Advancing our understanding of autonomous leaf-specific iron deficiency responses.

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Iron is an essential nutrient for all organisms and plants serve as the main dietary source of iron for humans and livestock. Iron deficiency, or anemia, affects nearly 30% of the world’s population (~2 billion people). Thus, understanding the mechanisms that plants use to regulate Fe uptake and accumulation in edible tissues, such as leaves and seeds, will support the development of biofortified crops for improved nutritional value. Iron is also extremely reactive and therefore plants need to sense their internal levels to determine the demand for iron and adjust uptake to prevent a potentially toxic iron overload situation. Over the last decade, there have been significant advances in the molecular mechanisms that regulate iron uptake at the root level. Iron sensing, however, remains an active area of research with several hypotheses that need to be tested experimentally. This project will test a recently proposed model where leaves integrate the iron status of the entire plant and communicate such information to roots to properly regulate iron uptake and allocation within the plant. Training aspects focus on the Bioinformatics in Plant Sciences (BIPS) program developed by the principal investigator that pairs undergraduate students in computer science and engineering with plant biology students to undertake interdisciplinary research projects of interest to both.Recent spatiotemporal gene expression analyses revealed that leaves respond faster to iron deficiency than roots. Moreover, under certain conditions, leaves and roots display opposite iron-related transcriptional programs suggesting that leaves have autonomous mechanisms to sense iron. This project will integrate leaf-specific time-series gene expression analyses together with targeted high-throughput DNA-protein and protein-protein interaction screens to identify transcriptional complexes necessary to regulate iron homoeostasis in leaves. In addition, tissue-specific gene editing approaches will be used to assess whether chloroplast-to-nucleus communication is essential for proper iron sensing in leaves, particularly in mature photosynthetic leaves. Experiments will be complemented with modeling approaches to explore the predictive power of constraint-based simulations when the stability of protein-DNA and protein-protein interactions, determined experimentally, are sequentially incorporated into kinetic models.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.