水稻分蘖逆制的可塑性生长发育试验和模型辅助分析：应用植物生长模型缩减基因型与表现型的间隙
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摘要
表现型是基因型和环境相互作用的结果,不同环境条件下给定基因型能表达为不同的表型,这是我们所熟知的植物表型可塑性。可塑性一方面帮助植物更好地适应不利环境,但我们也不得不承认可塑性,使得人们难以从表型直接理解基因功能。如今,基因组学快速发展允许解密基因更迅速便捷,甚至发现大量基因。因此,进一步理解可塑性过程的基因背景、理解基因和环境对表型的作用非常必要。由于从基因到表型非线性过程,从而引起基因型和表型差异,期望有效方法或工具能跨越这个横沟。植物生长模型已被开发用来模拟植物响应环境动态关系,并且将参数和环境整合到模型方法中。因此,普遍认为植物生长模型将在探讨复杂可塑性基因功能扮演重要作用。水稻是普遍应用在基因组学和功能基因组学典型的模式植物。水稻分蘖是重要的基因依赖环境敏感的过程,这是农学上非常关注的现象。本文将应用模型方法理解水稻分蘖逆制的可塑性。本研究设计了一个相对优化环境条件下,野生型水稻分蘖逆制试验,该试验有两个处理(1)手工剪切分蘖;(2)一个TDNA突变体,并分别设置对照。本试验在法国国际农业研究发展中心(CIRAD)温室开展,每个试验利用水培方法,培育植株50天左右(营养生长阶段)。在营养生长阶段,定期破坏性测量单个器官的鲜重、干重和单个器官的大小。本文尝试应用两个植物生长模型模拟和解释水稻响应分蘖逆制表型发育。GreenLab是一个植物结构数学模型,已被开发用来模拟植物结构动态和结构功能反馈。植物3D结构决定光捕获和生物产量,然后,生物量分配到新的器官,因此,器官形态结构将发生变化,新阶段的生物量生产将会更新。通过基于最小二乘法的CornerFit软件实现了模型参数优化。另一个模型EcoMeristem,基于作物模型和形态发生概念,用来模拟水稻分生组织活动、器官发生和形态过程等可塑性过程,内部竞争指数Ic主要与环境相关,参数主要描述基因功能。通过植物生长过程模拟与测量的优化,手工提取了模型参数。这两个植物生长模型演示了缩减基因型与表型之间的差距,并实现了水稻响应分蘖完全逆制的可塑性过程。GreenLab模型有一个极好的器官发生基础,但本研究限于单茎拓扑结构。另外,该模型有更长的时间步长,这对描述植物可塑性没有提供足够的分辨能力,这在EcoMeristem模型中得到了解决。很明显,EcoMeristem模型有更弱的结构基础,这可能蕴含了一些可塑性信息的缺失。总体而言,EcoMeristem模型有更专业的可塑性过程、基因环境理解和表达能力。
Phenotype is the result of the interaction of genotype and environment, a given genotype can be expressed as different phenotypes in different environments, which is known as plant inherent phenotypic plasticity. The plasticity helps plant to be adapted to adverse environment, nevertheless, but we have to admit that plasticity make it difficult to uncover genetic action from many phenotypic forms. Nowadays genomics rapid evolving allows decoding the genome more easily, and even discovering massive genes, therefore the expanding phenotype requires to be explored. Within the context, it will be essential to further understand the genetic profiles from plastic process and understand the contributions of gene and environment to phenotypes.
The gap exists between genotype and phenotype as the non-linear process from gene to protein, and phenotype, efficient approaches or tools are expected to span the gap between genotype and phenotype. Plant growth models have been developed to simulate plant growth kinetics response to environment, parameters and environmental conditions have been incorporated into modeling approaches. Thus, plant growth models were reckoned to play one important role to unravel plant real genetic action from complex plasticity. Rice is one typical model plant widely used in genomics or functional genomics, rice tillering is genotype dependant and environment sensitive. In the paper, we will study rice plastic development responding to tillering inhibition towards understanding genotype-phenotype and plasticity, and examine the ability of plant growth models to address plastic modeling within G×E.
Two experiments were successively carried out in an automatic controlled growth chamber at CIRAD (Montpellier, France). The experiments were designed with a reference (wild type) genotype Nippon Bare (1) grown under optimal conditions; (2) with a systematic (manual) pruning of tillers; and (3) one of its TDNA organogenesis-deficient mutant (phyllo, a knock out mutant based on Nippon bare genetic background). Plants were cultivated until 50 days (vegetative stage) after germination with a hydroponics system. A set of three to four plants per treatment were destructively measured to achieve fresh weight, dry weight and dimensions of individual organs.
