提高光合效率是提高作物产量的有效手段。然而光合系统的复杂性使得单纯利用遗传手段鉴定提高光合效率的有效靶点无论从时间上还是从经济上成本都极高。构建并利用多尺度光合系统成为当前鉴定控制光合效率核心靶点的重要手段。本文回顾了近年来光合系统模型的主要研究进展和主要应用实例，并进一步探讨了提高作物产量的光合作用系统模型未来的发展和应用方案。
刘扶桑1,2，宋青峰1，于桂朝1，毛林雄1，朱新广1*
(1 中国科学院分子植物科学卓越创新中心，植物分子遗传国家重点实验室，上海 200032；2 荷兰瓦赫宁根大学，植物科学系，作物系统分析中心，瓦赫宁根 430, 6700 AK)
1 作物高光效改良存在巨大空间及目前面临的挑战
1.1 作物高光效改良存在巨大空间
当前全球粮食安全面临的主要挑战是世界人口增长和全球气候变化[1-3]。为了满足2050年的粮食需求，未来30年主要作物的产量必须翻一番[4]。然而，目前主要作物的增产速度低于所需的增产速度[5-6]。寻找提高主要粮食作物产量的方法迫在眉睫。作物产量潜力主要指作物在最优生长条件下能实现的最大产量。作物产量潜力(Y)可以使用Monteith公式(eqn 1)进行评估[7-8]，主要由单位土地面积接收的光能(St)、作物冠层对光能的截获效率(εi)、光能转化效率(εc)、分配效率(收获指数, εp)决定。
叶片通常吸收约90%的光合有效辐射(400~700 nm)，但大部分近红外辐射(> 700 nm) 及紫外光等叶片不能吸收，约占阳光能量的一半(0.487)。冠层对光能的截获效率(εi)是指被植物冠层截获的入射光的比例。光能转化效率(εc)是指在一定时期内产生的生物质能与在同一时期内被冠层截获的辐射能之比。分配效率(εp)，也称为收获指数，是总生物质能量分配到作物收获部分的比例。在过去50年间，作物产量潜力的提升主要是由于冠层对光能的截获效率(εi)和分配效率(εp)的提升。对于当前优良高产品种，其冠层对光能的截获效率(εi)可以达90%，分配效率(εp)可以达到0.5以上，继续增加的潜力较小。目前观测到的C3植物和C4植物的最高光能转化效率(εc)分别约为2.4%和3.7%，田间作物平均光能转化效率仅约0.5%，而理论上C3植物和C4植物的最大光能转化效率(εc)分别约为4.6%和6.0%，因此目前光能转化效率仍有巨大的提升空间[8-11]。主要作物的育种进程也表明提高光能转换效率是提高产量的可行路径。绿色革命之后至20世纪80年代以前，培育品种的产量增加主要是由于引入矮化基因提高了收获指数，而此后培育品种的产量增加主要依赖于生物量的提高，表明是通过提高光能利用效率从而提高作物产量[12-16]。长期提高大田空气中CO2含量的实验(FACE实验，free-air CO2 enrichment)也证明：提高光能转换效率可以实现生物量及产量的提高[17-18]。
1.2 作物高光效高产育种面临的挑战
传统作物高产育种很大程度上依赖育种家的个人经验。一般来讲，育种家根据自己的育种经验和育种目标的需要选择优异性状，进而将众多品系的优异性状组合在一起 [19-20]。这种传统育种方法的一个潜在假设是优异性状的聚合将导致优异性状的加和效应。然而作物表型是基因、环境与管理互作的结果[21-22]。植物的关键特征(如叶片形态、根系形态、氮吸收效率等)相互作用常常以高度非线性的方式影响作物产量[23]。因此，所谓的“优异性状”会根据环境而产生改变，并经常出现与常识相悖的认识。例如，增加气孔导度通常被认为可以提高叶片光合速率从而提高水稻产量[24]；但这个策略不适合生长在干旱环境中的作物，因为较高的气孔导度会导致较高的蒸腾[25]。再比如，育种家倾向于选择叶面积指数较高、叶色更绿的作物株系，但最近实验发现，在全球大气变化条件下，大豆叶面积减少会提高作物产量[26]。此外，理论研究表明：降低叶片叶绿素含量可以提高光合效率和氮利用效率[27]。最近的实验也表明，适当降低叶片叶绿素含量可以提高光合效率，并在不降低产量的同时提高种子氮含量[28-29]。因此，在未来气候条件下根据特定的环境条件，鉴定合适的育种目标对作物精准育种至关重要。
2 植物系统模型在植物科学及作物研究中的应用
植物系统模型是指使用数学公式定量模拟细胞、器官、植物、种群或生态系统的物理、生化和生理过程。由于系统模型可以模拟基因、环境与管理的互作，因此系统模型被认为是提高育种效率和田间栽培管理的理想工具[30-32]。目前系统模型已被用于支撑植物科学中复杂过程的研究，如光合作用、营养元素和水分吸收及植物形态发育等[33-35]。
利用系统模型指导作物改良已有诸多成功的例证。例如，基于根系的细胞结构、三维结构、生理功能和生长发育过程建立的根系系统模型OPENSI- MROOT确定了许多可优化的根系育种性状，并在田间得到验证。比如根系生长角度、轴根数、侧根分枝密度、皮层中通气组织大小、皮层的细胞数和皮层的细胞大小等都可以成为优化株型的重要靶点[36]。此外，基于木质素代谢系统模型的木质素合成改造也是一个成功例证，该研究首先基于木质素合成通路结构和代谢物对于通路中酶的负反馈调节机制建立了代谢系统模型[37]；进一步通过创制数百种木质素合成代谢通路中相关基因的转基因材料和收集不同野生型的转录组、蛋白质组和表型数据，结合此前的代谢系统模型建立了机器学习模型，并成功预测了代谢通路中基因或基因组合的表达变化如何影响蛋白质丰度、代谢通量、代谢物浓度和25个木材性状，包括木质素含量、树木的高度、密度、强度和糖化程度，并制定了改良策略[38]。基于模型制定的改造策略，已通过CRISPR基因编辑技术实现，该策略将木材碳水化合物与木质素的比例提高到野生型木材的228%，从而提高了纤维制浆的效率[39]。
系统模型也用于植物科学的机理研究和提升作物的栽培管理。在机理研究方面，系统模型成功被用于研究植物的开花机制[40-42]、进化过程[43]、光形态建成[44]等。系统模型对栽培管理也有重要支撑作用，比如基于APSIM模型，可以研究不同栽培管理措施对产量的贡献，并以此优化栽培管理措施[45]。此外，植物系统模型与气象模型结合，可以用于减少极端天气对于产量的负面影响。鉴于极端天气条件的频繁发生，改变发育进程及模式是作物适应非生物胁迫的重要策略。目前系统模型能预测不同基因型小麦在不同环境下的抽穗时间，因此，结合气候模型与系统模型可以帮助制定栽培策略，从而最大限度地减少霜冻、高温和干旱等极端天气对作物生产的影响[46]。
3 光合系统模型及作物高光效改造
3.1 不同尺度的光合系统模型及其构建方法
光合效率受到涉及光合暗反应和光反应的基因表达水平、光合作用的代谢结构、叶片内部结构、冠层结构、冠层内部和外部的环境等因素影响。在基因水平，涉及光合暗反应和光反应的基因表达水平会直接影响电子传递链能力和碳代谢能力。在代谢水平，不同的光合代谢类型和代谢结构会导致代谢效率不同，进而影响整体光合效率。在叶片内部，由于叶片表皮细胞的聚光效应和维管束鞘延伸等结构对光线在叶片内部分布的影响，不同的叶片结构会使叶片内不同的叶绿体接收到不同的光强和光质[47]。在冠层水平，不同的冠层结构会导致冠层光分布的差异和光质差异，从而进一步导致冠层中温度和湿度的空间异质性，从而影响整体冠层光合效率。此外，诸多环境因素也会影响光合效率，例如高温或者低温会导致卡尔文-本森-巴萨姆循环中酶的活性变化；湿度和温度直接影响气孔导度；太阳高度角、云的运动及风等都会扰动冠层内部的光环境进而导致温度、湿度等微环境的变化。由于光合作用的复杂性和影响光合效率因素的多样性，系统模型被广泛地应用于光合研究中。目前已经开发了各个水平的光合系统模型，包括基因调控水平[48]、代谢水平[49-51]、叶片水平[47]和冠层水平[52-55]的光合模型(图 1)。
基因调控水平的模型通常有两种建模方法：(1)基于此前大量的基因互作研究结果获取目标基因的互作关系，并与相应表型连接，最终形成基因调控网络模型[40, 42, 44]。(2)基于基因组、转录组和蛋白质组数据，通过相应算法推测基因之间的互作关系，以此获取基因表达量和对应的蛋白质含量的数量关系，再将蛋白质含量与代谢模型中的酶活性对应，从而通过代谢模型获取相应的表型[38, 48]。对于代谢模型的构建，主要先从相关的数据库获取代谢网络的结构和代谢网络中的化学反应特征和参数，由此形成每步反应的化学动力学方程和代谢物浓度变化的常微分方程完成代谢模型的构建[50, 59-60]。对于三维叶片光合模型的构建，首先基于Mirco-CT或者叶片切片获取叶片内部的三维结构，再使用光线追踪模型和CO2扩散模型实现叶片内部光分布和CO2分布的模拟，最后基于所在区域的光强和CO2浓度结合光合代谢模型获得对应的光合速率[56, 61]。对于冠层光合模型，首先需要获取植株的三维结构。获取植株的三维结构主要有两种方法：(1)测量植株的结构参数，进而通过相应的数学公式得到植株的三维结构；(2)通过机器视觉算法或者传感器(如三维数字化仪、LiDAR等)直接获取植株三维结构，然后通过光线追踪算法获取冠层光分布和光截获，最后结合光合代谢模型获得实际的光合速率[54-55, 62]。
3.2 光合代谢模型指导的高光效改造
目前，C3 [59]和C4 [50]的光合代谢系统模型都已建立。基于光合代谢模型指导的高光效改造已有诸多实践。光合碳代谢酶之间的资源分配被认为是自然选择优化的结果。然而，由于生存和繁殖力的限制，植物并不一定选择最大的光合效率。此外，与过去2 500万年相比，过去100年大气中CO2的浓度变化更大，自然选择可能没有足够的时间来重新优化光合系统的资源分配以适应环境变化。朱新广等[59]通过建立光合系统模型并应用进化算法预测了能实现最大光合效率的光合碳代谢中的蛋白质资源分配模式。结果表明增加对景天庚酮糖-1, 7-二磷酸酶(SBPase)和果糖-1, 6-二磷酸醛缩酶(FBPA)的资源投入能显著提高光合效率，而且随着大气中CO2浓度的升高，上调这些酶的益处也会增加。随后实验证明，这些酶的上调可以显著提高植物的光合效率和生物量[63]，并且在CO2浓度升高的情况下，光合效率的提升更大[64]。
C3作物的光合效率很大程度上由RuBP (核酮糖-1, 5-二磷酸)羧化酶/加氧酶(Rubisco) 的活性决定。Rubisco可以催化RuBP的羧化和氧化作用。如果RuBP被氧化，则会形成2-磷酸乙醇酸。2-磷酸乙醇酸是一种有毒的化合物，可以抑制碳代谢中的几种酶。因此，植物进化出了光呼吸途径通过一系列过程将2-磷酸乙醇酸转换成3-磷酸甘油酸(PGA)并释放一个CO2分子。光呼吸过程和RuBP的羧化过程形成竞争，而且消耗了大量光反应产生的还原力和磷酸化能力从而降低了净光合效率。光合效率降低的幅度随温度的升高而增加。因为Rubisco对CO2的特异性随着温度的升高而降低，而且温度升高时，溶液对于CO2的溶解度下降程度大于对于O2的溶解度，使得溶液中的CO2/O2比值下降。因此，光呼吸对于光合效率的损失一般为30%左右，但在高温下光合效率的损失可上升到50%左右[11]。为了减少光呼吸作用，植物中的Rubisco已经进化得对CO2更有特异性，但在进化中获得对CO2的特异性(Sc/o)似乎是以牺牲催化速率(kcat)为代价，现在的Rubisco代表了Sc/o和kcat之间的折中[65-66]。但是，这种折中的策略不一定适应当前的大气环境。系统模型模拟表明：在当前大气环境下，即使以较低的Sc/o为代价，也需要更高的kcat，这个策略可以在酶含量一致的情况下将作物冠层的光合效率提高30%[67]。Rubisco由8个大亚基(RbcL)和8个小亚基(RbcS)组成。提升Rubisco的kcat可以通过过表达RbcS实现[68-69]。最近Matsumura等[70]使用CRISPR-Cas9技术敲除水稻的RbcS，之后导入高粱的RbcS，得到了杂合的Rubisco。杂合的Rubisco有较高的kcat和较低的Sc/o。虽然转基因材料Rubisco的含量显著下降，但在高CO2条件下，转基因材料的光合能力显著高于野生型水稻。
另一个降低光呼吸的方案是设计一种更有效的途径即光呼吸支路来代谢加氧酶反应的第一个产物磷酸乙醇酸。原核生物至少有三种更简单的途径将磷酸乙醇酸代谢为PGA[71]。通过光合代谢系统模型分析了此前提出的3种光呼吸支路的潜在收益，结果表明只有一种光呼吸支路能够提高净光合效率[72]。之后的实验验证了模型的模拟结果，在此前提出的两种光呼吸支路中，只有模型支持的光呼吸支路实现了生物量的提升 [73]。
最受关注的减少光呼吸的方案是将C4作物中的CO2浓缩机制在C3作物中实现。CO2是Rubisco氧化反应的竞争性抑制剂，一些光合生物在进化中已经充分利用了这一特性，通过改变叶片内部结构和代谢通路提高了Rubisco附近的CO2含量，从而很大程度上减少了光呼吸。由于此光合途径首先将CO2固定为四碳有机酸，所以被称为C4光合途径。C4光合途径在自然界中已经成功地独立进化了60多次，这表明C3到C4光合途径的改造是可以实现的[74]。光合系统模型模拟表明实现从C3光合到C4光合的改造可以提高30%的光合效率[75-76]。同时光合系统模型也用于模拟C3光合到C4光合的进化历程，模拟表明C3光合到C4光合的进化可以从多种中间状态进化实现，而且C3到C4的进化过程非常平滑并不存在局部最优[43]。同时该研究规划了收益最大的C3光合到C4光合的改造路线，提供了C4光合改造的蓝图。
3.3 叶肉细胞及叶片三维反应扩散模型指导的高光效改造
