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.
