Application of Phenomics in Rice Research

doi:10.16819/j.1001-7216.2020.9083

Abstract

Abstract:

As the staple crop throughout the world, rice functional genomic research for the important agronomic traits is a hotspot in plant biology research area. The genotype, phenotype, and environmental factors are three critical components of the genetic research in plant. The development of high-throughput sequencing technology makes plant genotyping easier. However, phenomics research has trailed the genotype research. Being labor-intensive, high-cost, and susceptible to artificial subjectivity, the conventional phenotyping techniques could not obtain adequate genetic information. Phenomics technique, which is non-invasive and high-throughput to obtain accurate phenotypes, plays a key role in solving the above problems existing in traditional phenotyping techniques. In this review, the application of plant phenomics in rice research is summarized, which provides theoretical support for the modern rice research methods that combines genomics with phenomics.

Key words: rice, phenomics, agronomic trait, genomics

摘要：

水稻作为重要粮食作物,其重要农艺性状的功能基因组研究是植物生物学研究的热点。植物基因型、表型和环境三者构成了遗传学研究的铁三角。随着高通量测序技术的快速发展,基因型的研究更加简单快速。然而表型研究严重滞后于基因型研究。目前传统的表型信息的获取存在所需劳动量巨大、成本高,所获取表型数据受人工主观因素影响大等诸多难题。高通量表型组学可无损、高通量、精准获取表型信息,对解决以上难题具有关键作用。本文对表型组学在水稻中的研究应用情况进行整理,为今后水稻基因组学和表型组学等多学科相结合的现代研究手段提供理论支撑。

关键词: 水稻, 表型组学, 农艺性状, 基因组学

CLC Number:

Yongbin PENG, Xianzhi XIE. Application of Phenomics in Rice Research[J]. Chinese Journal OF Rice Science, 2020, 34(4): 300-306.

彭永彬, 谢先芝. 表型组学在水稻研究中的应用[J]. 中国水稻科学, 2020, 34(4): 300-306.

