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。预计人口将增加，加上气候变化预计会破坏农业生产，因此迫切需要逐步改善农业生产，以保障全球粮食供应安全。氮肥施用量的增加是“绿色革命”的基础，但这是不可持续的。本提案中所述的工作将有助于找到一条增加农业生产的替代途径，这可以被称为“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，在这些作物中，生长反应得到优化，以在否则会受到限制的环境中维持生物量的增加。植物的生长从根本上取决于细胞的生长和增殖，而细胞的生长和增殖发生在分生组织中。细胞生产的速率必须与发育线索、可用能量、营养供应和环境条件保持一致。分生组织细胞中的细胞质生长在很大程度上受到蛋白质合成的限制，并与细胞分裂偶联以维持细胞大小的稳态。存在进化上保守的传感和细胞内信号传导机制，其告知细胞可用的营养供应。其核心是所谓的RAPAMYCIN（TOR）蛋白质的目标，以复活节岛Rapa Nui发现的细菌产生的抗真菌化合物命名。TOR主要通过调节蛋白质合成并连接蛋白质合成和细胞增殖来对细胞生长起核心作用，但这些调节机制在植物细胞中还不清楚。TOR是一个主调节器，也通过其他输出路径发挥作用。TOR功能的一个主要途径是通过刺激核糖体来增加细胞蛋白质合成的翻译能力。最近的研究结果出乎意料地表明，一个典型的核糖体蛋白靶也作为一个转录调节因子（阻遏物）。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（RBR）蛋白有关，该蛋白以人类眼睛中的癌症突变命名。RBR及其伴侣蛋白被认为构成了控制细胞增殖和细胞生长的开关，并且可以通过环境条件来触发。在这个项目中，我们将使用根分生组织细胞系统地揭示转录和转录调控基因的功能，连接细胞生长和增殖。然后，我们将设计实验，通过这些实验，我们可以在改变生长条件时及时精确地观察开关组分的分子行为，同时通过显微镜电影跟踪生长的变化。这些类型的实验将产生丰富的数据，使人们能够全面了解监管网络。通过仔细优化的计算机模型的额外帮助，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，从而预测根系生长。在构建了这样一个预测模型之后，我们将在不同的真实的生活情况下测试其性能，例如在黑暗中或在有限的硝酸盐或蔗糖下根的生长会发生什么。我们还可能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。在完善了模型之后，我们可以开始使其适应其他改变生长的条件，如压力，或对作物产量重要的植物其他部分，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.