Many human diseases and developmental defects are associated with malfunction in communication between cells. Similarly, plants also suffer from uncontrolled spread of diseases, inappropriate seasonal or environmental responses, or improper organ development when cell-to-cell communication goes unchecked. This research, building on unique expertise in cell and molecular biology and computational modeling and machine learning, aims to unravel how important protein regulators are targeted to the channels that connect neighboring cells and fine tune cell communications in plants. New concepts and discoveries made by this research will be introduced to both graduate and undergraduate students as integrated new bioinformatics courses as well as laboratory modules. Furthermore, the software developed from this project will be made available free online to the public together with the source code. In addition, the newly produced data, results, and tools will be incorporated into an existing webserver with user-friendly interface to better serve the research and education community. The fundamental knowledge gained by this study has the potential to be utilized in the long run to engineer genetically encoded synthetic proteins to effectively control nutrient distribution, flow, and fitness in agriculturally important crop plants.
Given the fundamental role that plasmodesmata play in the biology of plants as intercellular communication channels, we know so little about the molecular architecture and mechanisms underlying the plasmodesmal association and regulation. Numerous plant viral proteins, both soluble and membrane-attached, have been studied for a few decades in search for some clues about their plasmodesmal targeting signals and mechanisms. However, no universal signal or mechanism was identified. To address this gap in the current knowledge, the research team aims to unveil molecular mechanisms underlying plasmodesmal-targeting/retention using innate plasmodesmal regulators called the Plasmodesmata-located Proteins (PDLPs). These protein families are found to share an evolutionarily conserved feature which is required for their plasmodesmal localization and function; this novel domain is here named the Plasmodesmata-association & activation Cassette (PaaC). The outcomes of in-depth investigations of the PaaC domain, will provide unprecedented mechanistic insights into how plasmodesmata-associated integral membrane proteins are targeted and anchored at plasmodesmata, and how they may self-assemble to form functional protein complexes. To achieve these goals, the research team will take an integrative computational approach utilizing newly developed machine learning tools and techniques in combination with the state-of-the-art bioimaging techniques. It is anticipated that computational extraction and identification of complex features underlying the PaaC and PaaC-like signals will also serve as a valuable means to identify previously unrecognized plasmodesmal proteins that are encoded by various plant genomes.
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.