利用叶肉细胞反应扩散模型，Tholen和朱新广[77]定量研究了控制叶肉导度的关键细胞结构及生化特征，发现细胞壁厚度、叶绿体基质内的碳酸酐酶是控制叶肉导度的重要因子；在叶肉细胞反应扩散模型的基础上，基于microCT和共聚焦显微镜等技术成功重建了叶片内部的三维结构；进而结合叶片内部的光线追踪模型\叶片内部CO2扩散模型和光合代谢模型，实现了对光线、CO2在叶片内部分布的精确模拟[47, 58]。基于三维叶片模型，模拟发现由于叶片表皮细胞的聚焦效应，叶片上层的叶绿体接收到的光强比叶表面平均光强高10倍以上[47]。这个现象暗示光抑制现象在叶片中十分普遍，这可能是目前提高抗光破坏的能力能显著提高生物量和产量的原因[78]。基于三维叶片模型，通过改变叶片结构进行敏感性分析发现，增加维管束鞘延伸的比例导致每层叶绿体在较高入射光强下光饱和的比例降低，这使得叶片总光利用效率提高[47]。维管束鞘延伸是连接维管束与表皮的薄壁组织、厚壁组织或薄壁组织细胞条，可能是叶片结构改良的潜在靶点。
近年来，三维叶片光合反应扩散模型被用于设计栽培措施以提高叶片光合作用效率、生物量及产量。具体来讲，基于叶片三维模型，肖怡等[58]系统分析了在正常及高CO2下水稻光合效率改变背后的主要结构及代谢原因。结果发现，在高CO2浓度下，水稻叶片内部结构包括叶肉细胞大小、分布、细胞间空隙等产生变化使得叶肉导度增加，而光合器官却降低了CO2固定能力。基于这个发现，将水稻在幼苗期生长在高CO2浓度下，形成具有高叶肉导度的叶片结构，进而在正常CO2浓度下生长以避免高CO2下光合能力适应，从而实现水稻叶显微结构及代谢的同时优化，并实现了叶片的光合、生物量及产量的提高[79]。
3.4 光合冠层系统模型指导的高光效改造
在高光条件下，光合天线捕获的光能多于光合作用所使用的光能，这会使得光合作用电子传递链中产生大量激发的叶绿素分子和高度还原的电子载体，导致活性氧(ROS)的形成，从而破坏光合组件[80-81]。植物进化出了非光化学猝灭(NPQ)机制将多余的能量转换为热量，从而保护光合组件免受ROS的损害[82-83]。在作物冠层中，当云层经过太阳时、风扰动冠层时或者太阳角度的持续变化会使叶片持续经历高低光的转换。在叶片经历高光时，NPQ将会触发以保护光合组件，然而NPQ的弛豫半衰期从几秒到几小时不等[84]。因此，即使叶片转移到低光后，NPQ也会继续进行，以热的形式耗散光能，但此时叶片本可以利用所有接收到的光进行光合作用。光合冠层系统模型模拟表明这将导致损失10%~40%的潜在光合作用收益[85]。最近，通过同时过表达三个影响NPQ弛豫的关键蛋白玉米黄质环氧化酶(zeaxanthin epoxidase, ZEP)、光系统Ⅱ上的S亚基(photosystem Ⅱ subunit S, PsbS)、紫黄质脱环氧化酶(violaxanthin de-epoxidase, VDE)加速了烟草和大豆中NPQ的弛豫速度，成功提高了光合作用效率与生物量[86-87]。
在叶片所经历的快速光暗转化中，除了NPQ的弛豫速度之外，气孔的开闭速度也是影响动态光合效率的主要限制因素之一。气孔是植物和大气之间气体交换的通道。气孔大小会根据外部和内部的信号进行调节以适应环境变化。增加光照、降低CO2和降低水蒸气压差(VPD)会促进气孔打开。降低光照、增加CO2和VPD以及脱落酸含量、活性氧类(ROS)等会促进气孔关闭[88]。在光暗转换时，气孔关闭的速度通常比CO2同化的下降慢一个数量级。光合系统模型模拟表明加快气孔的开闭速度能显著提高动态光合效率[89-90]。气孔开闭的速度由保卫细胞的生化、解剖和结构成分的组合决定。保卫细胞膨胀的变化是由溶质和离子(通常是钾离子、苹果酸和蔗糖分子)的吸收和释放驱动的，这些溶质和离子会改变渗透势和水流入[91]。由于其复杂性，详细的气孔系统模型已被建立用于研究离子通道、激素和环境因素对于气孔开闭的影响[92-93]。基于建立的气孔系统模型发现：操纵单个通道或转运蛋白可能不足以实现所需的气孔快速开闭，因为离子的通量或转运通常与其他通道和膜电压变化相关[94-95]。实现气孔的快速开闭可能需要考虑多个离子通道协同和溶质通量等[96]。详细的气孔系统模型提供了一个测试潜在基因编辑目标和合成生物学策略的平台。例如，将合成的蓝光门控K+通道(BLINK1) 引入拟南芥中实现了更快的气孔开闭，进而加快了光合作用诱导过程，并且提高了水分利用率 [97]。
冠层结构是冠层光合效率的主要决定因素，因为它直接影响冠层内部的光线分布和冠层微环境。叶倾角和叶面积指数(LAI)是影响光截获和冠层光合作用的两个最重要的冠层结构特征[98-99]。提高冠层不同层LAI和叶角分布的协同效应，可以优化冠层结构，进而提高冠层光合作用[8, 11]。当LAI较低(小于3)时，具有较为水平叶片的冠层可以比具有直立叶片的冠层截获更多的光。当LAI较高时，最佳的冠层通常需要顶层叶片具有更多的直立叶片，而下层叶片的叶角从顶层到底层逐渐增大，这种结构可以避免冠层上部严重的光饱和，同时允许更多的光入射到下层叶片[10, 100]。目前基于精确冠层三维结构、冠层微环境和叶片光合模型的冠层光合作用系统模型已经被成功建立[54, 101-102]。基于该模型可以对影响冠层光合效率的主要因素，如主要株型(叶角、叶长、叶宽等)、生理和生化参数等进行单因素替换分析及多因素组合分析，从而解析不同因素对于冠层光合效率的贡献率，提出冠层光合效率的改良方案[103-105]。目前已基于此方法系统分析了中国推广面积最大的优质常规稻品种“黄华占”的关键株型及生理性状，并提出了进一步优化该品种的改良方案[106]。这种模型指导的品种改良路线为未来模型、表型直接参与育种提供了重要机遇。值得一提的是，参数替换方法得到的方案是品种特异性的，只能针对特定品种提出具体的、特异的改良方案。
冠层模型指导育种的另一个重要方式是提供与育种直接相关的重要参数。目前基于冠层光合虚拟实验，我们提出了一个可以评估叶角、叶面积和叶形协同效应的参数冠层占空体积(canopy occupation volume, COV)。利用这个参数，我们可以解释不同株形变化对于冠层光合效率的影响；尤其重要的是，该参数可以通过冠层点云数据高通量获取，从而为大田作物表型平台直接支持作物育种与栽培管理提供重要途径[107]。与在作物育种中的初步应用相比，作物三维冠层模型已经在温室及大田的栽培优化方面具有较多应用。例如利用该类模型，可以针对田间作物株行距、行向进行设计，从而设计出冠层光合效率最佳栽培方式[108-109]；在温室环境条件下，利用冠层光合模型可以实时模拟冠层光分布进而支持温室内照明方案设计[110-111]。
4 支撑光合系统模型发展和应用的基因和关键技术
4.1 表型组技术
随着计算机视觉技术、传感器和机器学习技术的发展，目前已能高通量地获取各个尺度形态和生理信息。在株形结构方面，可以利用多种传感器和算法获取地上部的精确三维结构。如三维数字化技术 [101, 112-114]、深度相机[115-117]、LiDAR [118-119]和基于图像的3D重建技术(例如，运动和多视图立体结构，SFM-MVS) [120-122]。利用这些技术，室内和田间的表型平台已经搭建，可以高通量地获取地上部的株形结构[104, 107, 123-124]。在此基础上，机器学习和计算机视觉技术很大程度上实现了对原始的三维数据和二维数据的处理，提取器官尺度的结构信息[62, 109, 125-126]。在根系方面，基于核磁共振成像和micro-CT可以实现对盆栽作物轴根的高通量获取[127-129]。虽然已有完整重建田间完整根系三维结构的方法[130]，但高通量获取完整的根系结构和田间根系结构仍是挑战。在生理参数估计方面，利用RGB、多光谱和高光谱图像提取相应的植被指数已经成功准确估计了叶片的氮含量、叶绿素含量还有关键光合参数Vcmax (最大羧化速率)和Jmax (最大电子传递速率)等重要生理参数[131-133]。这些表型技术的发展将很大程度上促进系统模型的参数化和进一步发展。与此同时，新一代测序技术极大地促进了功能基因组学的发展，使得数量性状位点(QTL)定位和全基因组关联分析(GWAS)成为阐明复杂性状遗传结构的有力工具[134-135]。基于此策略许多提高光合效率的基因被成功挖掘[136-138]。QTL定位和GWAS依赖于大量的植物表型信息，高通量表型技术将极大地加速高光效基因的挖掘。
4.2 基因组编辑技术和提高光合作用效率的基因
在模型指导的光合效率改良策略中，通常涉及多个基因的过表达和精确控制基因的表达量或蛋白质含量。随着基因组测序技术的飞速发展，基因编辑技术及其应用也得到快速发展，目前已有多种方法可以实现多个基因的过表达[139-142]。基于这些方法现在已经实现了光呼吸支路的建立[73, 143]、C4光合代谢在C3植物中的重现[144]等。目前在控制基因的表达量和蛋白质含量上已经发展了很多方法。例如，目前利用CRISPR-Cas9技术编辑基因的上游开放阅读框(uORF)已被用于微调植物靶蛋白的表达水平[145-146]，又如设计使用不同的启动子控制基因的表达程度[147]。启动子可以和调控基因转录的转录因子相互作用，控制基因表达的时间和程度。这些基因编辑技术的发展将极大促进模型指导的改造方案的实施。
目前已经挖掘出了多条高光效改造策略，其中包括过表达Rubisco和RCA (Rubisco activase)、过表达SBPase和FBPase以增加RuBP合成速度、提高光合电子传递速度、提高气孔导度、提高叶肉导度等的关键基因。这些关键基因为基于模型设计最佳改造策略提供了可用基因选择。在高光效改造中，相同的基因或改造策略在不同植物中的效果不尽相同。比如过表达RCA可以显著提高黄瓜光合速率、生物量和产量[148]，但对常温下的水稻光合、生物量及产量没有影响[149]。同时过表达三个影响NPQ动态变化的关键蛋白ZEP、PsbS、VDE加速了烟草和大豆从高光到低光转换时NPQ的弛豫速度，成功提高了它们的光合作用效率与生物量[86-87]；但在拟南芥和马铃薯中，同样过表达这三个基因却没有产生对生物量及产量预期的增加[150-151]。这个方面，不同植物本底代谢状态、植物冠层特性都可能影响特定改造方案的效果。因此，利用多尺度光合系统模型对特定作物进行针对性的高光效改良方案设计可能避免此类情况发生。目前已报道提高光合速率的关键基因如表 1所示。
5 光合系统模型支撑未来植物基础研究、作物栽培及作物育种研究
目前的实践结果表明，基于光合系统模型指导光合效率改良是可行的策略。尽管现在已经建立了各个层次的光合系统模型，但每个层次的模型都需要进一步发展。比如在代谢层面需要增加代谢物对光合相关酶的负反馈机制，冠层尺度需要进一步加入光适应的机制，而且需要耦联地下部根系模型形成整体的数字植物模型。在此基础上，需要整合目前已知的基因调控知识，特别是光氮信号基因调控网络，形成包含重要环境因子和基因调控网络互作的定量系统模型，从而实现准确预测作物在不同环境条件下的生理状态和最优的基因改造策略(图 2)。此外，将气候预测模型整合入系统模型将有利于设计适应于未来环境条件的作物。
目前，高通量组学数据、深度学习和基因编辑技术的发展将在很大程度上加快光合系统模型的发展和应用。多源组学数据将促进基因调控模型和代谢模型的快速参数化，由此预测作物中代谢通路中基因或基因组合的表达变化如何影响蛋白质丰度、代谢通量、代谢物浓度和生理表型，进而设计具体的改造策略，最后再利用基因编辑技术实现。而基因编辑作物的组学和表型数据也将用于模型的验证及提升。
系统模型的发展也将为未来育种提供新范式。首先，系统模型中的核心参数可以通过建立基因组预测模型实现预测。在此基础上，可以通过系统模型设计在目标环境下集合最多优势基因组片段或者有利等位基因的虚拟生态适应型(即不同的生态区域拥有适宜的基因型)；以此可以选择更接近虚拟生态适应型的自交系和选择优势基因互补的材料进行杂交，并逐渐接近虚拟生态适应型(图 2)。
6 总结
光合系统模型通过整合基因、环境及管理等相关信息，实现对不同尺度光合特性的准确预测。随着基因组技术、基因编辑技术及表型技术的发展，系统模型与这些技术的有效融合将为未来作物高光效高产改良提供全新范式，从而极大推动作物高光效栽培及改良的发展。
收稿日期：2024-02-01；修回日期：2024-05-11
基金项目：国家重点研发计划(2020YFA-0907600)；上海市科学技术委员会基础研究支持项目(23JC1403900)；中国科学院战略性先导项目(XDB0630000)；国家自然科学基金委NSFC-联合基金-重点支持项目(U22A20464)；2024 年省级乡村振兴战略专项资金种业振兴行动项目(第一批) (粤财农[2024]83号)
*通信作者：E-mail: zhuxg@cemps.ac.cn
[参考文献]
| [1] | Myers SS, Smith MR, Guth S, et al. Climate change and global food systems: potential impacts on food security and undernutrition. Annu Rev Public Health, 2017, 38: 259-77. |
| [2] | Anderson R, Bayer PE, Edwards D. Climate change and the need for agricultural adaptation. Curr Opin Plant Biol, 2020, 56: 197-202. |
| [3] | Ray DK, Ramankutty N, Mueller ND, et al. Recent patterns of crop yield growth and stagnation. Nat Commun, 2012, 3: 1293. |
