RESEARCH-PGR: Uncovering the molecular mechanisms that integrate nutrient and water dose sensing and impact crop production

对植物生长至关重要的两种资源，氮(N)和水(W)，在全球土壤中是有限的，造成边缘土壤在农业上不具生产力。要设计或培育能够在边缘环境中茁壮成长的作物品种，首先必须了解植物如何感知和整合对氮(N)和水(W)的反应。检测植物赖以感知和响应多种环境输入的基因和基因调控机制，是使作物适应不断变化的气候的努力的前沿。因此，该奖项设在巴斯德象限，这是一个基本科学发现应用成果的科学空间。研究人员将通过对遗传易驯化的模式植物拟南芥进行突变/转基因分析，发现植物感知和整合非生物信号N和W的分子机制。在拟南芥中的初步测试将优先在水稻中测试候选基因。这一发现将被用于工程或培育适应低N低W边缘土壤的水稻。这项研究将与菲律宾国际水稻研究所(IRRI)联系起来，该研究所是亚洲易受胁迫地区新育种材料的主要来源。通过立即考虑部署到IRRI边缘土壤环境的特征开发管道，该项目的结果具有很高的影响概率。该项目提供了基因组学和系统生物学方法的独特应用，以改善作物在压力下的表现，将网络知识从经过充分研究的模型转化为作物。虽然人们的努力主要集中在植物如何发出N或W状态的信号，但植物如何整合这两种非生物信号仍未被探索。生物是否对绝对营养量(摩尔)与其在水中的浓度(摩尔数)的变化做出反应在农业中特别相关，因为土壤干燥可以改变养分浓度，而不改变其绝对量。为了比较剂量和浓度的影响，水稻暴露在因子矩阵中，在不同的组合中改变氮素剂量(N)和水(W)，并量化转录组和表型反应。使用线性模型，研究人员确定了对N-摩尔、W-体积、N-摩尔浓度(N/W)或它们的协同作用(NxW)的不同剂量反应。重要的是，N剂量和W互作(N/W或NxW)最能解释其表达的基因与田间试验中的作物产量有关。这笔赠款的目标是揭示这些影响作物产量的N-W相互作用的分子基础。为此，纽约大学、威斯康星大学麦迪逊分校和菲律宾国际水稻研究所(IRRI)的一项合作努力提出，在遗传易驯化的模式植物拟南芥和世界上最重要的作物之一的水稻中，发现负责整合N和W输入的基因。该项目将使用结合基因组学、生物信息学、统计建模和植物生理学的多学科方法来了解植物基因组如何感知和整合N和W信号，以优化植物生物量和谷物生产。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Two resources critical to plant growth, Nitrogen (N) and Water (W), are limited in soils globally, creating marginal soils that are agriculturally unproductive. To engineer or breed crop varieties that can thrive in marginal environments, it must first be understood how plants sense and integrate responses to Nitrogen (N) and Water (W). Detecting the genes and gene regulatory mechanisms plants rely on to sense and respond to multiple environmental inputs is at the leading edge of efforts to adapt crops to a changing climate. Thus, this award resides in Pasteur's quadrant, the scientific space where fundamental scientific discoveries have applied outcomes. The researchers will discover the molecular mechanisms plants rely on to perceive and integrate the abiotic signals N and W using mutant/transgenic analysis on the genetically tractable model plant Arabidopsis thaliana. Initial testing in Arabidopsis will prioritize candidate genes to test in rice. This discovery will be exploited to engineer or breed rice adapted to low-N low-W marginal soils. The research will be linked to the International Rice Research Institute in the Philippines (IRRI), which is the primary source of new breeding material for stress-prone regions of Asia. Outcomes of this project have a high probability of impact through immediate consideration for deployment into the IRRI trait development pipeline for marginal soil environments. This project offers a unique application of genomics and systems biology approaches to improve crop performance under stress in translating network knowledge from well-studied models to crops. While efforts have focused on how plants signal either N or W status, how plants integrate these two abiotic signals remains unexplored. Whether organisms respond to changes in absolute nutrient amount (moles) vs. its concentration in water (molarity) is particularly relevant in agriculture, as soil drying can alter nutrient concentration, without changing its absolute amount. To compare effects of amount vs. concentration, rice was exposed to a factorial matrix varying nitrogen dose (N) and water (W) in range of combinations, and transcriptome and phenotype responses quantified. Using linear models, researchers identified distinct dose responses to either N-moles, W-volume, N-molarity (N/W), or their synergistic interaction (NxW). Importantly, genes whose expression is best explained by N-dose and W interactions (N/W or NxW) were associated with crop outcomes in field trials. The goal of this grant is to uncover the molecular basis for these N-by-W interactions that affect crop yield. To this end, a collaborative effort between New York University, University of Wisconsin-Madison, and the International Rice Research Institute (IRRI) in the Philippines proposes to discover the genes responsible for the integration of N and W inputs in the genetically tractable model plant Arabidopsis thaliana and in rice, one of the world's most important crops. This project will use a multidisciplinary approach that combines genomics, bioinformatics, statistical modeling, and plant physiology to understand how plant genomes sense and integrate N and W signals to optimize plant biomass and grain production.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.