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A 3D Model of Photosynthesis to Inform Breeding for Improved Rice Performance in a Changing Climate

光合作用 3D 模型为育种提供信息，以提高气候变化下的水稻性能

基本信息

批准号：
BB/N013719/1
负责人：
Andrew James Fleming
金额：
$ 72.95万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FN013719%2F1
关键词：
3D Model Photosynthesis Inform Breeding

项目摘要

Increasing demands for global food production over the next decades will be a huge burden on the world's shrinking farmland. It is estimated that an increase of agricultural productivity by as much as 100% is required, yet it is widely acknowledged that present agronomic practices are on a collision course with environmental and sustainability goals. Added to this, global climate change is altering the environment in which crops grow, making it unlikely that present day plants will perform well in the future. These problems will affect the whole world, but the most acute problems will be felt by the poorest people, most of whom rely on one crop, rice, for their food. The aim of this project is to provide plant breeders with information that will allow them to produce a more efficient rice plant and, moreover, a plant that is ready to cope with the challenges that climate change is going to throw at us.Breeding a new rice variety is a process which can take decades. Any procedure which shortens this process or more quickly identify the traits that farmers need to improve rice yield will have a significant affect on the lives of millions of people who depend on this crop. Moreover, due to the relative recent rapidity of climate change and the slowness of plant breeding, we need to start selecting new varieties of rice which will cope or even benefit from future elevated carbon dioxide levels (a driver of climate change) well in advance of those levels actually being reached. Exploiting the modern power of computational modelling provides the opportunity to do this. Photosynthesis is the prime driver of food production for all crops, including rice. We have a very good understanding of the biochemistry of photosynthesis and computational models have been produced which can simulate the process, allowing us to predict how photosynthesis changes to altered level of particular enzymes. However, these models have a major drawback. They are 1-dimensional, treating photosynthesis as a process that occurs uniformly in a cell. In reality, photosynthesis occurs in leaves which contain many thousands of cells and the position and shape of each cell influence the efficiency of photosynthesis in each cell. The overall performance of a leaf actually reflects the performance of all the cells put together. The aim of this project is to create a 3D model of photosynthesis which takes into account the position and shape of each cell in a rice leaf. This will allow us to investigate the affect of altering the number, size and packing of cells on photosynthesis on a computer, without having to actually breed the plants. This would save plant breeders immense time and money, allowing them to more rapidly generate the next generation of rice plants required to tackle the problems described at the beginning of this section.To achieve this aim we will use a combination of advanced imaging techniques to create 3-D leaf models and plant physiology and biochemistry techniques to measure leaf performance We will then use computational methods to model the entire process on a computer. We will then be able to ask questions such as: what pattern of cell division in the leaf is best for the efficiency of photosynthesis? Can we rationally design a rice leaf for improved performance? We will then use the new model to explore how photosynthesis is likely to respond to the increased levels of CO2 in the atmosphere which are likely to occur over the next century and test these predictions using plants grown under elevated CO2, both in laboratory and field conditions. We will explore the model to predict which aspects of leaf structure are important for plants to maintain or increase photosynthesis under the various conditions predicted by climate change models. This information will allow breeders to start selecting plants now so that in 20-30 years time rice plants will still be able to generate sufficient food for the world population.