| [4] | Ray DK, Mueller ND, West PC, et al. Yield trends are insufficient to double global crop production by 2050. PLoS One, 2013, 8: e66428. |
| [5] | Ladha JK, Radanielson AM, Rutkoski JE, et al. Steady agronomic and genetic interventions are essential for sustaining productivity in intensive rice cropping. Proc Natl Acad Sci USA, 2021, 118: e2110807118. |
| [6] | Rotter RP, Tao F, Hohn JG, et al. Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J Exp Bot, 2015, 66: 3463-76. |
| [7] | Monteith JL. Climate and the efficiency of crop production in Britain. Phil Tran R Soc Lond B, 1977, 281: 277-94. |
| [8] | Zhu XG, Long SP, Ort DR. Improving photosynthetic efficiency for greater yield. Annu Rev Plant Biol, 2010, 61: 235-61. |
| [9] | Zhu XG, Long SP, Ort DR. What is the maximum efficiency with which photosynthesis can convert solar energy into biomass?. Curr Opin Biotechnol, 2008, 19: 153-9. |
| [10] | Ort DR, Merchant SS, Alric J, et al. Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc Natl Acad Sci USA, 2015, 112: 8529-36. |
| [11] | Long SP, Zhu XG, Naidu SL, et al. Can improvement in photosynthesis increase crop yields?. Plant Cell Environ, 2006, 29: 315-30. |
| [12] | Miflin B. Crop improvement in the 21st century. J Exp Bot, 2000, 51: 1-8. |
| [13] | Peng S, Laza RC, Visperas RM, et al. Grain yield of rice cultivars and lines developed in the Philippines since 1966. Crop Sci, 2000, 40: 307-14. |
| [14] | Hubbart S, Peng S, Horton P, et al. Trends in leaf photosynthesis in historical rice varieties developed in the Philippines since 1966. J Exp Bot, 2007, 58: 3429-38. |
| [15] | Sadras VO, Lawson C. Genetic gain in yield and associated changes in phenotype, trait plasticity and competitive ability of South Australian wheat varieties released between 1958 and 2007. Crop Pasture Sci, 2011, 62: 533-49. |
| [16] | Liu W, Hou P, Liu G, et al. Contribution of total dry matter and harvest index to maize grain yield—a multisource data analysis. Food Energy Secur, 2020, 9: e256. |
| [17] | Ainsworth EA, Long SP. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol, 2005, 165: 351-71. |
| [18] | Ainsworth EA, Long SP. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation?. Global Change Biol, 2021, 27: 27-49. |
| [19] | Zeng D, Tian Z, Rao Y, et al. Rational design of high-yield and superior-quality rice. Nat Plants, 2017, 3: 17031. |
| [20] | Qian Q, Guo L, Smith SM, et al. Breeding high-yield superior quality hybrid super rice by rational design. Natl Sci Rev, 2016, 3: 283-94. |
| [21] | Stockle CO, Kemanian AR. Can crop models identify critical gaps in genetics, environment, and management interactions?. Front Plant Sci, 2020, 11: 737. |
| [22] | Hatfield JL, Walthall CL. Meeting global food needs: realizing the potential via genetics × environment × management interactions. Agron J, 2015, 107: 1215-26. |
| [23] | Chang TG, Zhu XG, Raines C. Source-sink interaction: a century old concept under the light of modern molecular systems biology. J Exp Bot, 2017, 68: 4417-31. |
| [24] | Zhang M, Wang Y, Chen X, et al. Plasma membrane H+-ATPase overexpression increases rice yield via simultaneous enhancement of nutrient uptake and photosynthesis. Nat Commun, 2021, 12: 735. |
| [25] | Caine RS, Yin X, Sloan J, et al. Rice with reduced stomatal density conserves water and has improved drought tolerance under future climate conditions. New Phytol, 2019, 221: 371-84. |
| [26] | Srinivasan V, Kumar P, Long SP. Decreasing, not increasing, leaf area will raise crop yields under global atmospheric change. Global Change Biol, 2017, 23: 1626-35. |
| [27] | Song Q, Wang Y, Qu M, et al. The impact of modifying photosystem antenna size on canopy photosynthetic efficiency-Development of a new canopy photosynthesis model scaling from metabolism to canopy level processes. Plant Cell Environ, 2017, 40: 2946-57. |
| [28] | Cho YB, Boyd RA, Ren Y, et al. Reducing chlorophyll levels in seed-filling stages results in higher seed nitrogen without impacting canopy carbon assimilation. Plant Cell Environ, 2024, 47: 278-93. |
| [29] | Mao L, Song Q, Li M, et al. Decreasing photosystem antenna size by inhibiting chlorophyll synthesis: double-edged sword for photosynthetic efficiency. Crop Environ, 2023, 2: 46-58. |
| [30] | Long SP, Marshall-Colon A, Zhu XG. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell, 2015, 161: 56-66. |
| [31] | Chang TG, Chang S, Song QF, et al. Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops. In Silico Plants, 2019, 1: diy003. |
| [32] | Yin X, Gu J, Dingkuhn M, et al. A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement. J Exp Bot, 2022, 73: 3173-88. |
| [33] | Zhu XG, Lynch JP, LeBauer DS, et al. Plants in silico: why, why now and what?—An integrative platform for plant systems biology research. Plant Cell Environ, 2016, 39: 1049-57. |
| [34] | Postma JA, Kuppe C, Owen MR, et al. OpenSimRoot: widening the scope and application of root architectural models. New Phytol, 2017, 215: 1274-86. |
| [35] | Chew YH, Wenden B, Flis A, et al. Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proc Natl Acad Sci USA, 2014, 111: E4127-36. |
| [36] | Lynch JP. Harnessing root architecture to address global challenges. Plant J, 2022, 109: 415-31. |
| [37] | Wang JP, Naik PP, Chen HC, et al. Complete proteomic-based enzyme reaction and inhibition kinetics reveal how monolignol biosynthetic enzyme families affect metabolic flux and lignin in Populus trichocarpa. Plant Cell, 2014, 26: 894-914. |
| [38] | Wang JP, Matthews ML, Williams CM, et al. Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis. Nat Commun, 2018, 9: 1579. |
| [39] | Sulis DB, Jiang X, Yang C, et al. Multiplex CRISPR editing of wood for sustainable fiber production. Science, 2023, 381: 216-21. |
