3.1. RGA1 mutation apparently affects many N-responsive genes and enhances N-responsive yield and NUE
The main impetus for a detailed study of genomewide nitrate response in rga1 mutant relative to WT came from our meta-analysis of nitrate-responsive transcriptomes and RGA1-regulated transcriptomes, as they were never analysed together. Venn selections were done using all the reported 5128 RGA1-regulated DEGs (Ferrero-Serrano, et al. 2018; Pathak, et al. 2021) with 23,926 N-responsive DEGs compiled from 7 published nitrate-transcriptomes in WT rice (Coneva, et al. 2014; Mandal, et al. 2022; Misyura, et al. 2014; Pathak, et al. 2020; Sawaki, et al. 2013; Shin, et al. 2018; Yang, et al. 2015). They identified 3839 DEGs as both N-responsive as well as RGA1-regulated (Fig. 1a). Moreover, our pot experiments revealed that the mutant has higher grain yield than the WT in both normal (15 mM) and low nitrate (1.5 mM) input, though only the former was statistically significant (Fig. 1b). This could be because 15 mM nitrate inhibited yield in the WT (relative to 1.5 mM), while it had no impact on the mutant (Fig. 1c). This also translates into enhancement of NUE in the mutant (Fig. 1c). RGA1 mutation seems to have rendered the rice plants un-responsive or insensitive to higher N-dose, indicating that RGA1 regulates N-sensitivity in the WT. These findings prompted a direct comparison of genomewide nitrate-response in the mutant and the WT.
3.2. RGA1 mutation alters genomewide nitrate-responsive gene expression
Nitrate is an important source of N for all plants including rice, even though it was adapted to tolerate ammonium (Bloom 2015; Kronzucker, et al. 1999). Farm soils contain a dynamic mixture of different N-forms due to microbial conversions and fertilizers containing different N-forms, so we specifically designed our experiments with nitrate as the only form of N-supply and provided it directly to the target tissue (leaf) as a transient treatment to study N-responsive transcriptome. Excised leaves of 32 days old WT and rga1 mutant deficient in G-protein α subunit (Ashikari, et al. 1999; Pathak, et al. 2021) were treated in vitro with KNO3 or KCl prior to RNA isolation and simultaneous microarray analysis. The nitrate responsive genes identified in the WT (KCl vs. KNO3) were reported and analyzed separately in Mandal et al. (2022) and used as the baseline data for the current analysis of nitrate responsive genes identified in the RGA1 mutant (KCl vs. KNO3). Scatterplot analysis revealed high correlations among our replicates, indicating the uniformity of results across replicates (Supplementary Fig. 1). A total of 3416 genes showed differential expression (2245 upregulated and 1171 downregulated) in response to nitrate treatment in the mutant, as visualized in the volcano plot (Fig. 1d). The complete list of DEGs is provided in Supplementary Table 1. The table also reveals 4 upregulated and 11 downregulated miRNAs that show significant N-response in the transcriptome. Overall, our data clearly show that RGA1 mutation has an extensive genomewide impact on nitrate-responsive gene expression.
3.3. Meta-analysis reveals common and unique N-responsive genes in RGA1 mutant
We compared our list of 3416 nitrate-responsive DEGs in the rga1 mutant (relative to KCl) with those from 2 published rga1 mutant transcriptomes that did not consider N and 7 WT nitrate transcriptomes that did not consider rga1 (Fig. 2a). The latter included one data set from the corresponding WT japonica under identical treatment conditions (Mandal et al., 2022). Our Venn selections delineated and confirmed at least 835 of the 3839 genes shared between nitrate-responsive transcriptomes and RGA1-regulated transcriptomes (Fig. 2a). In other words, these 835 genes were actually confirmed as nitrate-responsive in the mutant for the first time. There were an additional 2031 DEGs in the mutant that were common with the seven nitrate transcriptome datasets in WT; and an additional 107 DEGs common with the two rga1 mutant transcriptomes. More importantly, our microarray revealed 443 novel RGA1-regulated N-responsive DEGs not reported in any earlier transcriptome (Fig. 2a).