References 54

[1]	Tester M, Langridge P.Breeding technologies to increase crop production in a changing world[J]. Science, 2010, 327: 818-822.
[2]	Zhang Q.Strategies for developing green super rice[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 104: 16402-16409.
[3]	段凌凤, 杨万能. 水稻表型组学研究概况和展望[J]. 生命科学, 2016, 28(10): 1129-1137.
	Duan L F, Yang W N.Research advances and future scenarios of rice phenomics[J]. Chinese Bulletin of Life Sciences, 2016: 1129-1137. (in Chinese with English abstract)
[4]	王璟璐, 张颖, 潘晓迪, 卢宪菊, 马黎明, 郭新宇. 作物表型组数据库研究进展及展望[J]. 中国农业信息, 2018, 30(5): 13-23.
	Wang J L, Zhang Y, Pan X D, Lu X J, Ma L M, Guo X Y.Research advances and future scenarios of crop phenomics database[J]. China Agricultural Informatics, 2018, 30(5): 13-23. (in Chinese with English abstract)
[5]	方宣钧. 表型组学[J]. 分子植物育种, 2009, 7(3): 426.
	Fang X J.The phenomics[J]. Molecular Plant Breeding, 2009, 7(3): 426. (in Chinese with English abstract)
[6]	Finkel E.With ‘phenomics’ plant scientists hope to shift breeding into overdrive[J]. Science, 2009, 325: 380-381.
[7]	Pieruschka R, Poorter H.Phenotyping plants: Genes, phenes and machines[J/OL].Functional Plant Biology, 2012, 39: 813.
[8]	潘映红. 论植物表型组和植物表型组学的概念与范畴[J]. 作物学报, 2015, 41(2): 175-186.
	Pan Y H.Analysis of concepts and categories of plant phenome and phenomics[J]. Acta Agronomica Sinica, 2015, 41(2): 175-186. (in Chinese with English abstract)
[9]	Yang W N, Duan L F, Chen G X, Xiong L Z, Liu Q.Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies[J]. Current Opinion in Plant Biology, 2013, 16: 180-187.
[10]	Holtorf H, Guitton M C, Reski R.Plant functional genomics[J]. Naturwissenschaften, 2002, 89: 235-249.
[11]	Chen J J, Ding J H, Ouyang Y D, Du H Y, Yang J Y, Cheng K, Zhao J, Qiu S Q, Zhang X L, Yao J L, Liu K D, Wang L, Xu C G, Li X H, Xue Y B, Xia M, Ji Q, Lu J F, Xu M L, Zhang Q F.A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indica-japonica hybrids in rice[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105: 11436-11441.
[12]	Li Y B, Fan C C, Xing Y Z, Jiang Y H, Luo L J, Sun L, Shao D, Xu C J, Li X H, Xiao J H, He Y Q, Zhang Q F.Natural variation in GS5 plays an important role in regulating grain size and yield in rice[J]. Nature Genetics, 2011, 43: 1266-1269.
[13]	Hu H H, Dai M Q, Yao J L, Xiao B Z, Li X H, Zhang Q F, Xiong L Z.Overexpressing a NAM, ATAF, and CUC (NAC) transcription factor enhances drought resistance and salt tolerance in rice[J]. Proceedings of the National Academy of Sciences, 2006, 103: 12987-12992.
[14]	Wu C Y, You C J, Li C S, Long T, Chen G X, Byrne M, Zhang Q F.RID1, encoding a Cys2/His2-type zinc finger transcription factor, acts as a master switch from vegetative to floral development in rice[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105: 12915-12920.
[15]	Peng S.Single-leaf and canopy photosynthesis of rice[J]. Studies in Plant Science, 2000, 7: 213-228.
[16]	Gu J, Yin X, Stomph T J, Struik P C.Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis[J]. Plant Cell & Environment, 2013, 37: 22-34.
[17]	Tucker C J, Garratt M W.Leaf optical system modeled as a stochastic process[J/OL].Applied Optics, 1977, 16: 635.
[18]	Mech R.Modeling and simulation of the interaction of plants with the environment using l-systems and their extensions[D]. Calgary, Alberta, Canada: University of Calgary, 1997.
[19]	Chang T G, Zhao H L, Wang N, Song Q F, Xiao Y, Qu M, Zhu X G.A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties[J]. Journal of Experimental Botany, 2019, 70: 2479-2490.
[20]	Wang F L, Wang F M, Zhang Y, Hu J H, Huang J F, Xie J K.Rice yield estimation using parcel-level relative spectral variables from UAV-based hyperspectral imagery[J/OL].Frontiers in Plant Science, 2019, 10: 453.
[21]	Jiang Q, Fang S H, Peng Y, Gong Y, Zhu R S, Wu X T, Ma Y, Duan B, Liu J. UAV-based biomass estimation for rice-combining spectral, TIN-based structural and meteorological features[J/OL]. Remote Sensing, 2019, 11: 890. .