| [40] | Salazar JD, Saithong T, Brown PE, et al. Prediction of photoperiodic regulators from quantitative gene circuit models. Cell, 2009, 139: 1170-9. |
| [41] | Song YH, Smith RW, To BJ, et al. FKF1 conveys timing information for CONSTANS stabilization in photoperiodic flowering. Science, 2012, 336: 1045-9. |
| [42] | Azpeitia E, Tichtinsky G, Le Masson M, et al. Cauliflower fractal forms arise from perturbations of floral gene networks. Science, 2021, 373: 192-7. |
| [43] | Heckmann D, Schulze S, Denton A, et al. Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape. Cell, 2013, 153: 1579-88. |
| [44] | Nieto C, Catalán P, Luengo LM, et al. COP1 dynamics integrate conflicting seasonal light and thermal cues in the control of Arabidopsis elongation. Sci Adv, 2022, 8: eabp8412. |
| [45] | Wu A, Hammer GL, Doherty A, et al. Quantifying impacts of enhancing photosynthesis on crop yield. Nat Plants, 2019, 5: 380-8. |
| [46] | Zheng B, Biddulph B, Li D, et al. Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments. J Exp Bot, 2013, 64: 3747-61. |
| [47] | Xiao Y, Tholen D, Zhu XG. The influence of leaf anatomy on the internal light environment and photosynthetic electron transport rate: exploration with a new leaf ray tracing model. J Exp Bot, 2016, 67: 6021-35. |
| [48] | Kannan K, Wang Y, Lang M, et al. Combining gene network, metabolic and leaf-level models shows means to future-proof soybean photosynthesis under rising CO2. In Silico Plants, 2019, 1: diz008. |
| [49] | Zhu XG, Wang Y, Ort DR, et al. e-Photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis. Plant Cell Environ, 2013, 36: 1711-27. |
| [50] | Wang Y, Long SP, Zhu XG. Elements required for an efficient NADP-malic enzyme type C4 photosynthesis. Plant Physiol, 2014, 164: 2231-46. |
| [51] | Farquhar GD, von Caemmerer Sv, Berry JA. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 1980, 149: 78-90. |
| [52] | De Pury D, Farquhar G. Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models. Plant Cell Environ, 1997, 20: 537-57. |
| [53] | Sellers P, Randall D, Collatz G, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part Ⅰ: model formulation. J Clim, 1996, 9: 676-705. |
| [54] | Song Q, Zhang G, Zhu XG. Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2 – a theoretical study using a mechanistic model of canopy photosynthesis. Funct Plant Biol, 2013, 40: 109-24. |
| [55] | Zheng B, Shi L, Ma Y, et al. Comparison of architecture among different cultivars of hybrid rice using a spatial light model based on 3-D digitising. Funct Plant Biol, 2008, 35: 900-10. |
| [56] | Retta MA, Yin X, Ho QT, et al. The role of chloroplast movement in C4 photosynthesis: a theoretical analysis using a three-dimensional reaction-diffusion model for maize. J Exp Bot, 2023, 74: 4125-42. |
| [57] | Chang TG, Wei ZW, Shi Z, et al. Bridging photosynthesis and crop yield formation with a mechanistic model of whole-plant carbon–nitrogen interaction. In Silico Plants, 2023, 5: diad011. |
| [58] | Hui J, An X, Li Z, et al. The mycorrhiza-specific ammonium transporter ZmAMT3;1 mediates mycorrhiza-dependent nitrogen uptake in maize roots. Plant Cell, 2022, 34: 4066-87. |
| [59] | Zhu XG, de Sturler E, Long SP. Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm. Plant Physiol, 2007, 145: 513-26. |
| [60] | Zhao HL, Chang TG, Xiao Y, et al. Potential metabolic mechanisms for inhibited chloroplast nitrogen assimilation under high CO2. Plant Physiol, 2021, 187: 1812-33. |
| [61] | Xiao Y, Sloan J, Hepworth C, et al. Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach. New Phytol, 2023, 237: 441-53. |
| [62] | Liu F, Song Q, Zhao J, et al. Canopy occupation volume as an indicator of canopy photosynthetic capacity. New Phytol, 2021, 232: 941-56. |
| [63] | Simkin AJ, McAusland L, Headland LR, et al. Multigene manipulation of photosynthetic carbon assimilation increases CO2 fixation and biomass yield in tobacco. J Exp Bot, 2015, 66: 4075-90. |
| [64] | Rosenthal DM, Locke AM, Khozaei M, et al. Over-expressing the C3 photosynthesis cycle enzyme sedoheptulose-1-7 bisphosphatase improves photosynthetic carbon gain and yield under fully open air CO2 fumigation (FACE). BMC Plant Biol, 2011, 11: 123. |
| [65] | Savir Y, Noor E, Milo R, et al. Cross-species analysis traces adaptation of Rubisco toward optimality in a low-dimensional landscape. Proc Natl Acad Sci USA, 2010, 107: 3475-80. |
| [66] | Prywes N, Phillips NR, Tuck OT, et al. Rubisco function, evolution, and engineering. Annu Rev Biochem, 2023, 92: 385-410. |
| [67] | Zhu XG, Portis AR, Long SP. Would transformation of C3 crop plants with foreign Rubisco increase productivity? A computational analysis extrapolating from kinetic properties to canopy photosynthesis. Plant Cell Environ, 2004, 27: 155-65. |
| [68] | Ishikawa C, Hatanaka T, Misoo S, et al. Functional incorporation of sorghum small subunit increases the catalytic turnover rate of Rubisco in transgenic rice. Plant Physiol, 2011, 156: 1603-11. |
| [69] | Morita K, Hatanaka T, Misoo S, et al. Unusual small subunit that is not expressed in photosynthetic cells alters the catalytic properties of Rubisco in rice. Plant Physiol, 2014, 164: 69-79. |
| [70] | Matsumura H, Shiomi K, Yamamoto A, et al. Hybrid Rubisco with complete replacement of rice Rubisco small subunits by sorghum counterparts confers C4 plant-like high catalytic activity. Mol Plant, 2020, 13: 1570-81. |
| [71] | Maurino VG, Peterhansel C. Photorespiration: current status and approaches for metabolic engineering. Curr Opin Plant Biol, 2010, 13: 249-56. |
| [72] | Xin CP, Tholen D, Devloo V, et al. The benefits of photorespiratory bypasses: how can they work?. Plant Physiol, 2015, 167: 574-85. |
| [73] | South PF, Cavanagh AP, Liu HW, et al. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science, 2019, 363: eaat9077. |
