Natural variation of OsNPF7.3 regulates alkaline tolerance by modulating ammonium uptake and redistribution in rice

Natural variation of OsNPF7.3 regulates alkaline tolerance by modulating ammonium uptake and redistribution in rice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Natural variation of OsNPF7.3 regulates alkaline tolerance by modulating ammonium uptake and redistribution in rice Fan Zhang, Pingping Li, Jingbing Lu, Guogen Zhang, Song Mei, Qiuyang Yan, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8236504/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Soil alkalization is one of the most severe abiotic stresses constraining rice yields. However, the genetic basis underlying alkaline tolerance of rice remains poorly understood. Here, we used genome-wide association analysis to identify OsNPF7.3 as the candidate gene for qAT4 , which is a major locus associated with alkaline tolerance at rice seedling stage. OsNPF7.3 encodes a nitrate/oligopeptide transporter and acts as a negative regulator of rice alkaline tolerance. A natural variation of 7-bp insertion/deletion in the OsNPF7.3 promoter, affecting the binding affinity of transcription factor OsDOF11, mainly contributes to differential transcriptional levels of OsNPF7.3 , and thus leads to differential alkaline tolerance between japonica and indica subspecies. OsNPF7.3 localizes to the vacuolar membrane and mediates nitrogen transport from older to younger leaves under alkaline stress. Loss of OsNPF7.3 significantly upregulated the expression of nitrogen metabolism-related genes and metabolites, suggesting its regulatory role in nitrogen allocation. Together, these findings reveal an OsDOF11- OsNPF7.3 -nitrogen metabolism regulatory module that connects nitrogen homeostasis to alkaline tolerance, providing a promising target for the development of alkaline-tolerant rice varieties. Biological sciences/Plant sciences/Natural variation in plants Biological sciences/Plant sciences/Plant stress responses/Abiotic alkaline tolerance OsNPF7.3 haplotype analysis nitrogen homeostasis rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Soil salinization and alkalization pose a significant threat to the sustainability of global agriculture. Currently, this issue affects approximately 1.38 billion hectares (10.7% of the Earth's land surface), resulting in a substantial reduction in crop productivity in affected regions 1 . Climate change-induced water scarcity further intensifies this phenomenon, creating a vicious cycle of environmental degradation and reduced crop yields 1 . Based on their physicochemical characteristics, saline-alkaline soils can be categorized into two main types: saline soils with excessive soluble salts (e.g., NaCl, Na 2 SO 4 and other neutral salts), and alkaline soils with high pH levels (resulting from NaHCO 3 and Na 2 CO 3 ). Both soil types induce ionic and osmotic stress in plants, but alkaline soils also create high-pH microenvironments, which are more harmful to plants than salt stress 2 – 4 . The high pH conditions lead to nutrient deficiency, metabolic disorder and disruption of cellular functions in plants 2 , 5 . Rice ( Oryza sativa L.), as a staple crop worldwide, exhibits high sensitivity to alkaline stress. Despite decades of extensive research, the genetic mechanisms underlying alkali tolerance in rice remain poorly understood. There is an urgent need to identify and mechanistically characterize key alkaline tolerance genes to develop a molecular design strategy for improving the alkaline tolerance of rice varieties. To date, only a few rice alkaline tolerance genes have been reported, which are mainly involved in maintaining Na⁺/K⁺ homeostasis and scavenging ROS. For instance, OsBBX17 , a B-box zinc finger transcription factor, negatively regulates Na + /K + homeostasis by transcriptionally repressing OsHAK2 and OsHAK7 under saline-alkaline stress 6 . OsALT1 , encoding a chromatin remodeling ATPase, negatively regulates alkaline tolerance by increasing H 2 O 2 accumulation 7 . The zinc finger protein OsLOL5 improves alkaline tolerance by boosting salicylic acid synthesis and antioxidant-related gene expression 8 . The calcium/calmodulin-dependent protein kinase OsDMI3 positively regulates promoted root elongation under saline-alkaline stress by reducing root Na + and H + influx 9 . Recently, genome-wide association studies (GWAS) revealed AT1 as a key regulator of alkaline tolerance across multiple crop species 10 . Enhanced nitrogen-use efficiency (NUE) significantly improves rice salt tolerance by optimizing nitrogen remobilization processes 11 . Internal recycling of nitrogen becomes particularly critical under stress conditions where external nitrogen uptake is constrained. The spatial and temporal distribution of nitrogen within plants is tightly regulated by specific transporters. The nitrate/peptide transporter family (NPF) (previously called PTR/NRT1 family) plays a central role in nitrogen acquisition and signaling in plants 12 . Emerging evidence suggests that NPF genes may also be involved in abiotic stress responses. For example, transgenic overexpression of cassava MeNPF5.4 in rice simultaneously enhanced grain yield and salt tolerance 13 . Similarly, OsNPF6.1, a regulator of nitrogen-dependent