未来几十年，对全球粮食生产的需求不断增加，将给世界上不断缩小的农田带来巨大负担。据估计，需要将农业生产力提高100%，但人们普遍认为，目前的农艺做法与环境和可持续性目标相冲突。除此之外，全球气候变化正在改变作物生长的环境，使得现在的植物不太可能在未来表现良好。这些问题将影响到整个世界，但最严重的问题将由最贫穷的人感受到，他们中的大多数人依赖一种作物-稻米-作为食物。该项目的目的是为植物育种者提供信息，使他们能够生产出更高效的水稻植物，而且，这种植物已经准备好科普气候变化将给我们带来的挑战。培育新的水稻品种是一个可能需要几十年的过程。任何缩短这一过程或更快地确定农民提高水稻产量所需的性状的程序都将对数百万依赖这种作物的人的生活产生重大影响。此外，由于最近气候变化相对较快，而植物育种进展缓慢，我们需要在二氧化碳水平实际达到之前，开始选择能够科普甚至受益于未来二氧化碳水平升高（气候变化的驱动因素）的新品种。利用计算建模的现代力量提供了这样做的机会。光合作用是包括水稻在内的所有作物粮食生产的主要驱动力。我们对光合作用的生物化学有很好的理解，并且已经产生了可以模拟这一过程的计算模型，使我们能够预测光合作用如何改变特定酶的水平。然而，这些模型有一个主要的缺点。它们是一维的，将光合作用视为在细胞中均匀发生的过程。实际上，光合作用发生在含有数千个细胞的叶子中，每个细胞的位置和形状影响每个细胞中光合作用的效率。叶子的整体性能实际上反映了所有细胞的性能。该项目的目的是创建一个光合作用的3D模型，该模型考虑了水稻叶片中每个细胞的位置和形状。这将使我们能够在计算机上研究改变细胞数量、大小和包装对光合作用的影响，而不必实际繁殖植物。这将为植物育种者节省大量的时间和金钱，使他们能够更快地产生下一代水稻植物，以解决本节开始时所描述的问题。为了实现这一目标，我们将使用先进的成像技术的组合来创建3-三维叶片模型和植物生理生化技术来测量叶片性能然后我们将使用计算方法来模拟整个过程在电脑上。这样我们就可以提出这样的问题：叶片中什么样的细胞分裂模式对光合作用的效率最好？我们是否可以合理地设计一个水稻叶片，以提高性能？然后，我们将使用新的模型来探索光合作用可能如何响应大气中可能发生在下一个世纪的CO2水平的增加，并使用在实验室和田间条件下生长的植物来测试这些预测。我们将探索该模型，以预测在气候变化模型预测的各种条件下，叶片结构的哪些方面对植物维持或增加光合作用是重要的。这些信息将使育种者现在就开始选择植物，以便在20-30年的时间里，水稻仍然能够为世界人口提供足够的食物。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Investigating the microstructure of plant leaves in 3D with lab-based X-ray computed tomography.

DOI：
10.1186/s13007-018-0367-7
发表时间：
2018
期刊：
Plant methods
影响因子：
5.1
作者：
Mathers AW;Hepworth C;Baillie AL;Sloan J;Jones H;Lundgren M;Fleming AJ;Mooney SJ;Sturrock CJ
通讯作者：
Sturrock CJ

MOESM3 of Investigating the microstructure of plant leaves in 3D with lab-based X-ray computed tomography

MOESM3 使用基于实验室的 X 射线计算机断层扫描研究植物叶片的 3D 微观结构

DOI：
10.6084/m9.figshare.7333133
发表时间：
2018
期刊：
影响因子：
0
作者：
Mathers A
通讯作者：
Mathers A

Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach.

DOI：
10.1111/nph.18564
发表时间：
2023-01
期刊：
NEW PHYTOLOGIST
影响因子：
9.4
作者：
Xiao, Yi;Sloan, Jen;Hepworth, Chris;Fradera-Soler, Marc;Mathers, Andrew;Thorley, Rachel;Baillie, Alice;Jones, Hannah;Chang, Tiangen;Chen, Xingyuan;Yaapar, Nazmin;Osborne, Colin P.;Sturrock, Craig;Mooney, Sacha. J. J.;Fleming, Andrew. J. J.;Zhu, Xin-Guang
通讯作者：
Zhu, Xin-Guang

MOESM1 of Investigating the microstructure of plant leaves in 3D with lab-based X-ray computed tomography

MOESM1 使用基于实验室的 X 射线计算机断层扫描研究植物叶片的 3D 微观结构

DOI：
10.6084/m9.figshare.7333106
发表时间：
2018
期刊：
影响因子：
0
作者：
Mathers A
通讯作者：
Mathers A

Stomatal density affects rice mesophyll cell size and shape and modulates a conserved pattern of cells through the leaf

DOI：
10.1101/2022.11.09.515764
发表时间：
2022-11
期刊：
bioRxiv
影响因子：
0
作者：
Sloan Jen;Im-Chai Saranrat;Qi Yang Ngai;Xiao Yi;Armand Jodie;Matthew J. Wilson;Xin-Guang Zhu;A. Fleming
通讯作者：
Sloan Jen;Im-Chai Saranrat;Qi Yang Ngai;Xiao Yi;Armand Jodie;Matthew J. Wilson;Xin-Guang Zhu;A. Fleming