| [74] | Sage RF, Sage TL, Kocacinar F. Photorespiration and the evolution of C4 photosynthesis. Annu Rev Plant Biol, 2012, 63: 19-47. |
| [75] | Yin X, Struik PC. Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS. J Exp Bot, 2017, 68: 2345-60. |
| [76] | von Caemmerer S, Quick WP, Furbank RT. The development of C4 rice: current progress and future challenges. Science, 2012, 336: 1671-2. |
| [77] | Tholen D, Zhu XG. The mechanistic basis of internal conductance: a theoretical analysis of mesophyll cell photosynthesis and CO2 diffusion. Plant Physiol, 2011, 156: 90-105. |
| [78] | Chen JH, Chen ST, He NY, et al. Nuclear-encoded synthesis of the D1 subunit of photosystem Ⅱ increases photosynthetic efficiency and crop yield. Nat Plants, 2020, 6: 570-80. |
| [79] | Sloan JM, Mujab AAM, Mashitah J, et al. Elevated CO2 priming as a sustainable approach to increasing rice tiller number and yield potential. Rice (N Y), 2023, 16: 16. |
| [80] | Krieger-Liszkay A. Singlet oxygen production in photosynthesis. J Exp Bot, 2005, 56: 337-46. |
| [81] | Melis A. Photosystem-Ⅱ damage and repair cycle in chloroplasts: what modulates the rate of photodamage in vivo?. Trends Plant Sci, 1999, 4: 130-5. |
| [82] | Niyogi KK. Photoprotection revisited: genetic and molecular approaches. Annu Rev Plant Biol, 1999, 50: 333-59. |
| [83] | Ruban AV. Nonphotochemical chlorophyll fluorescence quenching: mechanism and effectiveness in protecting plants from photodamage. Plant Physiol, 2016, 170: 1903-16. |
| [84] | Long SP, Taylor SH, Burgess SJ, et al. Into the shadows and back into sunlight: photosynthesis in fluctuating light. Annu Rev Plant Biol, 2022, 73: 617-48. |
| [85] | Zhu XG, Ort DR, Whitmarsh J, et al. The slow reversibility of photosystem Ⅱ thermal energy dissipation on transfer from high to low light may cause large losses in carbon gain by crop canopies: a theoretical analysis. J Exp Bot, 2004, 55: 1167-75. |
| [86] | Kromdijk J, Głowacka K, Leonelli L, et al. Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science, 2016, 354: 857-61. |
| [87] | De Souza AP, Burgess SJ, Doran L, et al. Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection. Science, 2022, 377: 851-4. |
| [88] | Lawson T, Matthews J. Guard cell metabolism and stomatal function. Annu Rev Plant Biol, 2020, 71: 273-302. |
| [89] | Morales A, Kaiser E, Yin X, et al. Dynamic modelling of limitations on improving leaf CO2 assimilation under fluctuating irradiance. Plant Cell Environ, 2018, 41: 589-604. |
| [90] | De Souza AP, Wang Y, Orr DJ, et al. Photosynthesis across African cassava germplasm is limited by Rubisco and mesophyll conductance at steady state, but by stomatal conductance in fluctuating light. New Phytol, 2020, 225: 2498-512. |
| [91] | Blatt MR. Cellular signaling and volume control in stomatal movements in plants. Annu Rev Cell Dev Biol, 2000, 16: 221-41. |
| [92] | Wang Y, Hills A, Vialet-Chabrand S, et al. Unexpected connections between humidity and ion transport discovered using a model to bridge guard cell-to-leaf scales. Plant Cell, 2017, 29: 2921-39. |
| [93] | Jezek M, Silva-Alvim FAL, Hills A, et al. Guard cell endomembrane Ca2+-ATPases underpin a 'carbon memory' of photosynthetic assimilation that impacts on water-use efficiency. Nat Plants, 2021, 7: 1301-13. |
| [94] | Vialet-Chabrand S, Hills A, Wang Y, et al. Global sensitivity analysis of on guard models identifies key hubs for transport interaction in stomatal dynamics. Plant Physiol, 2017, 174: 680-8. |
| [95] | Wang Y, Hills A, Blatt MR. Systems analysis of guard cell membrane transport for enhanced stomatal dynamics and water use efficiency. Plant Physiol, 2014, 164: 1593-9. |
| [96] | Lawson T, Blatt MR. Stomatal size, speed, and responsiveness impact on photosynthesis and water use efficiency. Plant Physiol, 2014, 164: 1556-70. |
| [97] | Papanatsiou M, Petersen J, Henderson L, et al. Optogenetic manipulation of stomatal kinetics improves carbon assimilation, water use, and growth. Science, 2019, 363: 1456-9. |
| [98] | Duncan W, Loomis R, Williams W, et al. A model for simulating photosynthesis in plant communities. Hilgardia, 1967, 38: 181-205. |
| [99] | Anderson MC, Denmead O. Short wave radiation on inclined surfaces in model plant communities 1. Agron J, 1969, 61: 867-72. |
| [100] | Mantilla-Perez MB, Salas Fernandez MG. Differential manipulation of leaf angle throughout the canopy: current status and prospects. J Exp Bot, 2017, 68: 5699-717. |
| [101] | Zheng BY, Ma YT, Li BG, et al. Assessment of the influence of global dimming on the photosynthetic production of rice based on three-dimensional modeling. Sci China Earth Sci, 2011, 54: 290-7. |
| [102] | Chang TG, Zhao H, Wang N, et al. A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties. J Exp Bot, 2019, 70: 2479-90. |
| [103] | Song Q, Chu C, Parry MA, et al. Genetics-based dynamic systems model of canopy photosynthesis: the key to improve light and resource use efficiencies for crops. Food Energy Secur, 2016, 5: 18-25. |
| [104] | Song Q, Liu F, Bu H, et al. Quantifying contributions of different factors to canopy photosynthesis in 2 maize varieties: development of a novel 3d canopy modeling pipeline. Plant Phenomics, 2023, 5: 0075. |
| [105] | Song Q, Srinivasan V, Long SP, et al. Decomposition analysis on soybean productivity increase under elevated CO2 using 3-D canopy model reveals synergestic effects of CO2 and light in photosynthesis. Ann Bot, 2020, 126: 601-14. |
| [106] | Shi Z, Chang TG, Chen G, et al. Dissection of mechanisms for high yield in two elite rice cultivars. Field Crops Res, 2019, 241: 107563. |
| [107] | Liu F, Hu P, Zheng B, et al. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agric For Meteorol, 2021, 296: 108231. |