A separate Venn selection of the nitrate responsive DEGs in the rga1 mutant vs. WT was done, as the WT data were obtained under identical conditions (Mandal, et al. 2022). It revealed 1667 common DEGs, which were comparable in the nature and extent of their regulation by nitrate, especially in the mild to moderate levels of regulation (Fig. 2b,c). There were a few common DEGs with high levels of regulation that differed between the WT and mutant, especially among the downregulated DEGs. Interestingly, RGA1 mutation rendered 5021 or 75% of the 6688 nitrate-responsive genes in the WT as unresponsive, while also rendering 1749 unresponsive genes in the WT as nitrate responsive. Further segregation of these novel 1749 DEGs into up- and downregulated sub-categories revealed 1146 and 601 DEGs respectively in the mutant (Supplementary Table 2). Overall, the upregulated genes are almost twice the number of downregulated genes, both among the common DEGs and those unique to the mutant (Fig. 2b,c).
After including gene Ids for 151 differentially expressed proteins from the previously published rga1 mutant proteome dataset (Peng, et al. 2019), the number of novel and exclusive RGA1-regulated N-responsive DEGs identified in the current study was 2453 (Supplementary Table 3).
3.4. Common and unique nitrate responsive biological processes in WT and rga1 mutant
Further Venn selections were performed with the process categories to ascertain the functional biological implications of the above 1749 uniquely up/downregulated genes identified in the mutant. This revealed 15 uniquely enriched biological process for downregulated and 16 for upregulated nitrate-responsive DEGs in the rga1-nitrate transcriptome (Fig. 2d,e). In other words, the seemingly huge differences in the number of up- and downregulated DEGs does not translate into comparable differences in the number of processes involved. This is true at least for the processes uniquely regulated by nitrate in the mutant (but not in the wild type). All the common and unique processes enriched for nitrate-responsive DEGs in WT and rga1 datasets are shown in Fig. 2e.
AgriGO analyses revealed that the 2474 RGA1-regulated, nitrate-responsive DEGs identified exclusively in our study of the mutant (and not in the earlier studies) belonged to 9 uniquely enriched processes as well as to 10 previously known biological processes (Fig. 2e and Supplementary Table 4). Novel RGA1-regulated and N-responsive processes include regulation of biological quality (28 DEGs), porphyrin metabolism (8 DEGs), ARF protein signal transduction (7 DEGs), small GTPase mediated signal transduction (18 DEGs), tetrapyrrole metabolism (8 DEGs), and cofactor metabolism (21 DEGs). Further, additional/novel RGA1-regulated and N-responsive DEGs were identified belonging to 10 known processes such as cellular nitrogen compound metabolism (41 additional), heterocycle biosynthesis (15 additional), homeostasis (23 additional), carbohydrate metabolism (83 additional), photosynthesis and light harvesting (6 additional).
To understand the overall biological roles of all the N-responsive DEGs found in the current study, they were subjected to GO analysis using the AgriGO ver. 2.0 (Tian, et al. 2017) and REVIGO (Supek, et al. 2011). Only the statistically over-represented terms for biological processes were used for further analysis (Fig. 2d,e; Supplementary Tables 3 and 4). Photosynthesis, carbohydrate biosynthesis, chlorophyll, tetrapyrrole and porphyrin biosynthesis and cellular nitrogen compound biosynthesis were among the 16 uniquely over-represented GO-BP terms for upregulated nitrate responsive DEGs in the mutant. This was followed by 7 common processes for upregulated DEGs in both mutant and WT nitrate-transcriptomes. These include carbohydrate metabolism, cellular nitrogen compound metabolism and glucan metabolism. For the downregulated nitrate-responsive DEGs unique to the mutant, 15 enriched GO terms were identified, including cellular protein metabolism, intracellular protein transport, small GTPase mediated signal transduction, cell redox homeostasis and regulation of biological quality. There were also 8 processes common to downregulated DEGs in both WT and mutant datasets, including cellular protein localization, cellular homeostasis, protein folding and transport.
