Bilateral NSF/BIO-BBSRC - Translational landscape to link cell growth with proliferation in the root meristem

我们的生活依赖于种植植物。预计人口增加，加上气候变化预计会对农业生产造成干扰，迫切需要实现农业生产的阶段性改进，以保证全球粮食供应的安全。氮肥使用量的增加支撑了这场“绿色革命”，但这是不可持续的。这项提案中描述的工作将有助于为增加农业产量提供一条替代途径，这可以被描述为“第二次绿色革命”。根据这一战略，农业生产力是通过使用作物来提高的，在这种作物中，生长反应得到优化，以维持生物量的增加，否则将是有限的环境。植物的生长从根本上依赖于维持细胞的生长和增殖，这发生在分生组织中。细胞生产的速度必须与发育线索、可获得的能量、营养供应和环境条件相一致。分生组织细胞的细胞质生长在很大程度上受到蛋白质合成的限制，并与细胞分裂相结合，以维持细胞大小的动态平衡。有进化上保守的感知和细胞内信号机制，告知细胞可获得的营养供应。这一过程的中心是所谓的雷帕霉素(TOR)蛋白的靶标，TOR蛋白是以在复活节岛拉帕努伊发现的一种细菌产生的抗真菌化合物命名的。Tor是细胞生长的中心，主要通过调节蛋白质合成和连接蛋白质合成与细胞增殖，但这些调控机制在植物细胞中还不是很清楚。Tor是一个主调节器，也通过其他输出途径发挥作用。TOR功能的一个主要途径是通过刺激核糖体来增加细胞合成蛋白质的翻译能力。最近的发现出人意料地表明，一个典型的核糖体蛋白靶标也具有转录调节(抑制物)的功能。我们发现，这与细胞周期的关键控制器-视网膜母细胞瘤相关蛋白(RBR)有关，该蛋白以人类突变时眼睛中的癌症命名。RBR及其伙伴蛋白被认为构成了控制细胞增殖和细胞生长的开关，可以受到环境条件的影响。在这个项目中，我们将使用根分生组织细胞来系统地发现转录和翻译调控的基因，这些基因具有连接细胞生长和增殖的功能。然后我们将设计实验，通过这些实验，当我们改变生长条件时，我们可以及时准确地观察开关组件的分子行为，同时通过微观电影跟踪生长的变化。这些类型的实验将产生丰富的数据，从而建立对监管网络的全面知识。在精心优化的计算机模型的帮助下，我们可以了解这种细胞决策电路的功能，并在不同的环境和营养条件下预测细胞增殖的程度，从而实现根的生长。在构建了这样一个预测模型之后，我们将测试它在不同现实生活情况下的表现，例如在黑暗中，或者在有限的硝酸盐或蔗糖的情况下，根的生长会发生什么。我们可能还会发现我们遗漏了一些组件，这将促使我们进行进一步的实验。完善了模型后，我们可以开始使其适应其他改变生长的条件，如压力，或植物的其他对作物产量重要的部分，如水果或种子。

Our life depends on growing plants. Projected population increases together with anticipated disruptions to agricultural production by climate change create a pressing need to achieve step-change improvements in agricultural production to guarantee security of global food supplies. Increases in the application of nitrogen fertilizers underpinned the "green revolution" but are unsustainable. Work described in this proposal will contribute to an alternative route to increased agricultural production, which could be described as a "second green revolution". According to this strategy, agricultural productivity is increased through use of crops in which growth responses are optimized to sustain the increase in biomass in what would otherwise be limiting environments. Plant growth fundamentally depends on maintaining growth and proliferation of cells, which occurs in the meristems. The rate of cell production must be aligned with developmental cues, available energy, nutrient supplies and environmental conditions. Cytoplasmic growth in meristematic cells is largely constrained by protein synthesis and is coupled to cell division to maintain cell size homeostasis. There are evolutionarily conserved sensing and intracellular signalling mechanisms that inform cells on the available nutrient supply. Central to this is the so called TARGET OF RAPAMYCIN (TOR) protein, so named after an antifungal compound produced by a bacterium that was discovered in the Easter Island, Rapa Nui. TOR is central for cell growth mainly through the regulation of protein synthesis and connecting protein synthesis and cell proliferation, but these regulatory mechanisms are not well understood in plant cells. TOR is a master regulator and also functions through other output pathways. One main route of TOR function is through stimulating ribosomes to increase the translational capacity of cells for protein synthesis. Recent findings unexpectedly show that a canonical ribosomal protein target also functions as a transcriptional regulator (repressor). We found that this is in association with a key controller of the cell cycle, the RETINOBLASTOMA RELATED (RBR) protein, named after the cancer in the eye when mutated in humans. RBR and its partner proteins are thought to constitute a switch that controls cell proliferation and cell growth and can be flicked by environmental conditions. In this project we shall use root meristematic cells to systematically uncover transcriptionally and translationally regulated genes that function to connect cell growth and proliferation. We will then design experiments through which we can precisely observe the molecular behaviour of the components of the switch in time, as we alter the growth conditions, while at the same time following changes in growth through microscopic movies. These types of experiments will produce a wealth of data that allow building a comprehensive knowledge of the regulatory network. With additional help from carefully optimized computer models, we can learn the functioning of this cellular decision making circuitry and make predictions at different environmental and nutrient conditions what is the extent of cell proliferation and therefore root growth. Having achieved to construct such a predictive model we will test its performance in different real life situations, such as what happens to root growth in dark, or under limited nitrate or sucrose. We might also find that we missed some components, and this will prompt us for further experimentation. Having perfected the model we can start adapting it to other growth-altering conditions, such as stress, or to other parts of the plant important for crop yield, such as fruits or seeds.