Crop improvement using life cycle datasets acquired under field conditions

Frontiers in Plant Science

Volumn 6, Issue September, 2015, Pages

  Mochida, Keiichi ^a,b   Saisho, Daisuke ^c   Hirayama, Takashi ^c  

^a Elements Chemistry Laboratory   (Japan)

^b YOKOHAMA CITY UNIVERSITY   (Japan)

^c OKAYAMA UNIVERSITY   (Japan)

Author keywords

Crop phenology; Epigenome; Machine learning; Population genomics; Transcriptome

Indexed keywords

EID: 84942848247     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2015.00740     Document Type: Review

References (79)

1. Adamski, J., and Suhre, K. (2013). Metabolomics platforms for genome wide association studies—linking the genome to the metabolome. Curr. Opin. Biotechnol. 24, 39-47. doi: 10.1016/j.copbio.2012.10.003
2. Aikawa, S., Kobayashi, M. J., Satake, A., Shimizu, K. K., and Kudoh, H. (2010). Robust control of the seasonal expression of the Arabidopsis FLC gene in a fluctuating environment. Proc. Natl. Acad. Sci. U.S.A. 107, 11632-11637. doi: 10.1073/pnas.0914293107
3. Arai-Kichise, Y., Shiwa, Y., Nagasaki, H., Ebana, K., Yoshikawa, H., Yano, M., et al. (2011). Discovery of genome-wide DNA polymorphisms in a landrace cultivar of Japonica rice by whole-genome sequencing. Plant Cell Physiol. 52, 274-282. doi: 10.1093/pcp/pcr003
4. Balmer, D., Flors, V., Glauser, G., and Mauch-Mani, B. (2013). Metabolomics of cereals under biotic stress: current knowledge and techniques. Front. Plant Sci. 4:82. doi: 10.3389/fpls.2013.00082
5. Busemeyer, L., Mentrup, D., Moller, K., Wunder, E., Alheit, K., Hahn, V, et al. (2013). BreedVision—a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors (Basel) 13, 2830-2847. doi: 10.3390/s130302830
6. Causse, M., Desplat, N., Pascual, L., Le Paslier, M. C., Sauvage, C., Bauchet, G., et al. (2013). Whole genome resequencing in tomato reveals variation associated with introgression and breeding events. BMC Genomics 14:791. doi: 10.1186/14712164-14-791
7. Chinnusamy, V., and Zhu, J. K. (2009). Epigenetic regulation of stress responses in plants. Curr. Opin. Plant Biol. 12, 133-139. doi: 10.1016/j.pbi.2008.12.006
8. Dal Santo, S., Tornielli, G. B., Zenoni, S., Fasoli, M., Farina, L., Anesi, A., et al. (2013). The plasticity of the grapevine berry transcriptome. Genome Biol. 14, r54. doi: 10.1186/gb-2013-14-6-r54
9. Deshmukh, R., Sonah, H., Patil, G., Chen, W., Prince, S., Mutava, R., et al. (2014). Integrating omic approaches for abiotic stress tolerance in soybean. Front. Plant Sci. 5:244. doi: 10.3389/fpls.2014.00244
10. De Vos, D., Dzhurakhalov, A., Draelants, D., Bogaerts, I., Kalve, S., Prinsen, E., et al. (2012). Towards mechanistic models of plant organ growth. J. Exp. Bot. 63, 3325-3337. doi: 10.1093/jxb/ers037
11. Dinakar, C., and Bartels, D. (2013). Desiccation tolerance in resurrection plants: new insights from transcriptome, proteome and metabolome analysis. Front. Plant Sci. 4:482. doi: 10.3389/fpls.2013.00482
12. Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., et al. (2011). A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379. doi: 10.1371/journal.pone.0019379
13. Evans, L. M., Slavov, G. T., Rodgers-Melnick, E., Martin, J., Ranjan, P., Muchero, W., et al. (2014). Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations. Nat. Genet. 46, 1089-1096. doi: 10.1038/ng.3075