[22]	Duan B, Fang S H, Zhu R S, Wu X T, Wang S Q, Gong Y, Peng Y.Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis[J/OL].Frontiers in Plant Science, 2019, 10: 204. DOI: 10.3389/fpls.2019.00204.
[23]	Zhu F Y, Thapa S, Gao T, Ge Y F, Walia H, Yu H.3D reconstruction of plant leaves for high-throughput phenotyping[C]// Institute of Electrical and Electronics Engineers. IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018.
[24]	Liu K L, Li Y Z, Han T F, Yu X C, Ye H C, Hu H W, Hu Z H.Evaluation of grain yield based on digital images of rice canopy[J/OL].Plant Methods, 2019, 15, DOI: 10.1186/ s13007-019-0416-x.
[25]	Zhang K, Ge X K, Shen P C, Li W Y, Liu J X, Cao Q, Zhu Y, Cao Q, Tian Y C.Predicting rice grain yield based on dynamic changes in vegetation indexes during early to mid-growth stages[J/OL].Remote Sensing, 2019, 11: 387.
[26]	Din M, Ming J, Hussain S, Ata-UI-Karim S, Rashid, M, Tahir M, Hua, S Z, Wang S Q. Estimation of dynamic canopy variables using hyperspectral derived vegetation indices under varying N rates at diverse phenological stages of rice[J/OL].Frontiers in Plant Science, 2018, 9: 1883.
[27]	Padmavathi C, Balakrishnan D, Vgn T V, Javvaji S, Muthusamy S K, Venkata S R L, Neelamraju S, Katti G. Phenotyping and genotype × environment interaction of resistance to leaffolder, cnaphalocrocis medinalis guenee (lepidoptera: pyralidae) in rice[J/OL].Frontiers in Plant Science, 2019, 10: 49.
[28]	Chattopadhyay K, Behera L, Bagchi T B, Sardar S S, Moharana N, Patra N R, Chakraborti M, Das A, Marndi B C, Sarkar A, Ngangkham U, Chakraborty K, Bose L K, Sarkar S, Ray S, Sharma S.Detection of stable QTLs for grain protein content in rice (Oryza sativa L.) employing high throughput phenotyping and genotyping platforms[J/OL]. Scientific Report, 2019, 9: 3196.
[29]	Atsunori F, Katsuhiko K, Takashi I, Toshiyuki, T, Tanabata T, Yamamoto T. A novel QTL associated with rice canopy temperature difference affects stomatal conductance and leaf photosynthesis[J]. Breeding Science, 2018, 68: 305-315.
[30]	Hinsinger P, Bengough A G, Vetterlein D, Young L M.Rhizosphere: Biophysics, biogeochemistry and ecological relevance[J]. Plant & Soil, 2009, 321: 117-152.
[31]	Hodge A, Berta G, Doussan C, Merchan F, Crespi M.Plant root growth, architecture and function[J]. Plant & Soil, 2009, 321: 153-187.
[32]	Han T H, Kuo Y F.Developing a system for three- dimensional quantification of root traits of rice seedlings[J]. Computers and Electronics in Agriculture, 2018, 152: 90-100.
[33]	Mohammed U, Caine R S, Atkinson J A, Harrison E L, Wells D, Chater C C, Gray J E, Swarup R, Murchie E H.Rice plants overexpressing OsEPF1 show reduced stomatal density and increased root cortical aerenchyma formation[J]. Scientific Report, 2019, 9: 5584.
[34]	Das B, Sahoo R N, Pargal S, Krishna G, Verma R, Chinnusamy V, Sehgal, V K, Gupta V K, Dash S K, Swain P. Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2018, 192: 41-51.
[35]	Duan L F, Han J W, Guo Z L, Tu H F, Yang P, Zhang D, Fan Y, Chen G X, Xiong L Z, Dai M Q, Williams K, Corke F, Doonan J H, Yang W N.Novel digital features discriminate between drought resistant and drought sensitive rice under controlled and field conditions[J]. Frontiers in Plant Science, 2018, 9: 492.
[36]	Zhu C W, Kobayashi K, Loladze I, Zhu J G, Jiang Q, Xu X, Liu G, Saman S, Kristie L. E, Adam D, Naomi K. F, Lewis H. Z. Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries[J]. Science Advances, 2018, 4: eaaq1012. DOI: 10.1126/sciadv.aaq1012.
[37]	Moura D S, Brito G G, Moraesítalo L, Fagundes P, Castro A, Deuner S.Cold tolerance in rice plants: phenotyping procedures for physiological breeding[J]. Journal of Agricultural Science, 2017, 10: 313.
[38]	Wang J J, Li Z K, Jin X L, Liang G H, Struik PC, Gu J F, Zhou Y.Phenotyping flag leaf nitrogen content in rice using a three-band spectral index[J]. Computers and Electronics in Agriculture, 2019, 162: 475-481.