Further, statistically significant GO terms were also retrieved using gProfiler (Raudvere, et al. 2019) and EXPath (Tseng, et al. 2020). They revealed many similar but also a few additional process terms associated with uniquely N-responsive DEGs in the mutant viz., response to light stimulus, response to cytokinin, cell surface receptor signalling pathway, protein refolding, glutathione metabolism and cell wall biogenesis were the most significant among those processes (Supplementary Table 4). Photosynthesis, chlorophyll biosynthesis, carbohydrate metabolism and carboxylic acid metabolism were most over-represented in the upregulated DEGs, while protein transport (intracellular and vesicle-mediated), protein folding, cell redox homeostasis and regulation of ARF protein signal transduction were the most over-represented GO terms among the downregulated DEGs. All the DEGs were also mapped to various functional categories or ‘bins’ using MapMan (Thimm, et al. 2004).
The results of MapMan analysis were highly correlated with those of AgriGO, EXPath and gProfiler. Metabolic processes, Cellular Response and Photosynthesis were found to be the bins to which a significant number of DEGs were mapped (Fig. 3). In addition to these, DEGs were also mapped to regulation, receptor like kinases and tetrapyrrole biosynthesis, among others (Supplementary Fig. 2). Further analysis of these bins revealed that most of the DEGs were mapped to important sub-bins like stress response (abiotic and biotic), development, cell wall, light reactions, secondary metabolism, RLKs (LRR, S-locus, WAK, PERK-like and DUF26; receptor like cytoplasmic kinases) (Supplementary Fig. 3). Pathway analysis using PlantMetGenMap (Joung, et al. 2009) revealed 20 statistically significant differentially regulated pathways (Supplementary Table 5). These include cellulose biosynthesis, NAD salvage pathway II, β-D-glucuronide degradation, Calvin cycle, proline biosynthesis, etc. Most of the DEGs involved in these pathways showed upregulation.
Our search for processes (GO-BP terms) associated with the DEGs using AgriGO, EXPath and MapMan (Fig. 2e and 3–4) further elaborate the role of RGA1 in the regulation of several N-responsive processes. AgriGO analysis revealed that all the genes related to carbohydrate metabolic process were upregulated by N-treatment in WT (139) as well as in the mutant (102). However, there were 46 common genes in both the datasets and 71 and 41 unique genes in WT and mutant respectively. Similarly, all the genes related to cellular nitrogen compound metabolic process were also upregulated in WT (60) and mutant (52) and there were 26 common and 26 unique DEGs in the mutant. Out of 51 DEGs involved in photosynthesis which we earlier reported as N-responsive in WT plants, 25 were also common in the mutant dataset (total 72).
3.5. RGA1 signalling mediates N-regulation of agronomic traits
A total of 414 DEGs were mapped to the metabolic processes bin using MapMan (Supplementary Table 6; Supplementary Fig. 2). These included genes involved in carbohydrate, lipid, amino acid and nucleotide metabolism and cell wall biosynthesis (Fig. 3d,e). But more interesting is the downregulation the genes of N-uptake and assimilation in the mutant that are known to be upregulated by nitrate: Nitrate Transporter (NRT2.6) and Glutamate Synthase (GOGAT) were found to be downregulated, while other primary N-assimilation genes like Glutamine Synthetase (GS) and Glutamate Dehydrogenase (GDH) were upregulated. This is by far the most compelling genetic evidence of the role of G-protein (RGA1) in nitrate response.
We also obtained 61 DEGs which are involved in secondary metabolism like flavonoid and phenylpropanoid metabolism. Most of these DEGs were found to upregulated, other than those involved in tetrapyrrole metabolism. Further, a total of 185 DEGs belonging to various large enzyme families like cytochrome P450, oxidases, nitrilases, UDP glucosyl and glucoronyl transferases, glutathione S transferases, alcohol dehydrogenases, etc. were identified in the analysis. 142 of these enzyme-related DEGs were upregulated whereas 43 were downregulated in the rga1 mutant. Their heatmap in Fig. 3d and listing in Supplementary Table 6 show 90 unique DEGs related to these enzyme classes uniquely regulated by nitrate in the mutant. 79 DEGs related to biosynthesis and signalling of hormones like auxin, ABA, BR, ethylene, cytokinin, GA, jasmonic acid and salicylic acid were also identified (Fig. 3e).
Photosynthesis was found to be the most enriched category across analyses using all the in-silico tools. A total of 72 DEGs were mapped in the photosynthesis bin of MapMan (Fig. 3). These include genes involved in light reactions, Calvin cycle as well as photorespiration. Heatmap for 47 unique DEGs (along with total) related to photosynthesis uniquely regulated by nitrate in the mutant is shown in Fig. 3. Out of total 72, 68 DEGs were upregulated whereas only 4 were downregulated in the mutant (Fig. 3a).