14. Fahlgren, N., Gehan, M. A., and Baxter, I. (2015). Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 24, 93-99. doi: 10.1016/j.pbi.2015.02.006
15. Fujii, M., Yokosho, K., Yamaji, N., Saisho, D., Yamane, M., Takahashi, H., et al. (2012). Acquisition of aluminium tolerance by modification of a single gene in barley. Nat. Commun. 3,713. doi: 10.1038/ncomms1726
16. Fukushima, A., and Kusano, M. (2013). Recent progress in the development of metabolome databases for plant systems biology. Front. Plant Sci. 4:73. doi: 10.3389/fpls.2013.00073
17. Gibson, G. (2008). The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9, 575-581. doi: 10.1038/nrg2383
18. Grosskinsky, D. K., Svensgaard, J., Christensen, S., and Roitsch, T. (2015). Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J. Exp. Bot. 66, 5429-5440. doi: 10.1093/jxb/erv345
19. Hirayama, T., and Shinozaki, K. (2010). Research on plant abiotic stress responses in the post-genome era: past, present and future. Plant J. 61, 1041-1052. doi: 10.1111/j.1365-313X.2010.04124.x
20. Huang, X., and Han, B. (2014). Natural variations and genome-wide association studies in crop plants. Annu. Rev. Plant Biol. 65, 531-551. doi: 10.1146/annurev-arplant-050213-035715
21. Huang, X., Kurata, N., Wei, X., Wang, Z. X., Wang, A., Zhao, Q., et al. (2012). A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497-501. doi: 10.1038/nature11532
22. Ikeda, Y. (2012). Plant imprinted genes identified by genome-wide approaches and their regulatory mechanisms. Plant Cell Physiol. 53, 809-816. doi: 10.1093/pcp/pcs049
23. Jordan, M. I., and Mitchell, T. M. (2015). Machine learning: trends, perspectives, and prospects. Science 349, 255-260. doi: 10.1126/science.aaa8415
24. Kalisz, S., and Purugganan, M. D. (2004). Epialleles via DNA methylation: consequences for plant evolution. Trends Ecol. Evol. 19, 309-314. doi: 10.1016/j.tree.2004.03.034
25. Kanno, Y., Jikumaru, Y., Hanada, A., Nambara, E., Abrams, S. R., Kamiya, Y., et al. (2010). Comprehensive hormone profiling in developing Arabidopsis seeds: examination of the site of ABA biosynthesis, ABA transport and hormone interactions. PlantCellPhysiol. 51, 1988-2001. doi: 10.1093/pcp/pcq158
26. Katsuragi, T., Ono, N., Yasumoto, K., Altaf-Ul-Amin, M., Hirai, M. Y, Sriyudthsak, K., et al. (2013). SS-mPMG and SS-GA: tools for finding pathways and dynamicsimulationofmetabolicnetworks. PlantCellPhysiol. 54, 728-739. doi: 10.1093/pcp/pct052
27. Kim, J. M., To, T. K., and Seki, M. (2012). An epigenetic integrator: new insights into genome regulation, environmental stress responses and developmental controls by histone deacetylase 6. Plant Cell Physiol. 53, 794-800. doi: 10.1093/pcp/pcs004
28. Kinoshita, T., and Seki, M. (2014). Epigenetic memory for stress response and adaptationinplants. PlantCellPhysiol. 55,1859-1863. doi: 10.1093/pcp/pcu125
29. Kjaer, K. H., and Ottosen, C. O. (2015). 3D Laser Triangulation for Plant Phenotyping in Challenging Environments. Sensors (Basel) 15, 13533-13547. doi: 10.3390/s150613533
30. Klukas, C., Chen, D., and Pape, J. M. (2014). Integrated analysis platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165, 506-518. doi: 10.1104/pp.113.233932
31. Kojima, M., Kamada-Nobusada, T., Komatsu, H., Takei, K., Kuroha, T., Mizutani, M., et al. (2009). Highly sensitive and high-throughput analysis of plant hormones using MS-probe modification and liquid chromatography-tandem mass spectrometry: an application for hormone profiling in Oryza sativa. Plant CellPhysiol. 50, 1201-1214. doi: 10.1093/pcp/pcp057