| [108] | Wang Y, Song Q, Jaiswal D, et al. Development of a three-dimensional ray-tracing model of sugarcane canopy photosynthesis and its application in assessing impacts of varied row spacing. Bioenergy Res, 2017, 10: 626-34. |
| [109] | Zhu B, Liu F, Xie Z, et al. Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season. Ann Bot, 2020, 126: 701-12. |
| [110] | de Visser PH, Buck-Sorlin GH, van der Heijden GW. Optimizing illumination in the greenhouse using a 3D model of tomato and a ray tracer. Front Plant Sci, 2014, 5: 48. |
| [111] | Dieleman JA, De Visser PHB, Meinen E, et al. Integrating morphological and physiological responses of tomato plants to light quality to the crop level by 3D modeling. Front Plant Sci, 2019, 10: 839. |
| [112] | Sinoquet H, Moulia B, Bonhomme R. Estimating the three-dimensional geometry of a maize crop as an input of radiation models: comparison between three-dimensional digitizing and plant profiles. Agric For Meteorol, 1991, 55: 233-49. |
| [113] | Wang X, Guo Y, Li B, et al. Evaluating a three dimensional model of diffuse photosynthetically active radiation in maize canopies. Int J Biometeorol, 2006, 50: 349-57. |
| [114] | Chambelland JC, Dassot M, Adam B, et al. A double-digitising method for building 3D virtual trees with non-planar leaves: application to the morphology and light-capture properties of young beech trees (Fagus sylvatica). Funct Plant Biol, 2008, 35: 1059-69. |
| [115] | Paulus S, Behmann J, Mahlein AK, et al. Low-cost 3D systems: suitable tools for plant phenotyping. Sensors, 2014, 14: 3001-18. |
| [116] | Jiang Y, Li C, Paterson AH. High throughput phenotyping of cotton plant height using depth images under field conditions. Comput Electron Agric, 2016, 130: 57-68. |
| [117] | McCormick RF, Truong SK, Mullet JE. 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol, 2016, 172: 823-34. |
| [118] | Perez RPA, Costes E, Théveny F, et al. 3D plant model assessed by terrestrial LiDAR and hemispherical photographs: a useful tool for comparing light interception among oil palm progenies. Agric For Meteorol, 2018, 249: 250-63. |
| [119] | Sun S, Li C, Paterson AH, et al. In-field high throughput phenotyping and cotton plant growth analysis using LiDAR. Front Plant Sci, 2018, 9: 16. |
| [120] | Paproki A, Sirault X, Berry S, et al. A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol, 2012, 12: 63. |
| [121] | Pound MP, French AP, Murchie EH, et al. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol, 2014, 166: 1688-98. |
| [122] | Duan T, Chapman SC, Holland E, et al. Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes. J Exp Bot, 2016, 67: 4523-34. |
| [123] | Malambo L, Popescu SC, Horne DW, et al. Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data. ISPRS J Photogramm Remote Sens, 2019, 149: 1-13. |
| [124] | Xiao S, Ye Y, Fei S, et al. High-throughput calculation of organ-scale traits with reconstructed accurate 3D canopy structures using a UAV RGB camera with an advanced cross-circling oblique route. ISPRS J Photogramm Remote Sens, 2023, 201: 104-22. |
| [125] | Jiang N, Floro E, Bray AL, et al. Three-dimensional time-lapse analysis reveals multiscale relationships in maize root systems with contrasting architectures. Plant Cell, 2019, 31: 1708-22. |
| [126] | Huther P, Schandry N, Jandrasits K, et al. ARADEEPOPSIS, an automated workflow for top-view plant phenomics using semantic segmentation of leaf states. Plant Cell, 2020, 32: 3674-88. |
| [127] | van Dusschoten D, Metzner R, Kochs J, et al. Quantitative 3D analysis of plant roots growing in soil using magnetic resonance imaging. Plant Physiol, 2016, 170: 1176-88. |
| [128] | Brackin R, Atkinson BS, Sturrock CJ, et al. Roots-eye view: using microdialysis and microCT to non-destructively map root nutrient depletion and accumulation zones. Plant Cell Environ, 2017, 40: 3135-42. |
| [129] | Lynch JP. Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytol, 2019, 223: 548-64. |
| [130] | Wu Q, Wu J, Zheng B, et al. Optimizing soil-coring strategies to quantify root-length-density distribution in field-grown maize: virtual coring trials using 3-D root architecture models. Ann Bot, 2018, 121: 809-19. |
| [131] | Zhang N, Su X, Zhang X, et al. Monitoring daily variation of leaf layer photosynthesis in rice using UAV-based multi-spectral imagery and a light response curve model. Agric For Meteorol, 2020, 291: 108098. |
| [132] | Yendrek CR, Tomaz T, Montes CM, et al. High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiol, 2017, 173: 614-26. |
| [133] | Rorie RL, Purcell LC, Mozaffari M, et al. Association of "Greenness" in corn with yield and leaf nitrogen concentration. Agron J, 2011, 103: 529-35. |
| [134] | Zhang H, Wang X, Pan Q, et al. QTG-seq accelerates QTL fine mapping through QTL partitioning and whole-genome sequencing of bulked segregant samples. Mol Plant, 2019, 12: 426-37. |
| [135] | Xiao Y, Liu H, Wu L, et al. Genome-wide association studies in maize: praise and stargaze. Mol Plant, 2017, 10: 359-74. |
| [136] | Qu M, Essemine J, Li M, et al. Genome-wide association study unravels LRK1 as a dark respiration regulator in rice (Oryza sativa L.). Int J Mol Sci, 2020, 21: 4930. |
| [137] | Qu M, Essemine J, Xu J, et al. Alterations in stomatal response to fluctuating light increase biomass and yield of rice under drought conditions. Plant J, 2020, 104: 1334-47. |
| [138] | Wang Q, Xie W, Xing H, et al. Genetic architecture of natural variation in rice chlorophyll content revealed by a genome-wide association study. Mol Plant, 2015, 8: 946-57. |
| [139] | Zhu Q, Yu S, Zeng D, et al. Development of "purple endosperm rice" by engineering anthocyanin biosynthesis in the endosperm with a high-efficiency transgene stacking system. Mol Plant, 2017, 10: 918-29. |
