中国水稻科学 ›› 2020, Vol. 34 ›› Issue (4): 300-306.DOI: 10.16819/j.1001-7216.2020.9083
收稿日期:
2019-07-16
修回日期:
2019-10-14
出版日期:
2020-07-10
发布日期:
2020-07-10
通讯作者:
谢先芝
基金资助:
Received:
2019-07-16
Revised:
2019-10-14
Online:
2020-07-10
Published:
2020-07-10
Contact:
Xianzhi XIE
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。
中图分类号:
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.
Yongbin PENG, Xianzhi XIE. Application of Phenomics in Rice Research[J]. Chinese Journal OF Rice Science, 2020, 34(4): 300-306.
[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. |
[1] | 郭展, 张运波. 水稻对干旱胁迫的生理生化响应及分子调控研究进展[J]. 中国水稻科学, 2024, 38(4): 335-349. |
[2] | 韦还和, 马唯一, 左博源, 汪璐璐, 朱旺, 耿孝宇, 张翔, 孟天瑶, 陈英龙, 高平磊, 许轲, 霍中洋, 戴其根. 盐、干旱及其复合胁迫对水稻产量和品质形成影响的研究进展[J]. 中国水稻科学, 2024, 38(4): 350-363. |
[3] | 许丹洁, 林巧霞, 李正康, 庄小倩, 凌宇, 赖美玲, 陈晓婷, 鲁国东. OsOPR10正调控水稻对稻瘟病和白叶枯病的抗性[J]. 中国水稻科学, 2024, 38(4): 364-374. |
[4] | 候小琴, 王莹, 余贝, 符卫蒙, 奉保华, 沈煜潮, 谢杭军, 王焕然, 许用强, 武志海, 王建军, 陶龙兴, 符冠富. 黄腐酸钾提高水稻秧苗耐盐性的作用途径分析[J]. 中国水稻科学, 2024, 38(4): 409-421. |
[5] | 胡继杰, 胡志华, 张均华, 曹小闯, 金千瑜, 章志远, 朱练峰. 根际饱和溶解氧对水稻分蘖期光合及生长特性的影响[J]. 中国水稻科学, 2024, 38(4): 437-446. |
[6] | 刘福祥, 甄浩洋, 彭焕, 郑刘春, 彭德良, 文艳华. 广东省水稻孢囊线虫病调查与鉴定[J]. 中国水稻科学, 2024, 38(4): 456-461. |
[7] | 陈浩田, 秦缘, 钟笑涵, 林晨语, 秦竞航, 杨建昌, 张伟杨. 水稻根系和土壤性状与稻田甲烷排放关系的研究进展[J]. 中国水稻科学, 2024, 38(3): 233-245. |
[8] | 缪军, 冉金晖, 徐梦彬, 卜柳冰, 王平, 梁国华, 周勇. 过量表达异三聚体G蛋白γ亚基基因RGG2提高水稻抗旱性[J]. 中国水稻科学, 2024, 38(3): 246-255. |
[9] | 尹潇潇, 张芷菡, 颜绣莲, 廖蓉, 杨思葭, 郭岱铭, 樊晶, 赵志学, 王文明. 多个稻曲病菌效应因子的信号肽验证和表达分析[J]. 中国水稻科学, 2024, 38(3): 256-265. |
[10] | 朱裕敬, 桂金鑫, 龚成云, 罗新阳, 石居斌, 张海清, 贺记外. 全基因组关联分析定位水稻分蘖角度QTL[J]. 中国水稻科学, 2024, 38(3): 266-276. |
[11] | 魏倩倩, 汪玉磊, 孔海民, 徐青山, 颜玉莲, 潘林, 迟春欣, 孔亚丽, 田文昊, 朱练峰, 曹小闯, 张均华, 朱春权. 信号分子硫化氢参与硫肥缓解铝对水稻生长抑制作用的机制[J]. 中国水稻科学, 2024, 38(3): 290-302. |
[12] | 周甜, 吴少华, 康建宏, 吴宏亮, 杨生龙, 王星强, 李昱, 黄玉峰. 不同种植模式对水稻籽粒淀粉含量及淀粉关键酶活性的影响[J]. 中国水稻科学, 2024, 38(3): 303-315. |
[13] | 关雅琪, 鄂志国, 王磊, 申红芳. 影响中国水稻生产环节外包发展因素的实证研究:基于群体效应视角[J]. 中国水稻科学, 2024, 38(3): 324-334. |
[14] | 许用强, 姜宁, 奉保华, 肖晶晶, 陶龙兴, 符冠富. 水稻开花期高温热害响应机理及其调控技术研究进展[J]. 中国水稻科学, 2024, 38(2): 111-126. |
[15] | 吕海涛, 李建忠, 鲁艳辉, 徐红星, 郑许松, 吕仲贤. 稻田福寿螺的发生、危害及其防控技术研究进展[J]. 中国水稻科学, 2024, 38(2): 127-139. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||