scholar
  27. Harris, D., Rashid, A., Arif, M., & Yunas, M. (2005). Alleviating micronutrient deficiencies in alkaline soils of the North-West Frontier Province of Pakistan: On-farm seed priming with zinc in wheat and chickpea. In T. P. Andersen, J. K. Karki, & K. B. S. L. Maskey (Eds.), Micronutrients in South and South East Asia (pp. 143–151). Kathmandu: ICIMOD.Google Scholar
  28. Harris, D., Rashid, A., Miraj, G., Arif, M., & Shah, H. (2007). ‘On-farm’ seed priming with zinc sulphate solution-A cost-effective way to increase the maize yields of resource poor farmers. Field Crops Res., 102, 119–127.CrossRefGoogle Scholar
  29. Hatoum, D., Hertog, M. L. A. T. M., Geeraerd, A. H., & Nicolai, B. M. (2016). Effect of browning related pre- and post-harvest factors on the “Braeburn” apple metabolome during CA storage. Postharvest Biology and Technology, 111, 106–116.CrossRefGoogle Scholar
  30. Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., & Stevens, R. L. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature Biotechnology, 28(9), 977–982.  https://doi.org/10.1038/nbt.1672. Epub 2010 Aug 29.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hols, P., et al. (2005). New insights in the molecular biology and physiology of Streptococcus thermophilus revealed by comparative genomics. FEMS Microbiology Reviews, 29, 435–463.PubMedPubMedCentralGoogle Scholar
  32. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., the rest of the SBML Forum, Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., Cuellar, A. A., Dronov, S., Gilles, E. D., Ginkel, M., Gor, V., Goryanin, I. I., Hedley, W. J., Hodgman, T. C., Hofmeyr, J.-H., Hunter, P. J., Juty, N. S., Kasberger, J. L., Kremling, A., Kummer, U., Le Novere, N., Loew, L. M., Lucio, D., Mendes, P., Minch, E., Mjolsness, E. D., Nakayama, Y., Nelson, M. R., Nielsen, P. F., Sakurada, T., Schaff, J. C., Shapiro, B. E., Shimizu, T. S., Spence, H. D., Stelling, J., Takahashi, K., Tomita, M., Wagner, J., & Wang, J. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524–531.PubMedCrossRefPubMedCentralGoogle Scholar
  33. Imai, T., Tanabe, K., Kato, T., & Fukushima, K. (2015). Localization of ferruginol, a diterpene phenol, in Cryptomeria japonica heartwood by time-of-flight secondary ion mass spectrometry. Planta, 221, 549–556.  https://doi.org/10.1007/s00425-004-1476-2.CrossRefGoogle Scholar
  34. Jorge, T. F., Mata, A. T., & António, C. (2016). Mass spectrometry as a quantitative tool in plant metabolomics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374, 20150370.  https://doi.org/10.1098/rsta.2015.0370.CrossRefGoogle Scholar
  35. Kage, U., Karre, S., Kushalappa, A. C., & Mccartney, C. (2016). Identification and characterization of a fusarium head blight resistance gene TaACT in wheat QTL-2DL. Plant Biotechnology Journal, 15, 447–457.  https://doi.org/10.1111/pbi.12641.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Keseler, I. M., Collado-Vides, J., Santos-Zavaleta, A., et al. (2011). EcoCyc: A comprehensive database of Escherichia coli biology. Nucleic Acids Research, 39, D583–D590.PubMedCrossRefPubMedCentralGoogle Scholar
  37. Kind, T., Wohlgemuth, G., Lee, d. Y., Lu, Y., Palazoglu, M., Shahbaz, S., & Fiehn, O. (2009). Fiehn Lib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-light gas chromatography/mass spectrometry. Analytical Chemistry, 81, 10038–10048. [CrossRef] [PubMed] 35.PubMedPubMedCentralCrossRefGoogle Scholar
  38. Komatsu, S., Yamamoto, A., Nakamura, T., Nouri, M. Z., Nanjo, Y., Nishizawa, K., et al. (2011). Comprehensive analysis of mitochondria in roots and hypocotyls of soybean under flooding stress using proteomics and metabolomics techniques. Journal of Proteome Research, 10, 3993–4004.  https://doi.org/10.1021/pr2001918.CrossRefPubMedGoogle Scholar
