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

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.

Figure 1.

The N-dose-dependent regulation of N-uptake, N-signaling, and N-growth follows Michaelis-Menten (MM) kinetics. A) The rate of N-uptake by NRTs and AMTs is regulated by MM kinetics (Ho et al. 2009; McNickle and Brown 2014). B) Swift et al. (2020) demonstrated that the transcriptional response to N-dose also follows MM kinetics in Arabidopsis wild-type plants (Swift et al. 2020). Moreover, TGA1 overexpression and tga1/4 mutant analysis revealed that a portion of this MM-mediated N-dose transcriptional response is mediated by the master transcription factor TGA1, which affects plant growth rate (Swift et al. 2020). C) N-dose-regulated growth responses measured by biomass is also regulated by MM kinetics (Lana et al. 2005). Thus, transcriptome kinetics responding to changes in N-dose has the potential to enhance plant growth. Figure adapted from Swift et al. (2020). Figure created with BioRender.com.

Figure 2.

Time- and space-dependent modes of action for NLP7 as a master regulator of nitrate signaling. A) NLP7 binds to nitrate and acts as a nitrate sensor as determined using the genetically encoded split mCitrine-NLP7 nitrate biosensor (sCiNiS) assay (Liu et al. 2022). Fluorescent signal was detected 5 min after nitrate treatment in both mesophyll and primary root tip cells (Liu et al. 2022). B) Both NLP7 and NLP6 accumulate in the nucleus in response to nitrate as determined with TF-fusion proteins expressed in their respective mutant backgrounds, showing that accumulation of either TF in the nucleus is independent of each other, but dependent on nitrate (Marchive et al. 2013; Guan et al. 2017; Liu et al. 2017; Cheng et al. 2023). C) The “hit-and-run” model of transcription posits that a pioneer TF transiently binds to the promoter of a target gene to open the chromatin and allow for other partner TFs to bind the promoter, thereby making NLP7 available to bind the next target gene (Para et al. 2014; Alvarez et al. 2020). The TARGET assay combined with ChIP-seq and DamID was used to identify these highly transient NLP7 target genes (Alvarez et al. 2020). Figure created with BioRender.com.

Figure 3.

Determining high-confidence GRNs by AUPR and use in network walking. A) (A1) The predicted TF–target gene interactions are first ranked according to edge score, and then compared to validated TF–target gene interaction data to calculate precision and recall. (A2) The values are then plotted on the AUPR curve to select a cutoff TF-target edge score. The edges in the predicted network (blue line) were significantly more likely to be true (i.e. validated) edges than when the edge order ranking was randomized (gray lines). The graph is a screenshot from the automated AUPR analysis feature in connectf.org (Brooks et al. 2021). (A3) The edge score cutoff is used to “prune” the network for high-confidence interactions. B) Network walking charts a path between direct to indirect target genes of a TF₁ via TF₂s (Brooks et al. 2019, 2021). In this example, the TF NLP7 directly regulates TF₂s as identified with the TARGET cell-based assay (Alvarez et al. 2020). The target genes for each TF₂ can be determined using predicted GRN edges from the NxTime network and/or using validation data from methods such as the TF-TARGET assay and/or TF-target binding by DAP-seq (Table 1). Bottom panel adapted from Brooks et al. (2019).

Figure 4.

Spatiotemporal responses after nitrate treatments in Arabidopsis root cells are highly dynamic and localized. A) During nitrate treatments, the first cell type to respond is epidermis, followed by cortex. Consistent with their outermost location and first layers of nitrate acquisition. At later times of treatment, nitrate responses are present in all major root cell types (Contreras-López et al. 2022). B) Transverse view of root cells shows gene ontology (GO) enrichment after nitrate treatments. The first enriched GO term is “response to nitrate,” moving from epidermis toward innermost cell types. At later times, “nitrate assimilation” and “root system development” go from inner to outermost cell types. Transcriptomic analysis and GO terms were obtained from sorted root cells by Contreras-López et al. (2022). C) Nitrate-demand signaling model. When roots are grown on limited nitrate levels, C-terminally encoded peptides (CEPs) and tZ-type cytokinins (CK) are translocated to the shoot, increasing the expression levels of CEPD1/2 and CEPD-L2. In turn, shoot-derived CEPD1/2 and CEPD-L2 descend back to the root and increase the expression of nitrate transporters NRT3.1 and NRT1.1/NPF6.3 and NRT2.1 to compensate for the lack of nitrate in the soil. This highly coordinated system results in plant growth adaptation according to the changing nutrient levels (Tabata et al. 2014; Ohkubo et al. 2017; Ota et al. 2020).

Figure 5.

Investigating spatiotemporal gene expression using single-cell RNA sequencing in Arabidopsis thaliana. A) Longitudinal view of the root shows different developmental zones from young (meristematic) to mature (maturation zone), which is used as a model for single-cell analysis to construct developmental trajectories in a single experiment (Denyer et al. 2019; Rich-Griffin et al. 2020). B) Thousands of protoplasts or nuclei at different developmental stages are used for single-cell library construction (Swift et al. 2022). C) Computational analysis of scRNA-data allows the construction of “developmental trajectories” of root cells expressing a gene of interest (red dots), NRT1.1/NPF6.3 using the Plant scRNA-seq Browser with representative screenshots from this tool (Denyer et al. 2019; Ma et al. 2020) (Table 2). D) “Pseudotime” expression of NRT1.1/NPF6.3 from young meristematic cells to mature cells show that NRT1.1 expression is highly expressed in differentiated trichoblast (Denyer et al. 2019; Ma et al. 2020).