| [140] | Engler C, Youles M, Gruetzner R, et al. A golden gate modular cloning toolbox for plants. ACS Synth Biol, 2014, 3: 839-43. |
| [141] | Bruckner K, Schafer P, Weber E, et al. A library of synthetic transcription activator-like effector-activated promoters for coordinated orthogonal gene expression in plants. Plant J, 2015, 82: 707-16. |
| [142] | Zhu Q, Zeng D, Yu S, et al. From golden rice to aSTARice: bioengineering astaxanthin biosynthesis in rice endosperm. Mol Plant, 2018, 11: 1440-8. |
| [143] | Shen BR, Wang LM, Lin XL, et al. Engineering a new chloroplastic photorespiratory bypass to increase photosynthetic efficiency and productivity in rice. Mol Plant, 2019, 12: 199-214. |
| [144] | Ermakova M, Arrivault S, Giuliani R, et al. Installation of C4 photosynthetic pathway enzymes in rice using a single construct. Plant Biotechnol J, 2021, 19: 575-88. |
| [145] | Xing S, Chen K, Zhu H, et al. Fine-tuning sugar content in strawberry. Genome Biol, 2020, 21: 230. |
| [146] | Zhang H, Si X, Ji X, et al. Genome editing of upstream open reading frames enables translational control in plants. Nat Biotechnol, 2018, 36: 894-8. |
| [147] | Cai YM, Kallam K, Tidd H, et al. Rational design of minimal synthetic promoters for plants. Nucleic Acids Res, 2020, 48: 11845-56. |
| [148] | Bi H, Liu P, Jiang Z, et al. Overexpression of the Rubisco activase gene improves growth and low temperature and weak light tolerance in Cucumis sativus. Physiol Plant, 2017, 161: 224-34. |
| [149] | Suganami M, Suzuki Y, Tazoe Y, et al. Co-overproducing Rubisco and Rubisco activase enhances photosynthesis in the optimal temperature range in rice. Plant Physiol, 2021, 185: 108-19. |
| [150] | Garcia-Molina A, Leister D. Accelerated relaxation of photoprotection impairs biomass accumulation in Arabidopsis. Nat Plants, 2020, 6: 9-12. |
| [151] | Lehretz GG, Schneider A, Leister D, et al. High non-photochemical quenching of VPZ transgenic potato plants limits CO2 assimilation under high light conditions and reduces tuber yield under fluctuating light. J Integr Plant Biol, 2022, 64: 1821-32. |
| [152] | Salesse-Smith CE, Sharwood RE, Busch FA, et al. Overexpression of Rubisco subunits with RAF1 increases Rubisco content in maize. Nat Plants, 2018, 4: 802-10. |
| [153] | Yoon DK, Ishiyama K, Suganami M, et al. Transgenic rice overproducing Rubisco exhibits increased yields with improved nitrogen-use efficiency in an experimental paddy field. Nat Food, 2020, 1: 134-9. |
| [154] | Lefebvre S, Lawson T, Zakhleniuk OV, et al. Increased sedoheptulose-1, 7-bisphosphatase activity in transgenic tobacco plants stimulates photosynthesis and growth from an early stage in development. Plant Physiol, 2005, 138: 451-60. |
| [155] | Driever SM, Simkin AJ, Alotaibi S, et al. Increased SBPase activity improves photosynthesis and grain yield in wheat grown in greenhouse conditions. Philos Trans R Soc Lond B Biol Sci, 2017, 372: 20160384. |
| [156] | Ding F, Wang M, Zhang S, et al. Changes in SBPase activity influence photosynthetic capacity, growth, and tolerance to chilling stress in transgenic tomato plants. Sci Rep, 2016, 6: 32741. |
| [157] | Cai B, Ning Y, Li Q, et al. Effects of the chloroplast fructose-1, 6-bisphosphate aldolase gene on growth and low-temperature tolerance of tomato. Int J Mol Sci, 2022, 23: 728. |
| [158] | Miyagawa Y, Tamoi M, Shigeoka S. Overexpression of a cyanobacterial fructose-1, 6-/sedoheptulose-1, 7-bisphosphatase in tobacco enhances photosynthesis and growth. Nat Biotechnol, 2001, 19: 965-9. |
| [159] | Ichikawa Y, Tamoi M, Sakuyama H, et al. Generation of transplastomic lettuce with enhanced growth and high yield. GM Crops, 2010, 1: 322-6. |
| [160] | Li J, Yokosho K, Liu S, et al. Diel magnesium fluctuations in chloroplasts contribute to photosynthesis in rice. Nat Plants, 2020, 6: 848-59. |
| [161] | Yang SM, Chang CY, Yanagisawa M, et al. Transgenic rice expressing cyanobacterial bicarbonate transporter exhibited enhanced photosynthesis, growth and grain yield. Photosynthesis[C]. Energy from the Sun: 14th International Congress on Photosynthesis, 2008: 1243-6 |
| [162] | Hay WT, Bihmidine S, Mutlu N, et al. Enhancing soybean photosynthetic CO2 assimilation using a cyanobacterial membrane protein, ictB. J Plant Physiol, 2017, 212: 58-68. |
| [163] | Koester RP, Pignon CP, Kesler DC, et al. Transgenic insertion of the cyanobacterial membrane protein ictB increases grain yield in Zea mays through increased photosynthesis and carbohydrate production. PLoS One, 2021, 16: e0246359. |
| [164] | Nolke G, Barsoum M, Houdelet M, et al. The integration of algal carbon concentration mechanism components into tobacco chloroplasts increases photosynthetic efficiency and biomass. Biotechnol J, 2019, 14: e1800170. |
| [165] | Yadav SK, Khatri K, Rathore MS, et al. Introgression of UfCyt c6, a thylakoid lumen protein from a green seaweed Ulva fasciata Delile enhanced photosynthesis and growth in tobacco. Mol Biol Rep, 2018, 45: 1745-58. |
| [166] | Simkin AJ, McAusland L, Lawson T, et al. Overexpression of the Rieske FeS protein increases electron transport rates and biomass yield. Plant Physiol, 2017, 175: 134-45. |
| [167] | Ermakova M, Lopez-Calcagno PE, Raines CA, et al. Overexpression of the Rieske FeS protein of the cytochrome b6f complex increases C4 photosynthesis in Setaria viridis. Commun Biol, 2019, 2: 314. |
| [168] | Ermakova M, Woodford R, Taylor Z, et al. Faster induction of photosynthesis increases biomass and grain yield in glasshouse-grown transgenic Sorghum bicolor overexpressing Rieske FeS. Plant Biotechnol J, 2023, 21: 1206-16. |
| [169] | Yadav SK, Khatri K, Rathore MS, et al. Ectopic expression of a transmembrane protein KaCyt b6 from a red seaweed Kappaphycus alvarezii in transgenic tobacco augmented the photosynthesis and growth. DNA Cell Biol, 2020. DOI:10.1089/dna.2020.5479 |