Finally, on the basis of literature (Boonburapong and Buaboocha 2007; Gao and Xue 2012), MapMan and other database searches (RGAP, RAPdb, STIFDB2 and PlantTFDB), we prepared a list of non-redundant 857 DEGs across metabolism, signalling and transcriptional regulation categories. These DEGs were related to photosynthesis and/or enzymes and/or C and N metabolism and/or (TFs) and/or RLKs and/or G-proteins and/or MAP Kinases and/or hormones in the rga1 nitrate transcriptome (Supplementary Fig. 7 and Supplementary Table 7). Interestingly, as many as 1536 N-responsive DEGs belonging to the same above categories were highly enriched with higher fold change in the WT but not in the mutant (Supplementary Table 7), indicating that nitrate response in the mutant and WT target the same processes through different genes.
The agronomically important traits associated with the RGA1-regulated and N-responsive DEGs such as root/shoot/tiller development, heading date/panicle emergence/flowering, seed development and yield are shown in Fig. 4. These heatmaps also indicate their belonging to metabolism and/or signalling and/or transcriptional regulation categories (Fig. 4 and Supplementary Fig. 7). Out of 147 DEGs for leaf/culm/ root development, 59 belonged to these three functional categories (17 downregulated and 42 upregulated). Similarly, 21 (6 down- and 15 upregulated) and 46 DEGs (12 down- and 34 upregulated) belonging to these functional categories were identified for heading (64) and seed development (100) respectively. Additionally, these functional categories also make up 250 (87 down- and 163 upregulated), 29 (8 down- and 21 upregulated) and 5 (1 down- and 4 upregulated) DEGs for tolerance/resistance (581), yield/productivity (67) and tiller (17) respectively.
Further comparison revealed a smaller number of exclusive DEGs related to N-responsive agronomic traits in the mutant relative to the WT and their fold change was also lesser (Fig. 4 and Supplementary Table 7). There were in total 198 exclusive DEGs in WT related to leaf/culm/ root development compared to 74 exclusive in the mutant), 108 related to heading/inflorescence/panicle (39 exclusive in mutant), 114 related to seed development/yield (46 exclusive in mutant), 816 related to tolerance/resistance (296 exclusive in mutant) and 20 related to tiller development (6 exclusive in mutant). This means that 604 nitrate-responsive DEGs associated with agronomic traits in the WT were rendered unresponsive to nitrate due to the lack of functional RGA1 in the mutant. This pattern is also reflected in the phenotypic responses in the mutant and WT as explained in later sections.
3.6. Subcellular distribution of DEGs and identification of associated transcription factors
In order to understand the effect of Gα mutation on nitrate response at global cellular and subcellular levels, all DEGs were subjected to subcellular prediction using cropPAL2.0 program (Hooper, et al. 2016). We found that most of the upregulated DEGs were distributed into cytosol (22%), plastid (21%), plasma membrane (12%) and extracellular (11%), among others (Supplementary Fig. 4); while most of the downregulated DEGs were distributed into cytosol (26%), plasma membrane (18.5%) and nucleus (15%) (Supplementary Fig. 4 and Supplementary Table 8). This clearly suggests the involvement of RGA1 in the regulation of pathways occurring in these multiple sub-cellular locations and organelles. Our search with the DEGs in STIFDB2, RGAP, PlantTFDB and MapMan databases revealed a total of 343 differentially regulated (TFs) (TFs) spread across 30 TF families (TFFs) with bZIP, NAC and bHLH TF being most over-represented. About 66% of the TFs were upregulated while the rest of them (34%) were downregulated. Most of the TFs of bHLH, bZIP, HD-ZIP, GRAS, MYB-related and SBP TFFs were upregulated while that of C2H2, ERF, Dof, WRKY and HSF TFFs were downregulated (Supplementary Table 7).