32. Kuromori, T., Takahashi, S., Kondou, Y., Shinozaki, K., and Matsui, M. (2009). Phenome analysis in plant species using loss-of-function and gain-of-function mutants. Plant Cell Physiol. 50, 1215-1231. doi: 10.1093/pcp/pcp078
33. Kuromori, T., Wada, T., Kamiya, A., Yuguchi, M., Yokouchi, T., Imura, Y., et al. (2006). A trial of phenome analysis using 4000 Ds-insertional mutants in gene-coding regions of Arabidopsis. Plant J. 47, 640-651. doi: 10.1111/j.1365-313X.2006.02808.x
34. Lai, J., Li, R., Xu, X., Jin, W., Xu, M., Zhao, H., etal. (2010). Genome-widepatterns of genetic variation among elite maize inbred lines. Nat. Genet. 42, 1027-1030. doi: 10.1038/ng.684
35. Li, J. Y., Wang, J., and Zeigler, R. S. (2014a). The 3,000 rice genomes project: new opportunities and challenges for future rice research. Gigascience 3, 8. doi: 10.1186/2047-217X-3-8
36. Li, L., Zhang, Q., and Huang, D. (2014b). A reviewofimaging techniques for plant phenotyping. Sensors(Basel)14, 20078-20111. doi: 10.3390/s141120078
37. Li, Y. H., Zhao, S. C., Ma, J. X., Li, D., Yan, L., Li, J., et al. (2013). Molecularfootprints of domesticationand improvementinsoybeanrevealed by whole genome re-sequencing. BMC Genomics 14:579. doi: 10.1186/1471-216414-579
38. Libbrecht, M. W., and Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321-332. doi: 10.1038/nrg3920
39. Liebisch, F., Kirchgessner, N., Schneider, D., Walter, A., and Hund, A. (2015). Remote,aerialphenotypingofmaizetraitswithamobilemulti-sensorapproach. PlantMethods11, 9. doi: 10.1186/s13007-015-0048-8
40. Lin, T., Zhu, G., Zhang, J., Xu, X., Yu, Q., Zheng, Z., et al. (2014). Genomicanalyses provide insights into the historyoftomato breeding. Nat. Genet. 46, 1220-1226. doi: 10.1038/ng.3117
41. Liu, J., Feng, L., Li, J., and He, Z. (2015). Geneticandepigeneticcontrolofplantheat responses. Front. Plant Sci. 6:267. doi: 10.3389/fpls.2015.00267
42. Ma, C., Xin, M., Feldmann, K. A., and Wang, X. (2014). Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis. PlantCell26, 520-537. doi: 10.1105/tpc.113.121913
43. Matsuda, F., Nakabayashi, R., Yang, Z., Okazaki, Y., Yonemaru, J., Ebana, K., et al. (2015). Metabolome-genome-wide association study dissects genetic architectureforgeneratingnaturalvariationinricesecondarymetabolism. Plant J. 81, 13-23. doi: 10.1111/tpj.12681
44. Matsuzaki, J., Kawahara, Y., and Izawa, T. (2015). Punctual transcriptional regulation by the rice circadian clock under fluctuating field conditions. Plant Cell 27, 633-648. doi: 10.1105/tpc.114.135582
45. Mittler, R., and Blumwald, E. (2010). Genetic engineering for modern agriculture: challenges and perspectives. Annu. Rev. Plant Biol. 61, 443-462. doi: 10.1146/annurev-arplant-042809-112116
46. Miyazaki, Y., Maruyama, Y., Chiba, Y., Kobayashi, M. J., Joseph, B., Shimizu, K. K., et al. (2014). Nitrogen as a key regulator of flowering in Fagus crenata: understanding the physiological mechanism of masting by gene expression analysis. Ecol. Lett. 17, 1299-1309. doi: 10.1111/ele.12338
47. Mochida, K., Furuta, T., Ebana, K., Shinozaki, K., and Kikuchi, J. (2009). Correlation exploration of metabolic and genomic diversity in rice. BMC Genomics 10:568. doi: 10.1186/1471-2164-10-568
