CAREER: Biomolecular condensates in plant stress adaptation

Plants must constantly balance growth and reproduction with the need to survive environmental stress. Disease, heat, and physical challenges from their surroundings can all threaten plant fitness, yet plants cannot move to escape these conditions. Plant cells cope with these challenges using dynamic, subcellular compartments called biomolecular condensates. Unlike membrane-bound organelles, these compartments form by temporarily concentrating specific molecules together without surrounding physical membranes, allowing cells to rapidly reorganize gene activity, gene products, and stress responses. Although much is known about how condensates form, far less is known about how they control whole-organism traits like growth, resilience, and survival. This project will study a newly discovered class of condensates in the model plant Arabidopsis that contain proteins in the Guanylate-Binding-Protein Like (GBPL) GTPase family. The team will determine how these condensates help plants respond to environmental challenges such as infection, physical stress, and high temperature. Understanding the fundamentals of how living cells use these dynamic molecular assemblies to coordinate complex behaviors serves the national interest, laying a foundation for biotechnology strategies to improve crop resilience under increasingly variable environmental conditions, benefiting agriculture, food security, and economic stability. In addition, the project will train undergraduate and graduate students in interdisciplinary science spanning plant biology, biophysics, genomics, and artificial intelligence (AI), while sharing data, tools, and educational activities with the broader research and public communities. The deformable, viscoelastic materials within cells must remain dynamic, yet organized, to sustain life. Biomolecular condensates are non-equilibrium assemblies of proteins and nucleic acids, formed through liquid–liquid phase separation and related phase transitions, and there are prominent examples of them that regulate transcription, RNA processing, signaling, and development by concentrating specific factors without membranes. Despite major advances in understanding physical principles of condensate formation, how condensates control physiological outputs in multicellular organisms remains unclear. This CAREER project studies a newly discovered class of condensates formed by GBPL proteins in Arabidopsis thaliana under diverse environmental and physiological challenges. The project will determine how GBPL condensates integrate stress-associated signals, reorganize activities in the nucleus, and coordinate adaptive responses across distinct cellular and developmental contexts. It advances biotechnology by revealing condensate-based mechanisms that could be harnessed to engineer crops with improved resilience to environmental stress, while also generating genetic, biochemical, imaging, and computational resources for the broader plant science community. It advances AI through machine-learning-guided discovery of new GBPL-associated factors and predictive analysis of sequence features, interaction networks, and condensate behavior. By integrating AI, genetics, genomics, biochemical reconstitution, and quantitative live-cell imaging, this project will establish mechanistic principles for condensate-mediated signaling in multicellular plants while embedding interdisciplinary training across undergraduate and graduate levels. 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. NSF Award ID: 2543297 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Shuai Huang | Institution: OHIO STATE UNIVERSITY, THE, COLUMBUS, OH | Award Amount: $1,330,337 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543297 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543297.html