  39. Kopka, J., Schauer, N., Krueger, S., Birkemeyer, C., Usadel, B., Bergmüller, E., Dörmann, P., Weckwerth, W., Gibon, Y., Stitt, M., et al. (2005). GMD@CSB.DB: The Golm metabolome database. Bioinformatics, 21, 1635–1638.PubMedCrossRefGoogle Scholar
  40. Kueger, S., Steinhauser, D., Willmitzer, L., & Giavalisco, P. (2012). High-resolution plant metabolomics: From mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions. The Plant Journal, 70, 39–50.  https://doi.org/10.1111/j.1365-313X.2012.04902.x.CrossRefPubMedGoogle Scholar
  41. Kuepfer, L., et al. (2005). Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Research, 15, 1421–1430.PubMedPubMedCentralCrossRefGoogle Scholar
  42. Kumari, S., Stevens, D., Kind, T., Denkert, C., & Fiehn, O. (2011). Applying in silico retention index and mass spectra matching for identification of unknown metabolites in accurate mass GC-TOF mass spectrometry. Analytical Chemistry, 83, 5895–5902.PubMedPubMedCentralCrossRefGoogle Scholar
  43. Lei, M., Zhu, C., Liu, Y., Karthikeyan, A. S., Bressan, R. A., Raghothama, K. G., & Liu, D. (2011). Ethylene signalling is involved in regulation of phosphate starvation-induced gene expression and production of acid phosphatases and anthocyanin in Arabidopsis. The New Phytologist, 189, 1084–1095.PubMedCrossRefPubMedCentralGoogle Scholar
  44. Lesellier, E., & West, C. (2015). The many faces of packed column supercritical fluid chromatography – A critical review. Journal of Chromatography A, 1382, 2–46.  https://doi.org/10.1016/j.chroma.2014.12.083.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Marti, G., Erb, M., Boccard, J., Glauser, G., Doyen, G. R., Villard, N., et al. (2013). Metabolomics reveals herbivore-induced metabolites of resistance and susceptibility in maize leaves and roots. Plant, Cell & Environment, 36, 621–639.CrossRefGoogle Scholar
  46. Matsuda, N., Sato, S., Shiba, K., Okatsu, K., Saisho, K., Gautier, C. A., Sou, Y. S., Saiki, S., Kawajiri, S., Sato, F., Kimura, M., Komatsu, M., Hattori, N., & Tanaka, K. (2010). PINK1 stabilized by mitochondrial depolarization recruits Parkin to damaged mitochondria and activates latent Parkin for mitophagy. The Journal of Cell Biology, 189(2), 211–221.PubMedPubMedCentralCrossRefGoogle Scholar
  47. Matsuda, F., Okazaki, Y., Oikawa, A., Kusano, M., Nakabayashi, R., Kikuchi, J., et al. (2012). Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. The Plant Journal, 70, 624–636. PubMed.  https://doi.org/10.1111/j.1365-313X.2012.04903.x.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Mazzucotelli, E., Tartari, A., Cattivelli, L., & Forlani, G. (2006). Metabolism of γ-aminobutyric acid during cold acclimation and freezing and its relationship to frost tolerance in barley and wheat. Journal of Experimental Botany, 57, 3755–3766.  https://doi.org/10.1093/jxb/erl141.CrossRefPubMedGoogle Scholar
  49. Moco, S., Forshed, J., De Vos, R. C. H., Bino, R. J., & Vervoort, J. (2008). Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance. Metabolomics, 4, 202–215.  https://doi.org/10.1007/s11306-008-0112-8.CrossRefGoogle Scholar
  50. Mohammadi MH, Khataar M, Shekari F (2017) Effect of soil salinity on the wheat and bean root.Google Scholar
  51. Murungu, F. S., & Madanzi, T. (2004). Seed priming, genotype and sowing date effects on emergence, growth and yield of wheat in a tropical low altitude area of Zimbabwe. African Journal of Agricultural Research, 5(17), 2341–2349.Google Scholar
  52. Muscolo, A., Junker, A., Klukas, C., Weigelt-Fischer, K., Riewe, D., & Altmann, T. (2015). Phenotypic and metabolic responses to drought and salinity of four contrasting lentil accessions. Journal of Experimental Botany, 66, 5467–5480.  https://doi.org/10.1093/jxb/erv208. [PMC free article] [PubMed] [Cross Ref].CrossRefPubMedPubMedCentralGoogle Scholar
  53. Nagana Gowda, G. A., & Daniel, R. (2015). Can NMR solve some significant challenges in metabolomics. Journal of Magnetic Resonance, 260, 144–160.PubMedCrossRefGoogle Scholar