3.7. PPI Network Analysis revealed photosynthetic and metabolism as enriched clusters
We developed protein-protein interaction (PPI) networks and mapped the DEGs onto them to find RGA1 regulated interactions in nitrate response/NUE. The network was constructed using only the experimentally verified interaction data from STRING, MCDRP, BioGRID and PRIN databases and expression values of the DEGs were colour-coded to visualise the network using Cytoscape 3.8.2 (Shannon, et al. 2003). The network consisted of 542 nodes and 1808 edges after removing the duplicated edges. As compared to the WT, the DEGs in the mutant formed a majority of the interacting proteins in the network, indicating the differences in functional interactions underlying nitrate response in the wild type and RGA1 mutant (Supplementary Fig. 5 and S6 and Supplementary Table 9). MCODE analyses revealed four highly connected sub-clusters of molecular complexes having MCODE score > 3 with node number > 10 (Supplementary Fig. 6). A total of 32, 30, 12 and 11 nodes having 445, 402, 64 and 55 edges were found in the sub-cluster 1, 2, 3 and 4 respectively. Singular Enrichment Analysis (SEA) of the genes in these clusters using AgriGO revealed that genes of both clusters 1 and 2 are associated with “metabolism”, “cellular biosynthesis” and “translation” (Supplementary Fig. 6). The genes of cluster 3 and 4 were found to be involved in “ubiquitin-dependent protein catabolic process” and “photosynthesis” respectively.
We also found a large network formed only of the validated interactors of RGA1 (Supplementary Fig. 5 and S6). A majority of these interactors (46 out of 64) were found to be N-responsive when these interactors were screened against known N-responsive genes.
3.8. qPCR validation of DEGs across important biological processes
In order to validate the RGA1 regulation of N-responsive DEGs selected from different functional categories identified in the microarray data (GSE62164), relative transcript abundance of 12 upregulated DEGs and 6 downregulated DEGs was determined by qRT-PCR (Fig. 5a). The primer sequences for the DEGs validated in the experiment are provided in Supplementary Table 10. They were selected based on their involvement in important pathways such as regulation of cell division and cell cycle and/or DNA synthesis and repair and/or development (OsSTA28, OsCYP37, CYP26-2, Zinc finger, FYVE-type domain containing protein, OsNYC3, Regulator of chromosome condensation, OsRPA1, Replication protein A1, OsRH16 and OsRH10). The expression patterns matched with the microarray data and Fold changes for the 8 DEGs (4 upregulated and 4 downregulated) were significantly different in the mutant as compared to the WT (Fig. 5a). We also validated 4 DEGs belonging to light signalling and/or photosynthesis (OsCRY1a, OsLFNR2, Non-photo hypocotyl3, and YL1) as well as 5 belonging to abiotic stress response and/or metabolism (OsDjC26, OsGS2, OsLACS1, Sugar/inositol transporter, and OsADH1). Under these categories, the fold change for 4 upregulated and 1 downregulated DEGs were significantly different in the mutant as compared to the WT. Figure 5a shows the comparison of the nitrate responsive gene expression after in vitro treatment of wild type vs. rga1 mutant leaves. The figure also shows the upregulation of 7 (OsLFNR2, OsDjC26, OsGS2, OsSTA28 OsCYP37, OsCYP26-2, Zn-finger FYVE) and downregulation of 2 (RCC, OsRPA1) exclusive N-responsive DEGs in the mutant whereas the expression for these DEGs was very close to the baseline ‘0’ in the WT.
Further validation was done in whole plants, using the leaf tissues of WT and mutant plants at active tillering stage grown with 1.5- and 15 mM nitrate. The differential expression of 8 DEGs (Fig. 5b) was validated as upregulated viz. OsSTA28, OsCYP26-2, OsDjC26, OsCAT-A, ALPHA-AMYLASE 3D, OsLhca3, OsFBP1 and OsCDPK23. Downregulated DEGs were also validated namely, RNA Helicase, CLASS I CLP ATPASE B-M, OsGPDH1-1 and Similar to Coatomer gamma subunit. These DEGs belonged to cell cycle and/or DNA synthesis (Os RH16, OsSTA28, OsCYP26-2) and abiotic stress responses (OsDjC26, OsCAT-A & CLASS I CLP ATPASE B-M). Other DEGs belonging to photosynthesis/yield (OsGPDH1-1, ALPHA-AMYLASE 3D, OsLhca3 and OsFBP1) and signalling or protein transport (OsCDPK23, Similar to Coatomer gamma subunit) were also validated. The expression patterns matched with the microarray data and Fold changes of all these DEGs were significantly different in the mutant as compared to the WT (Fig. 5). These results clearly reveal that the RGA1 and N-regulation of the DEGs as observed in excised leaves was also valid in intact plants grown with normal dose of nitrate prescribed in AH solution. They also provide conclusive evidence for RGA1-regulation for novel genes and processes for subtle N-responsive effects in the mutant. On the basis of literature (Kumari, et al. 2021; Neeraja, et al. 2021; Sandhu, et al. 2021), we report 226 NUE-related DEGs (compared to 477 in WT) in the mutant using the nitrate responsive transcriptome (Supplementary Fig. 8 and Supplementary Table 11). We validated the nitrate-responsive expression of 6 upregulated (OsCRY1a, OsLFNR2, YL1, OsGS2, OsFBP1 and α-amylase 3D) and 3 downregulated (OsADH1, OsNYC3 and OsRH10) NUE-related DEGs (Fig. 5).
