BIO-AI: RESEARCH-PGR: Harnessing Multi-Omics and AI/ML to Unveil Nutrient Recycling Mechanisms in Maize Senescence

In plants, like corn, leaf aging (also known as senescence) is accompanied by regular cellular and physiological changes. Some of these changes are outwardly visible--like drying, loss of green color due to reduced photosynthesis, and browning under drought. Other changes are not obvious to the naked eye but are very important for the plant. One such change involves recycling of nutrients from the aging leaf to developing parts of the plant, such as kernels on the ear. The timing of senescence is crucial—too early results in low grain yield. By contrast, plants that delay senescence and stay green longer into the growing season tend to yield more grain. Exactly what controls the timing and the many steps in senescence is not clearly known. This project will develop new ways to measure corn traits during aging, discover causative genes, and apply computational methods like artificial intelligence to understand how nutrient recycling in plants can improve agricultural outcomes. The project will also provide interdisciplinary training for undergraduate and graduate students, postdoctoral researchers, and high school teachers to help inspire and prepare the next generation of scientists, ultimately strengthening capacity in plant biology, data science, and agricultural biotechnology. This project aims to uncover the genetic, metabolic, and regulatory mechanisms that govern leaf senescence and nutrient remobilization in maize—processes that directly impact grain quality, nitrogen use efficiency, and overall crop productivity. Despite their agronomic importance, the molecular drivers of senescence remain poorly understood in cereals. To address this gap, high-resolution, time-series datasets will be generated across a genetically diverse maize panel, capturing physiological traits, metabolite profiles, transcriptomes, and chromatin accessibility. Single-cell assays will add spatial resolution to senescence-related transcriptional and chromatin changes. These datasets will be integrated using artificial intelligence tools, such as large language models, and machine learning to identify causal genes, regulatory elements, and metabolite-phenotype associations. Top candidate genes will be functionally validated using gene-editing technologies to confirm their roles in regulating senescence and nutrient allocation. The resulting insights will inform breeding and biotechnological strategies to enhance nitrogen use efficiency, reduce fertilizer inputs, and improve resilience to abiotic stress. This project is co-funded by the Division of Integrative Organismal Systems Plant Genome Research Program and by the Division of Emerging Frontiers, both in the Directorate for Biological Sciences. 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.