  54. Nagpala, E. G., Guidarelli, M., Gasperotti, M., Masuero, D., Bertolini, P., Vrhovsek, U., et al. (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, 1869–1878.  https://doi.org/10.1021/acs.jafc.5b06005. [PubMed][Cross Ref].CrossRefPubMedGoogle Scholar
  55. Notebaart, R. A. (2006). Accelerating the reconstruction of genome scale metabolic networks. BMC Bioinformatics, 7, 296.PubMedPubMedCentralCrossRefGoogle Scholar
  56. Obata, T., & Fernie, A. R. (2012). The use of metabolomics to dissect plant responses to abiotic stresses. Cellular and Molecular Life Sciences CMLS, 69(19), 3225–3243.  https://doi.org/10.1007/s00018-012-1091-5.CrossRefPubMedPubMedCentralGoogle Scholar
  57. Obata, T., Witt, S., Lisec, J., Palacios-Rojas, N., Florez-Sarasa, I., Yousfi, S., et al. (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.  https://doi.org/10.1104/pp.15.01164.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Oberhardt, M. A., et al. (2009). Applications of genome-scale metabolic reconstructions. Molecular Systems Biology, 5, 320.PubMedPubMedCentralCrossRefGoogle Scholar
  59. Ogbaga, C. C., Stepien, P., Dyson, B. C., Rattray, N. J., Ellis, D. I., Goodacre, R., et al. (2016). Biochemical analyses of sorghum varieties reveal differential responses to drought. PLoS One, 11, e0154423.  https://doi.org/10.1371/journal.pone.0154423.CrossRefPubMedPubMedCentralGoogle Scholar
  60. Oikawa, K., Yamasato, A., Kong, S.-G., Kasahara, M., Nakai, M., Takahashi, F., Ogura, Y., Kagawa, T., & Wada, M. (2008). Chloroplast outer envelope protein CHUP1 is essential for chloroplast Anchorage to the plasma membrane and chloroplast movement. Plant Physiology, 148(2), 829–842.PubMedPubMedCentralCrossRefGoogle Scholar
  61. Oikawa, A., Otsuka, T., Nakabayashi, R., Jikumaru, Y., Isuzugawa, K., Murayama, H., et al. (2015). Metabolic profiling of developing pear fruits reveals dynamic variation in primary and secondary metabolites, including plant hormones. PLoS One, 10, e0131408.PubMedPubMedCentralCrossRefGoogle Scholar
  62. Okazaki, Y., & Saito, K. (2016). Integrated metabolomics and phytochemical genomics approaches for studies on rice. Gigascience, 5, 11.  https://doi.org/10.1186/s13742-016-0116-7.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Oliveira, A. P., Nielsen, J., & Forster, J. (2005). Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiology, 5, 39.PubMedPubMedCentralCrossRefGoogle Scholar
  64. Osorio, S., Alba, R., Damasceno, C. M., Lopez-Casado, G., Lohse, M., Zanor, M. I., et al. (2011). Systems biology of tomato fruit development: Combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutants reveals novel regulatory interactions. Plant Physiology, 157, 405–425.  https://doi.org/10.1104/pp.111.175463.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Pan, Z., Li, Y., Deng, X., & Xiao, S. (2014). Non-targeted metabolomic analysis of orange (Citrus sinensis [L.] Osbeck) wild type and bud mutant fruits by direct analysis in real-time and HPLC-electrospray mass spectrometry. Metabolomics, 10, 508–523.  https://doi.org/10.1007/s11306-013-0597-7.CrossRefGoogle Scholar
  66. Pandey, M. K., Roorkiwal, M., Singh, V. K., Ramalingam, A., Kudapa, H., Thudi, M., et al. (2016). Emerging genomic tools for legume breeding: Current status and future perspectives. Frontiers in Plant Science, 7, 455.  https://doi.org/10.3389/fpls.2016.00455. [PMC free article] [PubMed] [Cross Ref].CrossRefPubMedPubMedCentralGoogle Scholar
  67. Parera, C. A., & Cantliffe, D. J. (1994). Pre-sowing seed priming. Horticultural Reviews, 16, 109–141.Google Scholar
  68. Perez-Fons, L., Wells, T., Corol, D. I., Ward, J. L., Gerrish, C., Beale, M. H., et al. (2014). A genome-wide metabolomic resource for tomato fruit from Solanum pennellii. Scientific Reports, 4, 3859.  https://doi.org/10.1038/srep03859.CrossRefPubMedPubMedCentralGoogle Scholar
