Location: Global Change and Photosynthesis Research
Project Number: 5012-21000-032-037-I
Project Type: Interagency Reimbursable Agreement
Start Date: Sep 1, 2025
End Date: Aug 31, 2029
Objective:
The objective of this proposal is to improve nitrogen use efficiency (NUE) and reduce N2O emissions in bioenergy maize and sorghum by manipulating combinations of transcriptions factors identified through an evolutionarily informed machine learning approach.
Approach:
ARS will characterize the transcriptome response, nitrogen and photosynthesis traits, and nitrous oxide emissions of sorghum and maize transgenics in the nitrogen field trials. These trials will be managed by our collaborator. Collaborators will use machine learning to identify the genes that are most predictive of photosynthesis, biomass, and nitrogen use efficiency in maize, sorghum and setaria. Collaborators will identify targets of important transcription factors using cell-based and in vitro assays. ARS and collaborators will combine these datasets to generate evolutionarily informed gene regulatory networks that reveal connections between important genes and physiological traits of interest that determine nitrogen use efficiency. Constructs for transgenic manipulation of identified transcription factor combinations will be designed by the collaborator as well as the predicted impact on NUE and N2O emissions tested by collaborators in the field under different nitrogen treatments.