NutriNet: A Network Inspired Approach to Improving Nutrient Use Efficiency (NUE) in Crop Plants

主要研究者：格洛丽亚科鲁齐（纽约大学）CoPI：丹尼斯沙沙（纽约大学），斯蒂芬穆斯（伊利诺伊大学厄巴纳-香槟分校），桑德琳鲁弗斯和加布里埃尔克鲁克（INRA，蒙彼利埃，法国）高级人员：曼普利特卡塔里（纽约大学）和W。Richard McCombie（冷泉港实验室）提高作物的养分利用效率（NUE）对于缓解未来气候变化的影响以及可持续地提高全球作物产量以满足预期的粮食和能源需求至关重要。NutriNet项目旨在识别和比较生物学上相连的基因网络，其集体表达模式可预测拟南芥和玉米NUE的表型变异。 这种跨物种网络启发的方法可以很容易地应用于许多其他经济上重要的性状，并适应其他作物。NutriNet方法的优势包括：i）利用拟南芥中基因和蛋白质相互作用的详细数据集，为数据贫乏的作物物种的分析提供信息，以及ii）识别可应用于分子育种计划的强大网络模块。原理验证研究将证明网络模块（但不一定是候选基因）调节氮同化和再动员的保守和物种特异性特征。该项目产生的新知识将包括基因发现，阐明调控电路，以及更好地理解驱动作物生产力的营养生理学的分子基础。作为一个实际的可交付成果，将开发网络启发的分子育种工具，预计在选择具有改善NUE的基因型方面比候选基因方法表现更好。NutriNet团队将系统生物学、植物生理学和作物基因组学方面的专业知识结合起来，以增加对作物养分利用的基本理解。 该项目为纽约和伊利诺斯州的博士后科学家、研究生和本科生提供多学科培训。高中生将通过纽约大学的生物学家和计算机科学家的共同导师介绍系统生物学。此外，由于公众对营养高效作物的广泛兴趣，项目团队将通过在伊利诺伊州玉米育种者学校开展的外联活动吸引观众，以利用伊利诺伊大学与育种者合作的开创性努力和悠久历史，了解作物对营养的反应和氮利用育种。基因组测序，功能基因组学，和计算工具，使系统水平的关键生理和发育过程的理解，包括NUE在模式植物拟南芥。然而，将这种“网络知识”从拟南芥转化到作物中以潜在地增强作物物种中的农业重要表型仍然具有挑战性。该项目的目标是开发网络连接的基因模块，可用于预测作物NUE的结果，通过利用拟南芥的网络知识。该项目通过开发新的数据集和分析方法来实现这一目标，具体如下：1）将NUE的表型变异与营养响应基因表达谱的新数据和现有数据相结合，从而开发出利用拟南芥和玉米遗传多样性的训练集; 2）利用分根实验设计来鉴定在根冠氮信号传导中起作用的进化上保守的基因机制，所述基因机制可以控制根在土壤中对养分的觅食; 3）使用生物信息学管道定义预测NUE性状的网络模块，以将拟南芥“网络知识”与玉米转录组数据联合收割机组合以生成NutriNet模块，所述NutriNet模块将使用其基于基因表达预测NUE的能力进行验证和测试;（4）利用从NutriNet模块中获得的信息，从不同的种质库中选择具有最佳NutriNet配置的个体基因型，然后在实验室中对这些基因型进行评估，以改善NUE性状（拟南芥）和田间（玉米）。对实验室到田间结果的比较分析将直接评估从拟南芥到玉米的网络知识的“翻译”，以作为一般的原理证明，这可以应用于其他网络和物种。 所有数据和生物资源都将应要求提供，并可通过长期数据和种质储存库获取。

PI: Gloria Coruzzi (New York University)CoPIs: Dennis Shasha (New York University), Stephen Moose (University of Illinois at Urbana-Champaign), Sandrine Ruffel and Gabriel Krouk (INRA, Montpellier, France)Senior Personnel: Manpreet Katari (New York University) and W. Richard McCombie (Cold Spring Harbor Laboratory)Improving nutrient use efficiency (NUE) in crop plants is critical to ameliorating the impacts of future climate change and to sustainably increasing global crop yields to meet projected food and energy demands. The NutriNet project seeks to identify and compare biologically connected gene networks whose collective expression patterns are predictive of phenotypic variation in NUE in Arabidopsis and maize. This cross-species network inspired approach may be readily applied to many other economically important traits, and adapted to other crops. The advantages of the NutriNet approach include: i) exploiting detailed datasets for gene and protein interactions in Arabidopsis, to inform analysis of data poor crop species, and ii) identification of robust network modules that can be applied in molecular breeding programs. Proof-of-principle studies will demonstrate both conserved and species-specific features of network modules (but not necessarily candidate genes) regulating nitrogen assimilation and remobilization. The new knowledge generated in this project will consist of gene discovery, elucidation of regulatory circuits, and a better understanding of the molecular basis for nutrient physiology that drives crop productivity. As a practical deliverable, network-inspired molecular breeding tools will be developed that are expected to perform better than candidate gene approaches in selecting genotypes with improved NUE. The NutriNet team links expertise in systems biology, plant physiology, and crop genomics, to increase the fundamental understanding of crop utilization of nutrients. The project offers multidisciplinary training to postdoctoral scientists, graduate and undergraduate students in New York and Illinois. High school students will be introduced to systems biology through co-mentorship by biologists and computer scientists at NYU. In addition, because of the broad public interest in nutrient-efficient crops, the project team will engage audiences through outreach activities at the Illinois Corn Breeders' school to leverage the pioneering efforts and long history of the University of Illinois in concert with breeders to understand crop responses to nutrients and breeding for nitrogen utilization.Recent advances in genome sequencing, functional genomics, and computational tools enable a systems level understanding of key physiological and developmental processes including NUE in the model plant Arabidopsis thaliana. However, translating this "network knowledge" from Arabidopsis to crops to potentially enhance agriculturally important phenotypes in crop species remains challenging. The goal of this project is to develop network-connected gene modules that can be used to predict the outcome of NUE in crops, by exploiting Arabidopsis network knowledge. The project approaches this goal by developing novel data sets and analytical methods as follows: 1) integrating phenotypic variation for NUE with new and existing data for nutrient-responsive gene expression profiles which allows for the development of a training set that exploits the power of genetic diversity from both Arabidopsis and maize; 2) using a split-root experimental design to identify evolutionarily conserved gene mechanisms that function in root-shoot N-signaling that may control root foraging for nutrients in the soil; 3) defining network modules predictive of NUE traits using a bioinformatics pipeline to combine Arabidopsis "network knowledge" with maize transcriptome data to generate NutriNet modules that will be validated using and tested for their ability to predict NUE based on gene expression; and, 4), using information derived from NutriNet modules to select individual genotypes that possess optimal NutriNet configurations from diverse germplasm pools which will then be evaluated for improved NUE traits in the lab (Arabidopsis) and field (maize). A comparative analysis of lab-to-field results will directly assess the "translation" of network knowledge from Arabidopsis to maize to serve as a general proof-of-principle, which can be applied to other networks and species. All data and biological resources will be available upon request and accessible through long-term data and germplasm repositories.