[39]	Qu M N, Zheng G Y, Hamdani S, Essmine J, Song Q F, Wang H R, Chu C C, Sirault X, Zhu X G.Leaf photosynthetic parameters related to biomass accumulation in a global rice diversity survey[J]. Plant Physiology, 2017, 175: 332.
[40]	Ghosal S, Jr C C, Quilloy F A, Septiningsih E M, Mendioro M S, Dixit S.Deciphering genetics underlying stable anaerobic germination in rice: phenotyping, QTL identification, and interaction analysis[J]. Rice, 2019, 12: 50.
[41]	Furbank R T. Plant phenomics: From gene to form and function[J]. Functional Plant Biology, 2009, 36: V-VI.
[42]	Campbell M T, Knecht A C, Berger B, Brien C J, Wang D, Walia, H. Integrating image-based phenomics and association analysis to dissect the genetic architecture of temporal salinity responses in rice[J]. Plant Physiology, 2015, 168: 1476-1489.
[43]	Dingkuhn M, Pasco R, Pasuquin J M, Pasuquin J M, Damo J, Soulié J C, Raboin L M, Dusserre J, Sow A, Manneh B, Shrestha S, Kretzschmar T.Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice: Ⅱ. Thermal stress and spikelet sterility[J]. Journal of Experimental Botany, 2018: 713.
[44]	Anupama A, Bhugra S, Lall B, Chaudhury S, Chugh A.Assessing the correlation of genotypic and phenotypic responses of indica, rice varieties under drought stress[J]. Plant Physiology & Biochemistry, 2018, 127: 343-354.
[45]	Momen M, Campbell MT, Walia H, Morota G.Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic legendre polynomials and B-splines[J]. G3-Genes Genomes Genetics, 2019, 9: 3369-3380.
[46]	Rebolledo MC, Dingkuhn M, Clément-Vidal A, Rouan L, Luquet D.Phenomics of rice early vigour and drought response: Are sugar related and morphogenetic traits relevant?[J]. Rice, 2012, 5(1): 22. DOI: https://doi.org/ 10.1186/1939-8433-5-22.
[47]	Yang W N, Guo Z L, Huang C L, Wang K, Jiang N, Feng H, Chen G X, Liu Q, Xiong L Z.Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer[J]. Journal of Experimental Botany, 2015, 66: 5605-5615.
[48]	Chern C G, Fan M J, Yu S M, Hour A L, Lu P C, Lin Y C, Wei F J, Huang S C, Chen S, Lai M H, Tseng C S, Yen H M, Jwo W S, Wu C C, Yang T L, Li L S, Kuo Y C, Li S M, Wey C K, Trisiriroj A, Lee H F, Hsing Y I C. A rice phenomics study: Phenotype scoring and seed propagation of a T-DNA insertion-induced rice mutant population[J]. Plant Molecular Biology, 2007, 65: 427-438.
[49]	Yang W L, Xu X C, Duan L F, Luo Q M, Chen S B, Zeng S Q, Liu Q.High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography[J]. Review of Scientific Instruments, 2011, 82(2): 025102-025109. DOI: 10.1063/1.3531980.
[50]	冯慧, 熊立仲, 陈国兴, 杨万能, 刘谦. 基于高光谱成像和主成分分析的水稻茎叶分割[J]. 激光生物学报, 2015, 24(1). 31-37.
	Feng H, Xiong L Z, Chen G X, Yang W N, Liu Q.The segmentation of leaf and stem of individual rice plant with hyperspectral imaging system and principal component analysis[J]. Acta Laser Biology Sinica, 2015, 24(1): 31-37. (in Chinese with English abstract)
[51]	Knecht A C, Campbell M T, Caprez A, Swanson D R, Walia H.Image Harvest: an open-source platform for high-throughput plant image processing and analysis[J]. Journal of Experimental Botany, 2016, 67(11): 3587-3599.
[52]	Li D Y, Huang Z Y, Song S H, Xin Y Y, Mao D H, Lv Q M, Zhou M, Tian D M, Tang M F, Wu Q, Liu X, Chen T T, Song X W, Fu X Q, Zhao B R, Liang C Z, Li A, Liu G Z, Li S G, Hu S N, Cao X F, Yu J, Yuan L P, Chen C Y, Zhu L H.Integrated analysis of phenome, genome, and transcriptome of hybrid rice uncovered multiple heterosis-related loci for yield increase[J]. Proceedings of the National Academy of Sciences, 2016: 201610115.
[53]	Wu H P, Wei F J, Wu C C, Lo S F, Chen L J, Fan M J, Chen S, Wen L C, Yu S M, David T H, Lai M H, Hsing Y C.Large-scale phenomics analysis of a T-DNA tagged mutant population[J]. GigaScience, 2017, 6(8): 1-7.
[54]	Urano D, Leong R, Wu T Y, Jones A M.Quantitative morphological phenomics of rice G protein mutants portend autoimmunity[J/OL].Developmental Biology, 2019, DOI: 10. 1016/j. ydbio. 2019. 09. 007.