| [170] | Ermakova M, Heyno E, Woodford R, et al. Enhanced abundance and activity of the chloroplast ATP synthase in rice through the overexpression of the AtpD subunit. J Exp Bot, 2022, 73: 6891-901. |
| [171] | An G, Qi Y, Zhang W, et al. LsNRL4 enhances photosynthesis and decreases leaf angles in lettuce. Plant Biotechnol J, 2022, 20: 1956-67. |
| [172] | Peng M, Gan F, Lin X, et al. Overexpression of OsNF-YB4 leads to flowering early, improving photosynthesis and better grain yield in hybrid rice. Plant Sci, 2023, 331: 111661. |
| [173] | Flugel F, Timm S, Arrivault S, et al. The photorespiratory metabolite 2-phosphoglycolate regulates photosynthesis and starch accumulation in Arabidopsis. Plant Cell, 2017, 29: 2537-51. |
| [174] | Timm S, Florian A, Arrivault S, et al. Glycine decarboxylase controls photosynthesis and plant growth. FEBS Lett, 2012, 586: 3692-7. |
| [175] | Timm S, Wittmiss M, Gamlien S, et al. Mitochondrial dihydrolipoyl dehydrogenase activity shapes photosynthesis and photorespiration of Arabidopsis thaliana. Plant Cell, 2015, 27: 1968-84. |
| [176] | Lopez-Calcagno PE, Fisk S, Brown KL, et al. Overexpressing the H-protein of the glycine cleavage system increases biomass yield in glasshouse and field-grown transgenic tobacco plants. Plant Biotechnol J, 2019, 17: 141-51. |
| [177] | Kebeish R, Niessen M, Thiruveedhi K, et al. Chloroplastic photorespiratory bypass increases photosynthesis and biomass production in Arabidopsis thaliana. Nat Biotechnol, 2007, 25: 593-9. |
| [178] | Maier A, Fahnenstich H, von Caemmerer S, et al. Transgenic introduction of a glycolate oxidative cycle into A. thaliana chloroplasts leads to growth improvement. Front Plant Sci, 2012, 3: 38. |
| [179] | Nolke G, Houdelet M, Kreuzaler F, et al. The expression of a recombinant glycolate dehydrogenase polyprotein in potato (Solanum tuberosum) plastids strongly enhances photosynthesis and tuber yield. Plant Biotechnol J, 2014, 12: 734-42. |
| [180] | Dalal J, Lopez H, Vasani NB, et al. A photorespiratory bypass increases plant growth and seed yield in biofuel crop Camelina sativa. Biotechnol Biofuels, 2015, 8: 175. |
| [181] | Wang LM, Shen BR, Li BD, et al. A synthetic photorespiratory shortcut enhances photosynthesis to boost biomass and grain yield in rice. Mol Plant, 2020, 13: 1802-15. |
| [182] | Lopez-Calcagno PE, Brown KL, Simkin AJ, et al. Stimulating photosynthetic processes increases productivity and water-use efficiency in the field. Nat Plants, 2020, 6: 1054-63. |
| [183] | Simkin AJ, Lopez-Calcagno PE, Davey PA, et al. Simultaneous stimulation of sedoheptulose 1, 7-bisphosphatase, fructose 1, 6-bisphophate aldolase and the photorespiratory glycine decarboxylase-H protein increases CO2 assimilation, vegetative biomass and seed yield in Arabidopsis. Plant Biotechnol J, 2017, 15: 805-16. |
| [184] | Ambavaram MM, Basu S, Krishnan A, et al. Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stress. Nat Commun, 2014, 5: 5302. |
| [185] | Perveen S, Qu M, Chen F, et al. Overexpression of maize transcription factor mEmBP-1 increases photosynthesis, biomass, and yield in rice. J Exp Bot, 2020, 71: 4944-57. |
| [186] | Wei S, Li X, Lu Z, et al. A transcriptional regulator that boosts grain yields and shortens the growth duration of rice. Science, 2022, 377: eabi8455. |
| [187] | Li X, Wang P, Li J, et al. Maize GOLDEN2-LIKE genes enhance biomass and grain yields in rice by improving photosynthesis and reducing photoinhibition. Commun Biol, 2020, 3: 151. |
| [188] | Xu F, Wang K, Yuan W, et al. Overexpression of rice aquaporin OsPIP1;2 improves yield by enhancing mesophyll CO2 conductance and phloem sucrose transport. J Exp Bot, 2019, 70: 671-81. |
| [189] | Tanaka Y, Sugano SS, Shimada T, et al. Enhancement of leaf photosynthetic capacity through increased stomatal density in Arabidopsis. New Phytol, 2013, 198: 757-64. |
| [190] | Zheng S, Ye C, Lu J, et al. Improving the rice photosynthetic efficiency and yield by editing OsHXK1 via CRISPR/Cas9 system. Int J Mol Sci, 2021, 22: 9554. |
| [191] | Kimura H, Hashimoto-Sugimoto M, Iba K, et al. Improved stomatal opening enhances photosynthetic rate and biomass production in fluctuating light. J Exp Bot, 2020, 71: 2339-50. |
| [192] | Kusumi K, Hirotsuka S, Kumamaru T, et al. Increased leaf photosynthesis caused by elevated stomatal conductance in a rice mutant deficient in SLAC1, a guard cell anion channel protein. J Exp Bot, 2012, 63: 5635-44. |
| [193] | Yamori W, Kusumi K, Iba K, et al. Increased stomatal conductance induces rapid changes to photosynthetic rate in response to naturally fluctuating light conditions in rice. Plant Cell Environ, 2020, 43: 1230-40. |
| [194] | Wang Y, Noguchi K, Ono N, et al. Overexpression of plasma membrane H+-ATPase in guard cells promotes light-induced stomatal opening and enhances plant growth. Proc Natl Acad Sci U S A, 2014, 111: 533-8. |
| [195] | Medeiros DB, Martins SC, Cavalcanti JH, et al. Enhanced photosynthesis and growth in atquac1 knockout mutants are due to altered organic acid accumulation and an increase in both stomatal and mesophyll conductance. Plant Physiol, 2016, 170: 86-101. |
朱新广，博士，中国科学院分子植物科学卓越创新中心研究员，中国科学院光合作用与环境生物学开放实验室主任；曾获中国科学院国际合作奖(2013)、国家高层次人才特殊支持计划(2021)等；由于发现提高光合效率的新途径，2013年被国际光合作用协会授予“Melvin Calvin - Andrew Benson Award”。Frontiers in Plant Sciences -- Photosynthesis and Photobiology (Chief Editor)，同国际同行共同创立in silico Plants杂志，是F1000Prime的faculty，编著《光合作用研究技术》。迄今发表文章190多篇，被引用21 000次以上，H index 61，进入Elsevier 2020—2023年中国最高引学者名单。
《生命科学》是由中国科学院上海营养与健康研究所主办，国家自然科学基金委员会生命科学部和中国科学院生命科学和医学学部共同指导的综合性学术期刊。1988年创刊，原刊名为《生物学信息》内部发行；1992年起更名为《生命科学》，公开发行CN31-1600/Q，大16开，96页。本刊是“中文核心期刊” “中国科技核心期刊” “中国科学引文数据库来源期刊(CSCD)”。
