Research PGR: Genome-enabled characterization of orphan receptor-like kinases in plants.

该项目将提高对一类重要蛋白质的理解，即所谓的受体样激酶。像细胞哨兵一样，受体样激酶在感知和协调对环境线索的反应方面具有重要功能。激酶感知来自其他生物体（如病原体）或邻近细胞的分子信号，并将信号沿着传递给其他蛋白质，以启动防御反应或其他变化，如停止生长。与这些功能的重要性相一致，激酶是最丰富的植物蛋白家族之一;然而，激酶也被证明很难研究。该项目将利用理解和技术方面的新进展，使植物激酶与它们检测到的信号和信息的下游“接收者”相匹配，从而揭示这些蛋白质的功能。激酶知识的提高将为提高植物的粮食、饲料、纤维和能源产量开辟新的途径。此外，该项目将培训至少5名年轻科学家使用先进技术，并将通过专业发展研讨会为约60名教师提供植物科学教育机会，通过为期一周的植物信号研究项目研讨会介绍约30名美国原住民高中和本科生进行研究，并提供约40个夏季和学年的本科生研究机会，融合植物和数据科学。这项研究将表征拟南芥，水稻和大豆的受体样激酶（RLKs）的功能和底物。目的1旨在通过结合磷酸化和S-酰化（例如，棕榈酰化）作为RLK参与生物过程的指示剂。这项分析将利用新的结果表明，这些翻译后修饰是拮抗性的。该项目将利用蛋白质组学技术，在有和没有干旱胁迫的情况下，对水稻节间发育过程中的总蛋白质和质膜富集蛋白质进行深入定量采样;以及大豆和水稻根系对保守的推定小分泌肽的反应。将检查相对较好理解的拟南芥-假单胞菌相互作用以优化方法。对肽激素配体和根生长的关注将广泛调查预测的小分泌肽家族。目的2采取一种互补的方法来揭示RLK功能，通过为每个物种创建深度（12 k）肽库并针对纯化的蛋白激酶对其进行筛选，目的是鉴定至少25种新型激酶的激酶客户。激酶客户检测加速了下游信号的分配，其有效性已被社区迅速采用。在目标3中，将使用机器学习方法扩展实验结果，以预测植物磷酸化位点和特定激酶客户端。蛋白质组数据将用于分子网络重建和比较分析，以确定RLK信号转导模式。结果和预测将用于增强以前建立的数据库，P3 DB和P3 KB，这些数据库已经被社区广泛使用，放大了项目价值。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This project will improve understanding of an important class of proteins, the so-called receptor-like kinases. Like cellular sentries, receptor-like kinases have crucial functions in sensing and orchestrating responses to environmental cues. Kinases perceive molecular signals from other organisms (like pathogens) or from neighboring cells and pass signals along to other proteins to initiate defense responses or other changes, such as halting growth. Consistent with the importance of these functions, kinases are one of the most abundant plant protein families; however, kinases have also proven to be difficult to study. New advances in both understanding and technology will be used in this project to enable matching of plant kinases to the signals that they detect and to the downstream “recipients” of the information, thereby revealing the function of these proteins. Improvements in knowledge of kinases will open new avenues for improving plant production for food, feed, fiber, and energy. In addition, the project will train at least five young scientists to use advanced technologies and will provide plant science educational opportunities to ~60 teachers via professional development workshops, introduce ~30 Native American high school and undergraduate students to research via week-long plant signaling research project workshops, and provide ~40 summer and academic year undergraduate research opportunities that meld plant and data science.This research will characterize the functions and substrates of receptor-like kinases (RLKs) of Arabidopsis, rice, and soybean. Objective 1 aims to reveal RLK functions by combing quantification of phosphorylation and S-acylation (e.g., palmitoylation) as indicators of involvement of an RLK in a biological process. This analysis will take advantage of new results that suggest that these posttranslational modifications are antagonistic. The project will use proteomics technology to deeply and quantitatively sample total and plasma membrane-enriched proteins during rice internode development, with and without drought stress; and soybean and rice root responses to conserved putative small secreted peptides. The relatively well-understood Arabidopsis- Pseudomonas syringae interaction will be examined to optimize methods. The focus on peptide hormone ligands and root growth will survey broadly across predicted small secreted peptide families. Objective 2 takes a complementary approach to revealing RLK function, by creating deep (12k) peptide libraries for each species and screening them against purified protein kinases with the goal of identifying kinase clients for at least 25 novel kinases. The kinase client assay accelerates assignment of downstream signaling and its usefulness has been demonstrated by rapid community adoption. In Objective 3, experimental results will be extended using machine learning approaches to predict plant phosphorylation sites and specific kinase clients. Proteome data will be used for molecular network reconstruction and for comparative analysis to identify patterns in RLK signaling. Results and predictions will be used to enhance previously established databases, P3DB and SoyKB, which are already well-used by the community, amplifying project value.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.