Nitrogen sensing and regulatory networks: it's about time and space

Authors: SHANKS, CARLY - New York University; ROTHKEGEL, KARIN - Catholic Pontifical University Of Chile; Brooks, Matthew; CHENG, CHIA-YI - National Taiwan University; ALVAREZ, JOSE - Universidad Andres Bello (UNAB); RUFFEL, SANDRINE - Institute Of National Research For Agriculture; KROUK, GABRIEL - Institute Of National Research For Agriculture; GUTIERREZ, RODRGIO - Catholic Pontifical University Of Chile; CORUZZI, GLORIA - New York University

Submitted to: The Plant Cell
Publication Type: Review Article
Publication Acceptance Date: 1/18/2024
Publication Date: 2/14/2024
Citation: Shanks, C.M., Rothkegel, K., Brooks, M.D., Cheng, C., Alvarez, J.M., Ruffel, S., Krouk, G., Gutierrez, R.A., Coruzzi, G.M. 2024. Nitrogen sensing and regulatory networks: it's about time and space. The Plant Cell. 36:1482-1503. https://academic.oup.com/plcell/article/36/5/1482/7608181.
DOI: https://doi.org/10.1093/plcell/koae038

Interpretive Summary:

Technical Abstract: A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its “hit-and-run” mode of target gene regulation, and temporal transcriptional cascade identified by “network walking.” Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.