  69. Pinchuk, G. E., et al. (2010). Constraint-based model of Shewanella oneidensis MR-1 metabolism: A tool for data analysis and hypothesis generation. PLoS Computational Biology, 6, e1000822.PubMedPubMedCentralCrossRefGoogle Scholar
  70. Pinheiro, C., Passarinho, J. A., & Ricardo, C. P. (2004). Effect of drought and rewatering on the metabolism of Lupinus albusorgans. Journal of Plant Physiology, 161, 1203–1210.PubMedCrossRefPubMedCentralGoogle Scholar
  71. Sanchez, D. H., Pieckenstain, F. L., Escaray, F., Erban, A., Kraemer, U., Udvardi, M. K., et al. (2011). Comparative ionomics and metabolomics in extremophile and glycophytic Lotus species under salt stress challenge the metabolic pre-adaptation hypothesis. Plant, Cell & Environment, 34, 605–617.CrossRefGoogle Scholar
  72. Shao, Y., Zhu, B., Zheng, R., Zhao, X., Yin, P., Lu, X., Jiao, B., Xu, G., & Yao, Z. (2015). Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery. Journal of Proteome Research, 14, 906–916.PubMedCrossRefPubMedCentralGoogle Scholar
  73. 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.  https://doi.org/10.1093/jxb/erw059.CrossRefPubMedPubMedCentralGoogle Scholar
  74. Simó, M., Guerrero, J. C., Giuliani, L., Castellano, I., & Acosta, L. E. (2014a). A predictive modeling approach to test distributional uniformity of Uruguayan harvestmen (Arachnida: Opiliones). Zoological Studies, 53, 50.CrossRefGoogle Scholar
  75. Simó, C., Ibáñez, C., Valdés, A., Cifuentes, A., & García-Cañas, V. (2014b). Metabolomics of genetically modified crops. International Journal of Molecular Sciences, 15, 18941–18966.  https://doi.org/10.3390/ijms151018941.CrossRefPubMedPubMedCentralGoogle Scholar
  76. Slisz, A. M., Breksa, A. P., Mishchuk, D. O., McCollum, G., & Slupsky, C. M. (2012). Metabolomic analysis of citrus infection by Candidatus Liberibacter reveals insight into pathogenicity. Journal of proteome research, 11(8), 4223–4230.PubMedCrossRefPubMedCentralGoogle Scholar
  77. Subedi, K. D., & Ma, B. L. (2005). Seed priming does not improve corn yield in a humid temperate environment. Agronomy Journal, 97, 211–217.Google Scholar
  78. Sun, P., Mantri, N., Lou, H., Hu, Y., Sun, D., Zhu, Y., et al. (2012). Effects of elevated CO2 and temperature on yield and fruit quality of strawberry (Fragaria x ananassa Duch.) at two levels of nitrogen application. PLoS One, 7, e41000.  https://doi.org/10.1371/journal.pone.0041000.CrossRefPubMedPubMedCentralGoogle Scholar
  79. Suravajhala, P., Kogelman, L. J. A., & Kadarmideen, H. N. (2016). Multi-omic data integration and analysis using systems genomics approaches: Methods and applications in animal production, health and welfare. Genetics, Selection, Evolution, 48, 38.PubMedCrossRefGoogle Scholar
  80. Taylor, A. G., Allen, P. S., Bennett, M. A., Bradford, K. J., Burrisand, J. S., & Misra, M. K. (1998). Seed enhancements. Seed Science Research, 8, 245–256.CrossRefGoogle Scholar
  81. Taymaz-Nikerel, H., et al. (2010). Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnology and Bioengineering, 107, 369–381.PubMedCrossRefGoogle Scholar
  82. Tempest, D. W., & Neijssel, O. M. (1984). The status of Y ATP and Maintenance Energy As Biologically Interpretable phenomenon. Annual Review of Microbiology, 38, 459–486.PubMedCrossRefGoogle Scholar
  83. Teusink, B., et al. (2005). In silico reconstruction of the metabolic pathways of Lactobacillus plantarum: Comparing predictions of nutrient requirements with those from growth experiments. Applied and Environmental Microbiology, 71, 7253–7262.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Teusink, B., et al. (2006). Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model. The Journal of Biological Chemistry, 281, 40041–40048.PubMedCrossRefGoogle Scholar
  85. Thiele, I., & Palsson, B. O. (2010). A protocol generating high quality genome scale metabolic reconstruction. Nature Protocols, 5, 53–121.CrossRefGoogle Scholar