Two plant growth models were attempted to interpret and simulate rice phenotypic development in response to tillering inhibition. GreenLab, one mathematical plant architectural model, was developed to simulate plant architectural dynamics and feedback of architecture and physiological function. Plant 3D architecture determines the radiation capture and thus biomass production, whereafter biomass is allocated to new organs designated as thermal time, the morphological architecture (e.g. geometric dimension & leaf angle) of organs will change as biomass accumulation, thus biomass production will be renewed in new growth cycle. Parameters optimization was achieved with least square method by CornerFit software. The other model EcoMeristem, one crop model basis with morphogenesis concept, was introduced here to simulate
Phenotype is the result of the interaction of genotype and environment, a given genotype can be expressed as different phenotypes in different environments, which is known as plant inherent phenotypic plasticity. The plasticity helps plant to be adapted to adverse environment, nevertheless, but we have to admit that plasticity make it difficult to uncover genetic action from many phenotypic forms. Nowadays genomics rapid evolving allows decoding the genome more easily, and even discovering massive genes, therefore the expanding phenotype requires to be explored. Within the context, it will be essential to further understand the genetic profiles from plastic process and understand the contributions of gene and environment to phenotypes.
The gap exists between genotype and phenotype as the non-linear process from gene to protein, and phenotype, efficient approaches or tools are expected to span the gap between genotype and phenotype. Plant growth models have been developed to simulate plant growth kinetics response to environment, parameters and environmental conditions have been incorporated into modeling approaches. Thus, plant growth models were reckoned to play one important role to unravel plant real genetic action from complex plasticity. Rice is one typical model plant widely used in genomics or functional genomics, rice tillering is genotype dependant and environment sensitive. In the paper, we will study rice plastic development responding to tillering inhibition towards understanding genotype-phenotype and plasticity, and examine the ability of plant growth models to address plastic modeling within G×E.
Two experiments were successively carried out in an automatic controlled growth chamber at CIRAD (Montpellier, France). The experiments were designed with a reference (wild type) genotype Nippon Bare (1) grown under optimal conditions; (2) with a systematic (manual) pruning of tillers; and (3) one of its TDNA organogenesis-deficient mutant (phyllo, a knock out mutant based on Nippon bare genetic background). Plants were cultivated until 50 days (vegetative stage) after germination with a hydroponics system. A set of three to four plants per treatment were destructively measured to achieve fresh weight, dry weight and dimensions of individual organs.
Two plant growth models were attempted to interpret and simulate rice phenotypic development in response to tillering inhibition. GreenLab, one mathematical plant architectural model, was developed to simulate plant architectural dynamics and feedback of architecture and physiological function. Plant 3D architecture determines the radiation capture and thus biomass production, whereafter biomass is allocated to new organs designated as thermal time, the morphological architecture (e.g. geometric dimension & leaf angle) of organs will change as biomass accumulation, thus biomass production will be renewed in new growth cycle. Parameters optimization was achieved with least square method by CornerFit software. The other model EcoMeristem, one crop model basis with morphogenesis concept, was introduced here to simulate
引文
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2. Baker D N, Lambert J R, McKinion J M. Gossym: A simulator of cotton crop growth and yield. South Carolina Agric. 1983, Expt. Sta. Tech. Bull. 1089.
3. Birch C J, Hammer G L, Rickert K G. Dry matter accumulation and distribution in five cultivars of maize: relationship and procedures for use in crop modeling. Agric. Res, 1999, 50:512-527
4. Birch C J, Vos J, Kiniry J, et al. Phyllochron responds to acclimation to temperature and irradiance in maize. Field Crops Research, 1998, 59:187-200
5. Bonato O, Schulthess F, Baumgartner J. Simulation model for maize crop growth based on acquisition and allocation process for carbonhydrate and nitrogen. Ecological Modeling, 1999,124:11~28
6. Boote K J, Jones J W, Batchelor W D, Nafziger E D and Myers O. Genetic coefficients in the CROPGRO-Soybean Model: Links to field performance and genomics. Agronomy Journal, 2003, 95(1): 32-113.
7. Bunce J A, Sicher J R C. Daily irradiance and feedback inhibition of photosynthesis of elevated carbon dioxide in Brassica Oleracea. Photosynthesis Research, 2004, 41:481-488.
8. Cao W, Moss D N. Temperature effect of leaf emergence and phyllochron in wheat and barley. Crop Science, 1989a, 29:1018-1021.
9. Chelle M, Andrieu B. The nested radiosity model for the distribution of light within plant canopies. Ecological Modeling, 1998, 111:75-91.