48. Mochida, K., and Shinozaki, K. (2010). Genomics and bioinformatics resources for crop improvement. Plant Cell Physiol. 51, 497-523. doi: 10.1093/pcp/pcq027
49. Mochida, K., and Shinozaki, K. (2011). Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol. 52, 2017-2038. doi: 10.1093/pcp/pcr153
50. Mochida, K., and Shinozaki, K. (2013). Unlocking Triticeae genomics to sustainably feed the future. Plant Cell Physiol. 54, 1931-1950. doi: 10.1093/pcp/pct163
51. Mochida, K., Uehara-Yamaguchi, Y., Yoshida, T., Sakurai, T., and Shinozaki, K. (2011). Global landscape of a co-expressed gene network in barley and its application to gene discovery in Triticeae crops. Plant Cell Physiol. 52, 785-803. doi: 10.1093/pcp/pcr035
52. Morris, G. P., Ramu, P., Deshpande, S. P., Hash, C. T., Shah, T., Upadhyaya, H. D., et al. (2013). Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc. Natl. Acad. Sci. U.S.A. 110, 453-458. doi: 10.1073/pnas.1215985110
53. Nagano, A. J., Sato, Y, Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., et al. (2012). Deciphering and prediction of transcriptome dynamics under fluctuating field conditions. Cell 151, 1358-1369. doi: 10.1016/j.cell.2012.10.048
54. Nakashima, K., Yamaguchi-Shinozaki, K., and Shinozaki, K. (2014). The transcriptional regulatory network in the drought response and its crosstalk in abiotic stress responses including drought, cold, and heat. Front. Plant Sci. 5:170. doi: 10.3389/fpls.2014.00170
55. Nourani, E., Khunjush, F., and Durmus, S. (2015). Computational approaches for prediction of pathogen-host protein-protein interactions. Front. Microbiol. 6:94. doi: 10.3389/fmicb.2015.00094
56. Obayashi, T., Okamura, Y., Ito, S., Tadaka, S., Aoki, Y., Shirota, M., et al. (2014). ATTED-II in 2014: evaluation ofgene coexpression in agriculturallyimportant plants. Plant Cell Physiol. 55, e6. doi: 10.1093/pcp/pct178
57. Onda, Y., Hashimoto, K., Yoshida, T., Sakurai, T., Sawada, Y., Hirai, M. Y., et al. (2015). Determination of growth stages and metabolic profiles in Brachypodium distachyon for comparison of developmental context with Triticeae crops. Proc. Biol. Sci. 282.
58. Poland, J. A., Brown, P. J., Sorrells, M. E., and Jannink, J. L. (2012). Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7:e32253. doi: 10.1371/journal.pone.0032253
59. Richards, C. L., Rosas, U., Banta, J., Bhambhra, N., and Purugganan, M. D. (2012). Genome-wide patterns of Arabidopsis gene expression in nature. PLoS Genet. 8, 482-495. doi: 10.1371/journal.pgen.1002662
60. Romay, M. C., Millard, M. J., Glaubitz, J. C., Peiffer, J. A., Swarts, K. L., Casstevens, T. M., et al. (2013). Comprehensive genotyping of the USA national maize inbred seed bank. GenomeBiol. 14, R55. doi: 10.1186/gb-2013-14-6-r55
61. Saisho, D., Ishii, M., Hori, K., and Sato, K. (2011). Natural variation of barley vernalization requirements: implication of quantitative variation of winter growth habit as an adaptive trait in East Asia. Plant Cell Physiol. 52, 775-784. doi: 10.1093/pcp/pcr046
62. Saisho, D., and Purugganan, M. D. (2007). Molecular phylogeography of domesticated barley traces expansion of agriculture in the Old World. Genetics 177, 1765-1776. doi: 10.1534/genetics.107.079491
63. Sakurai, G., Satake, A., Yamaji, N., Mitani-Ueno, N., Yokozawa, M., Feugier, F. G., et al. (2015). In silico simulation modeling reveals the importance of the Casparian strip for efficient silicon uptake in rice roots. Plant Cell Physiol. 56, 631-639. doi: 10.1093/pcp/pcv017