3.9. Phenotypic and Physiological validation of differential N-response in mutant and WT
Phenotypic data were generated using the same plants used for RT-qPCR validation of DEGs associated with agronomic traits (Fig. 6b), to relate gene expression found in leaves to other organs, traits and overall comparative agronomic performance in WT and mutant plants. For this purpose, 10 days old seedlings were grown hydroponically in AH solution containing either 1.5 or 15 mM nitrate for next 10 days and then their root lengths were recorded (Fig. 7a, c). Under the contrasting doses of nitrate, WT and mutant plants exhibited significant differences in phenotypic and physiological responses in four independent experiments (Fig. 7,8 and Supplementary Fig. 11,12). The duration of treatments was selected based on our previous experiments with WT japonica plants, wherein significant N dose-responsive phenotypic and physiological differences were observed with 20 days old seedlings and thereafter throughout the life cycle (Mandal, et al. 2022). Root lengths were significantly longer, as expected in the WT plants treated with 1.5 mM NO3− relative to 15 mM. However, no significant difference in root length was observed in mutant plants under any nitrate concentration (Fig. 6c). The same trend was also observed for shoot length (Fig. 6d). Similarly heading date and productive tillers were affected by 15 mM NO3− (relative to low N) in the WT but not in the mutant (Fig. 6e,f). These consistent results across multiple growth and yield-related parameters suggest that the mutation in RGA1 may have rendered the plant insensitive to changes in N supply/content, which may explain its higher NUE relative to WT, especially at 15 mM NO3− (Fig. 1b,c).
Total nitrogen and total protein contents were found to be higher in 15 mM NO3− treated plants as compared to the 1.5 mM NO3− treated ones in both the WT and mutant plants. However, the mutants had lower total nitrogen and total protein contents relative to the WT (Fig. 7g,h). Our measurements of relative water content and catalase activity (Supplementary Fig. 9) in these plants indicate higher N-dose sensitivity in WT at 15 mM NO3− as compared to the mutant. Similarly, our data shown in Supplementary Fig. 10 show insignificant differences in P contents at the harvest stage, though the differences in K content between WT and mutant were significant only at 1.5 mM nitrate and not at 15 mM nitrate.
In order to reconfirm that the phenotypic results are solely due to N-response and not any other external effect, we repeated the experiment with higher light intensity (200 µmol m− 2 s− 1) at the plant level in the growth chamber. We also saturated the pots with water to rule out any water deficiency and still obtained similar N-responsive results. Our data in Supplementary Fig. 12 show that RGA1 mutant was insensitive to N-dose while the WT exhibited N-dose sensitivity and decreased plant height. Further, we showed here that RGA1 mutation renders even other agronomically important traits unaffected by N-dose (Fig. 6). Consequently, the inhibition of yield and NUE seen in the WT at 15 mM nitrate did not manifest in the mutant, thus appearing to perform better in terms of yield and NUE-related parameters.
