NutriNet: A Network Inspired Approach to Improving Nutrient Use Efficiency (NUE) in Crop Plants

New York University, New York NY

Investigators

Abstract

PI: Gloria Coruzzi (New York University) CoPIs: Dennis Shasha (New York University), Stephen Moose (University of Illinois at Urbana-Champaign), Sandrine Ruffel and Gabriel Krouk (INRA, Montpellier, France) Senior Personnel: Manpreet Katari (New York University) and W. Richard McCombie (Cold Spring Harbor Laboratory) Improving nutrient use efficiency (NUE) in crop plants is critical to ameliorating the impacts of future climate change and to sustainably increasing global crop yields to meet projected food and energy demands. The NutriNet project seeks to identify and compare biologically connected gene networks whose collective expression patterns are predictive of phenotypic variation in NUE in Arabidopsis and maize. This cross-species network inspired approach may be readily applied to many other economically important traits, and adapted to other crops. The advantages of the NutriNet approach include: i) exploiting detailed datasets for gene and protein interactions in Arabidopsis, to inform analysis of data poor crop species, and ii) identification of robust network modules that can be applied in molecular breeding programs. Proof-of-principle studies will demonstrate both conserved and species-specific features of network modules (but not necessarily candidate genes) regulating nitrogen assimilation and remobilization. The new knowledge generated in this project will consist of gene discovery, elucidation of regulatory circuits, and a better understanding of the molecular basis for nutrient physiology that drives crop productivity. As a practical deliverable, network-inspired molecular breeding tools will be developed that are expected to perform better than candidate gene approaches in selecting genotypes with improved NUE. The NutriNet team links expertise in systems biology, plant physiology, and crop genomics, to increase the fundamental understanding of crop utilization of nutrients. The project offers multidisciplinary training to postdoctoral scientists, graduate and undergraduate students in New York and Illinois. High school students will be introduced to systems biology through co-mentorship by biologists and computer scientists at NYU. In addition, because of the broad public interest in nutrient-efficient crops, the project team will engage audiences through outreach activities at the Illinois Corn Breeders' school to leverage the pioneering efforts and long history of the University of Illinois in concert with breeders to understand crop responses to nutrients and breeding for nitrogen utilization. Recent advances in genome sequencing, functional genomics, and computational tools enable a systems level understanding of key physiological and developmental processes including NUE in the model plant Arabidopsis thaliana. However, translating this "network knowledge" from Arabidopsis to crops to potentially enhance agriculturally important phenotypes in crop species remains challenging. The goal of this project is to develop network-connected gene modules that can be used to predict the outcome of NUE in crops, by exploiting Arabidopsis network knowledge. The project approaches this goal by developing novel data sets and analytical methods as follows: 1) integrating phenotypic variation for NUE with new and existing data for nutrient-responsive gene expression profiles which allows for the development of a training set that exploits the power of genetic diversity from both Arabidopsis and maize; 2) using a split-root experimental design to identify evolutionarily conserved gene mechanisms that function in root-shoot N-signaling that may control root foraging for nutrients in the soil; 3) defining network modules predictive of NUE traits using a bioinformatics pipeline to combine Arabidopsis "network knowledge" with maize transcriptome data to generate NutriNet modules that will be validated using and tested for their ability to predict NUE based on gene expression; and, 4), using information derived from NutriNet modules to select individual genotypes that possess optimal NutriNet configurations from diverse germplasm pools which will then be evaluated for improved NUE traits in the lab (Arabidopsis) and field (maize). A comparative analysis of lab-to-field results will directly assess the "translation" of network knowledge from Arabidopsis to maize to serve as a general proof-of-principle, which can be applied to other networks and species. All data and biological resources will be available upon request and accessible through long-term data and germplasm repositories.