10. Condon M A, Sasek T W and Strain B R. Allocation patterns in two tropical vines in response to increased atmospheric CO2. Functional Ecology, 1992, 6: 680-685.
11. Connor D J, Fereres E. A dynamic model of crop growth and partitioning of biomass. Field Crops Research, 1999, 63:139-157
12. Cookson S J, Van Lijsebettens M, Granier C. Correlation between leaf growth variables suggest intrinsic and early controls of leaf size in Arabidopsis thaliana. Plant, Cell and Environment, 2005, 28:1355-1366.
13. Dauzat J. Radiative transfer simulation on computer models of Elaeis guineensis. Oléagineux, 1994, 49: 8-90.
14. de Castro f, Fetcher N. Three dimensional model of the interception of light by canopy, Agricultural and Forest Meteorology, 1998, 90: 215-233.
15. de Reffye P and Houllier F. Modeling plant growth and architecture: some recent advances and applications to agronomy and forestry. Current Science, 1997, 73(11): 984-992.
16. de Reffye P, Blaise F, Chemouny S, et al. Calibration of hydraulic growth model on the architecture of cotton plants. Agronomie, 1999, 19: 265-280.
17. de Reffye P, Edelin C, Francon J, et al. Plant models faithful to botanical architecture and development. Computer graphics, 1988, 22(4): 151-158.
18. Dingkuhn M, Johnson D E, Sow A and Audebert A Y. Relationships between upland ricecanopy characteristics and weed competitiveness. Field Crop Research, 1999, 61(1): 79-95.
19. Dingkuhn M, Jones M P, Johnson D E and Sow A. Growth and yield potential of Oriza sativa and O. glaberrima upland rice cultivars and their interspecific progenies. Field Crop Research, 1998, 57: 57-69.
20. Dingkuhn M, Luquet D, Clement-vidal A, Tambour L, Kim H K, Song Y H. Is plant growth driven by sink regulation? Implications for crop models, phenotyping approaches and ideotypes. Wageningen plant genotype-phenotype international conference, June, 2006a (accepted).
21. Dingkuhn M, Luquet D, Kim H K, Tambour L, Clément-Vidal A. EcoMeristem, a model of morphogenesis and competition among sinks in Rice: 2. simulating genotype responses to phosphorus deficiency. Functional Plant Biology, 2006b, 32(4).
22. Dingkuhn M, Penning de Vries F.W.T, De Datta SK, van Laar HH. Concepts for a new plant type for direct seeded flooded tropical rice. In: Direct Seeded Flooded Rice in the Tropics. International Rice Research Institute, P.O. Box 933, Manila, Philippines, .1991 17-38.
23. Dingkuhn M, Schnier H F, Datta S K D, Dorffling K, Javellana X and Pamplona R. Nitrogen fertilization of direct-seeded flooded vs. transplanted rice: II- Interactions among canopy properties. Crop Science, 1990, 30: 1284-1292.
24. Dingkuhn M., Miezan KM. Climatic determinants of irrigated rice performance in the Sahel. II. Validation of photothermal constants and characterization of genotypes. Agricultural Systems, 1995, 48: 411-434.
25. Dingkuhn M., Penning de Vries F.W.T.. Improvement of rice plant type concepts: systems research enables interaction of physiology and breeding. In: Systems Approaches for Agricultural Development. F.W.T Penning de Vries et al. (Ed.). Kluwer Academic Publishers, The Netherlands, 1993, 9-35.
26. Dingkuhn, M, Luquet D, Quilot B, and de Reffye P. Environmental and genetic control of morphogenesis in crops: Towards models simulating phenotypic plasticity. Australian Journal of Agricultural Research, 2005, 56:1-14.
27. Drouet J L, Pages L. GRAAL: a model of Growth, Architecture and carbon Allocation during vegetative phase of the whole plant model description and parameterisation. Ecological Modelling, 2003, 165:147-173.
28. Dubcovsky, J. Marker-assisted selection in public breeding programs: the wheat experience. Crop Science, 2004, 44:1895-1898.
29. Dusserre J, Crozat Y, Warembourg FR, Dingkuhn M. Effects of shading on sink capacity and yield components of cotton in controlled environments. Agronomie,2002, 22: 307-320.
30. Farrar J F. Sinks-integral parts of a whole plant. Journal of Experimental Botany, 1996, 47: 1273-1279.
31. Fiorani F, Beemster GT, Bultynck L, Lambers H. Can meristem activity determine variation in leaf size and elongation rate among four poa species? A kinematic study. Plant Physiology, 2000, 124: 845-855.
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