The total number of stomata on leaf surfaces did not show any significant difference attributable to nitrate dose in the wild type plants, however it was lower in the mutant plants at 15 mM NO3− relative to 1.5 mM nitrate (Fig. 7a-c). On the other hand, significant effect of nitrate dose was observed in the stomatal length on the abaxial surfaces of both WT and mutant leaves (Fig. 7d). Moreover, the mutant plants showed larger stomata relative to the WT, as is known in Arabidopsis (Zhang, et al. 2008) but not reported in N-response rice so far to our knowledge. Stomatal conductance and transpiration rate increased with increase in nitrate concentration in the WT plants; while the mutant did not show significant change with respect to N-dose (Fig. 7e,f). However, the mutant plants exhibited lower stomatal conductance and transpiration rates relative to the WT irrespective of the N-dose.
Interestingly, the mutant plants also showed significantly higher chlorophyll content than their corresponding WT irrespective of the nitrate concentration, with visibly darker green leaves of the mutant plants (Fig. 7j). Nitrate dose had significant effect on the chlorophyll content only in mutant plants, as those grown under 15 mM NO3− had significantly higher chlorophyll content than those grown under 1.5 mM NO3− (Fig. 7g). However, nitrate concentration did not significantly affect the photosynthetic rate in the mutants, whereas a significant increase was observed in case of WT plants from low to normal nitrate concentration (15 mM) at both early (Fig. 7g) and late vegetative stages (Supplementary Fig. 11). Additionally, mutant plants showed better water use efficiency (WUE) and intrinsic water use efficiency (WUEi) owing to higher photosynthetic rate and lower stomatal conductance and transpiration rates (Fig. 8h,i and Supplementary Fig. 11).
Abiotic stress tolerance/resistance is one of the best studied roles of RGA1. It also emerged as an important process in the functional annotation of the DEGs in WT and RGA1 to nitrate in this study (Fig. 4). Further physiological examination was done on the effect of nitrate dose (1.5 and 15 mM nitrate) and/or salt stress (120 mM NaCl) on grain yield in the WT and RGA1 mutant. It revealed that despite similar sodium accumulation, the mutant was far less sensitive (or far more tolerant) to both nitrate dose and salt (Fig. 8a&b). However, the lack of additive effect of higher nitrate/salt on yield in the WT as well as in the mutant indicates that nitrate and salt affect yield through the same regulatory step. In other words, RGA1 emerges as a convergence point in the mechanism of salt tolerance and NUE for the first time.
The total nitrogen content in the shoot increased from 1.5 to 15 mM nitrate in both the WT in the mutant, though to a slightly lesser degree in the mutant than in the WT (Fig. 8c). This explains why the mutant is less sensitive to the inhibitory effects of nitrate dose on root/shoot length and yield than the WT and suggests that RGA1 regulate nitrate uptake. Interestingly, the same cannot be said for reduced sensitivity to salt stress in the mutant, as the sodium content remained unchanged between the WT and mutant (Fig. 8a). Total protein contents increased in response to N dose in both WT and the mutant, but were more affected by salt stress in the WT but not significantly affected in the mutant (Fig. 8d). Interestingly, this is not due to reduced sodium intake in the mutant and is inspite of the reduced nitrogen content in the mutant (Fig. 8a) and needs further exploration for other mechanisms. RGA1 mutant plants also showed lower physiological sensitivity than WT to salt stress at both the N-doses and maintained better maximal quantum efficiency of photosystem II (Fv/Fm) photosynthetic rate, water use efficiency and relative water content (Fig. 8e-h).
Overall, these findings on nitrate-responsive phenotypic traits must have a basis in differential gene expression in the mutant plants, as many of the nitrate-responsive DEGs found in this study are functionally linked to such phenotypic traits (Fig. 4, Supplementary Figs. 5 and 6). The lesser number of DEGs with higher foldchange in mutant over WT may be due to the reduced N-sensitivity of the mutant relative to the WT, which may in turn subdue the associated phenotypic effects. PPI network of total DEGs in the mutant for traits like leaf and/or culm and/or root, panicle and/or seed and/or yield and tolerance and/or resistance were mainly enriched with upregulated DEGs (Fig. 9). At least 10 of them have been validated as nitrate responsive by RT-qPCR (Fig. 5), clearly indicating their role in regulating the agronomic response to nitrate through G-protein (RGA1) signalling in rice. They are, OsLFNR2, YL1, OsSTA28, Oslhca3, OsCAT-A, Regulator of chromosome condensation, OsRH16, OsRH10, Class I CLP ATPase B-M and Catomer γ subunit.