  86. Tian, H., Bai, J., An, Z., Chen, Y., Zhang, R., He, J., Bi, X., Song, Y., & Abliz, Z. (2013). Plasma metabolome analysis by integrated ionization rapid-resolution liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 27, 2071–2080.PubMedCrossRefPubMedCentralGoogle Scholar
  87. Tohge, T., & Fernie, A. R. (2009). Web-based resources for mass-spectrometry-based metabolomics: A user’s guide. Phytochemistry, 70, 450–456.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 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.  https://doi.org/10.1093/pcp/pcv093. [PubMed][Cross Ref].CrossRefGoogle Scholar
  89. Tripathi, P., Rabara, R. C., Reese, R. N., Miller, M. A., Rohila, J. S., Subramanian, S., et al. (2016). A toolbox of genes, proteins, metabolites and promoters for improving drought tolerance in soybean includes the metabolite coumestrol and stomatal development genes. BMC Genomics, 17, 102.  https://doi.org/10.1186/s12864–016–2420-0.CrossRefPubMedPubMedCentralGoogle Scholar
  90. Tsugawa, H., Bamba, T., Shinohara, M., Nishiumi, S., Yoshida, M., & Fukusaki, E. (2011). Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis. Journal of Bioscience and Bioengineering, 112, 292–298.PubMedCrossRefGoogle Scholar
  91. Ussher, J. R., Elmariah, S., Gerszten, R. E., & Dyck, J. R. B. (2016). The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. Journal of the American College of Cardiology, 68(25), 2850–2870.Google Scholar
  92. van Gulik, W. M., & Heijnen, J. J. (1995). A metabolic network stoichiometry analysis of microbial growth and product formation. Biotechnology and Bioengineering, 48(6), 681–698.PubMedCrossRefGoogle Scholar
  93. Vanrolleghem, P. A., & Heijnen, J. J. (1998). A structured approach for selection among candidate metabolic network models and estimation of unknown stiometric coefficients. Biotechnology and Bioengineering, 58, 133–138.PubMedCrossRefGoogle Scholar
  94. Vanrolleghem, P. A., et al. (1996). Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies. Biotechnology Progress, 12, 434–448.PubMedCrossRefGoogle Scholar
  95. Venkatesh, T. V., Chassy, A. W., Fiehn, O., Flint-Garcia, S., Zeng, Q., Skogerson, K., et al. (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.  https://doi.org/10.1021/acs.jafc.5b04901.CrossRefPubMedGoogle Scholar
  96. Wang, J., Sun, L., Xie, L., He, Y., Luo, T., Sheng, L., et al. (2016). Regulation of cuticle formation during fruit development and ripening in “Newhall” navel orange (Citrus sinensis Osbeck) revealed by transcriptomic and metabolomic profiling. Plant Science, 243, 131–144.  https://doi.org/10.1016/j.plantsci.2015.12.010.CrossRefPubMedGoogle Scholar
  97. Ward, C., & Courtney, D. (2013). Kiwifruit. Taking its place in the global fruit bowl. Advances in Food and Nutrition Research, 68, 1–14.  https://doi.org/10.1016/B978-0-12-394294-4.00001-8.CrossRefPubMedGoogle Scholar
  98. Zhang, J., Wang, X., Yu, O., Tang, J., Gu, X., Wan, X., et al. (2011). Metabolic profiling of strawberry (Fragaria x ananassa Duch.) during fruit development and maturation. Journal of Experimental Botany, 62, 1103–1118.PubMedCrossRefGoogle Scholar
  99. 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.  https://doi.org/10.1111/nph.14011.CrossRefPubMedGoogle Scholar
  100. Zivy, M., Wienkoop, S., Renaut, J., Pinheiro, C., Goulas, E., & Carpentier, S. (2015). The quest for tolerant varieties: The importance of integrating “omics” techniques to phenotyping. Frontiers in Plant Science, 6, 448.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Javid Ahmad Parray
    • 1
  • Mohammad Yaseen Mir
    • 2
  • Nowsheen Shameem
    • 3
  1. 1.Department of Environmental ScienceGovernment SAM Degree CollegeBudgamIndia
  2. 2.Centre of Research for DevelopmentUniversity of KashmirSrinagarIndia
  3. 3.Department of Environmental ScienceCluster UniversitySrinagarIndia

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