Nitrogen use efficiency in crops under salt stress: from molecular networks to intelligent breeding

Abstract

Soil salinization threatens global arable land and agricultural sustainability, severely reducing crop nitrogen use efficiency (NUE) by disrupting root ammonium and nitrate fluxes, impairing nitrogen-assimilation enzymes, and disrupting carbon–nitrogen (C–N) balance. This review synthesizes recent advances in the coordination of salt-stress signaling and nitrogen homeostasis in plants. Two mechanistically distinct regulatory axes have recently been proposed. In one, a nitrate transporter acts as a dual sensor for nitrate and abscisic acid (ABA); in the other, SOS kinase-mediated phosphorylation of an ammonium transporter maintains ammonium uptake under Na+ stress. In addition, rapid post-translational regulatory mechanisms, including reversible protein phosphorylation and S-nitrosylation of nitrate reductase, can fine-tune nitrogen fluxes shortly after salt exposure. These findings inform a four-tier closed-loop conceptual framework comprising signal perception, transport reprogramming, metabolic redistribution, and genetic redesign. The framework yields three testable predictions: the sequential activation of regulatory tiers; a quantitative relationship between Ca2+ signal amplitude and the extent of C–N metabolic redistribution; and salt-concentration thresholds that distinguish basal homeostatic buffering from full adaptive reprogramming. Translation of this framework to field crops requires an integrated breeding pipeline that combines multi-environment quantitative trait locus (QTL) mapping, pan-genome-enabled genome-wide association studies, genomic selection for minor-effect alleles, and multiplex CRISPR editing coupled with stress-inducible synthetic promoters to pyramid favorable traits while minimizing yield penalties. A major unresolved challenge is to resolve the dynamic protein–metabolite networks that govern growth–defense trade-offs under combined salinity and nitrogen limitation. The integration of single-cell transcriptomics, isotope-based metabolic flux analysis, and machine-learning-assisted phenomics may help link genotypic variation to agronomic performance in salinized agroecosystems.