64. Sakurai, T., Yamada, Y., Sawada, Y., Matsuda, F., Akiyama, K., Shinozaki, K., et al. (2013). PRIMe Update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation. Plant Cell Physiol. 54, e5. doi: 10.1093/pcp/pcs184
65. Satake, A., Kawagoe, T., Saburi, Y., Chiba, Y., Sakurai, G., and Kudoh, H. (2013). Forecasting flowering phenology under climate warming by modelling the regulatory dynamics of flowering-time genes. Nat. Commun. 4, 2303. doi: 10.1038/ncomms3303
66. Sato, Y., Antonio, B., Namiki, N., Motoyama, R., Sugimoto, K., Takehisa, H., et al. (2011). Field transcriptome revealed critical developmental and physiological transitions involved intheexpressionofgrowthpotentialinjaponica rice. BMC Plant Biol. 11:10. doi: 10.1186/1471-2229-11-10
67. Schmitz, R. J., Schultz, M. D., Urich, M. A., Nery, J. R., Pelizzola, M., Libiger, O., et al. (2013). Patterns ofpopulation epigenomic diversity. Nature 495, 193-198. doi: 10.1038/nature11968
68. Schmutz, J., Mcclean, P. E., Mamidi, S., Wu, G. A., Cannon, S. B., Grimwood, J., et al. (2014). A reference genome for common bean and genome-wide analysis of dual domestications. Nat. Genet. 46, 707-713. doi: 10.1038/ng.3008
69. Seki, M., Feugier, F. G., Song, X. J., Ashikari, M., Nakamura, H., Ishiyama, K., et al. (2015). A mathematical model of phloem sucrose transport as a new tool for designing rice panicle structure for high grain yield. Plant Cell Physiol. 56, 605-619. doi: 10.1093/pcp/pcu191
70. Sonah, H., O'Donoughue, L., Cober, E., Rajcan, I., and Belzile, F. (2015). Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnol. J. 13, 211-221. doi: 10.1111/pbi.12249
71. Subbaiyan, G. K., Waters, D. L., Katiyar, S. K., Sadananda, A. R., Vaddadi, S., and Henry, R. J. (2012). Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol. J. 10, 623-634. doi: 10.1111/j.1467-7652.2011.00676.x
72. Tohge, T., Mettler, T., Arrivault, S., Carroll, A. J., Stitt, M., and Fernie, A. R. (2011). From models to crop species: caveats and solutions for translational metabolomics. Front. Plant Sci. 2:61. doi: 10.3389/fpls.2011.00061
73. Vankudavath, R. N., Bodanapu, R., Sreelakshmi, Y., and Sharma, R. (2012). High-throughput phenotyping of plant populations using a personal digital assistant. Methods Mol. Biol. 918, 97-116. doi: 10.1007/978-1-61779-995-2_8
74. Webb, A. A., and Satake, A. (2015). Understanding circadian regulation of carbohydrate metabolism in Arabidopsis using mathematical models. Plant Cell Physiol. 56, 586-593. doi: 10.1093/pcp/pcv033
75. Weigel, D., and Colot, V. (2012). Epialleles in plant evolution. Genome Biol. 13, 249. doi: 10.1186/gb-2012-13-10-249
76. Wen, W, Li, D., Li, X., Gao, Y., Li, W, Li, H., et al. (2014). Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat. Commun. 5, 3438. doi: 10.1038/ncomms4438
77. Woods, D. P., Ream, T. S., and Amasino, R. M. (2014). Memory of the vernalized state in plants including the model grass Brachypodium distachyon. Front. Plant Sci. 5:99. doi: 10.3389/fpls.2014.00099
78. Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., et al. (2014). Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat. Commun. 5, 5087. doi: 10.1038/ncomms6087
79. Zhang, Y. Y, Fischer, M., Colot, V., and Bossdorf, O. (2013). Epigenetic variation creates potential for evolution of plant phenotypic plasticity. New Phytol. 197, 314-322. doi: 10.1111/nph.12010