growth in rice, was specifically upregulated in nitrogen-efficient rice varieties under salt stress conditions 11 . Notably, ammonium (NH 4 + ) uptake through NRT1.1 facilitates chloride extrusion and Na + /K + homeostasis maintenance 14 , 15 . OsNRT1.1B/OsNPF6.5 also functions as an abscisic acid (ABA) receptor that activates ABA-responsive genes and may play an important role in environmental adaptation 16 . However, the role of NPF members in conferring alkaline tolerance is still unknown, leaving the underlying molecular mechanisms largely uncharacterized. In this study, we identified OsNPF7.3 as the candidate gene of qAT4 , a major locus associated with alkaline tolerance at rice seedling stage through GWAS. Functionally, OsNPF7.3 acts as a negative regulator of alkaline tolerance without compromising grain yield under normal conditions. A 7-bp insertion/deletion (InDel) in the OsNPF7.3 promoter region leads to differential transcriptional activity mediated by the transcription factor OsDOF11, thereby contributing to the distinct levels of alkaline tolerance between indica and japonica subspecies. Under alkaline stress, OsNPF7.3 mediates NH₄⁺ uptake and blocks the redistribution of nitrogen from older to younger leaves, promoting vacuole storage of NH₄⁺. This vacuolar accumulation disrupts cellular NH₄⁺ homeostasis and amplifies oxidative damage. Highly conserved coding sequence and subspecies-specific functional divergence promoter region of OsNPF7.3 suggest that adaptive evolution has fine-tuned the expression levels of OsNPF7.3 to suit the distinct environmental niches of each subspecies. These findings highlight the potential value of OsNPF7.3 in developing alkaline-tolerant rice varieties. Results GWAS and haplotype analysis identify OsNPF7.3 as a candidate gene for alkaline tolerance To dissect the genetic basis of alkaline tolerance in rice, we evaluated the alkaline tolerance of 306 rice accessions at the seedling stage using three phenotypic indicators, including seedling survival days (SSD), vegetative growth index (VGI), and scores of alkalinity toxicity (SAT) (Supplementary Data 1). GWAS was conducted with 2,652,345 SNPs and the three alkaline tolerance-related traits based on the mixed linear model (Fig. 1 a, Supplementary Fig. 1a). A total of 106 associated SNPs were identified, including 9, 24, and 73 SNPs significantly associated with SAT, SSD, and VGI, respectively (Supplementary Data 2). To reduce redundancy in association signals, adjacent significant SNPs were merged into a single locus based on local linkage disequilibrium (LD) analysis. Consequently, a total of 18 genomic loci associated with alkaline tolerance were identified. Among these loci, qAT4 , a 125.948-kb interval on chromosome 4 (Chr4: 30091090–30217038 bp), exhibited the strongest association signals in GWAS (lead SNP p -value = 9.70E-10) (Fig. 1 a and Supplementary Data 3). According to the annotation of the Nipponbare reference genome in the Rice Annotation Project database ( http://rice.plantbiology.msu.edu/ ), the qAT4 region contains 24 annotated genes (Fig. 1 b and Supplementary Data 4). Among them, 6 annotated genes harbor significant SNPs in their promoter regions or nonsynonymous SNPs in their coding sequences. Haplotype analysis revealed that three ( LOC_Os04g50950 , LOC_Os04g50970 and LOC_Os04g51009 ) of the six genes showed significant differences in alkaline tolerance across different haplotypes (Fig. 1 c, d, Supplementary Fig. 1c-f and Supplementary Data 5). Of the three genes, only LOC_Os04g50950 showed a significantly higher expression level in the roots of the alkali-sensitive accession C1027 than in the alkali-tolerant accession G480 (Supplementary Fig. 1g). LOC_Os04g50950 , also known as OsNPF7.3 , encodes a member of nitrate/peptide transporter family, which is induced by organic nitrogen and plays a role in nitrogen allocation in rice 17 . By analyzing the genetic variants within the promoter region of OsNPF7.3 , two major haplotypes with distinct subspecies-specific distribution were identified. Specifically, OsNPF7.3 Hap1 was predominantly detected in japonica accessions, whereas OsNPF7.3 Hap2 was mainly present in indica accessions (Fig. 1 e). The two haplotypes were significantly associated with alkaline tolerance in rice. OsNPF7.3 Hap1 accessions showed significantly higher SSD and VGI under alkaline stress compared to OsNPF7.3 Hap2 accessions (Fig. 1 c, d). Similarly, japonica accessions showed stronger alkaline tolerance than indica accessions, as evidenced by their significantly higher SAT, SSD, and VGI (Fig. 1 f, g and Supplementary Fig. 1b). Collectively, these results suggest that OsNPF7.3 is an important candidate gene for qAT4 and may contribute to subspecies divergence in alkaline tolerance. Genetic manipulation demonstrates OsNPF7.3 as a negative regulator of alkaline tolerance To verify the function of OsNPF7.3 , knockout (KO) and overexpression (OE) transgenic lines were generated in the ZH11 genetic background. Three KO mutants were obtained, namely KO-1, KO-2, and KO-3. These mutants carried a 13 bp deletion, a 1 bp deletion, and a 1 bp insertion, respectively, resulting in altered and truncated proteins. (Fig. 1 i and Supplementary Fig. 2). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis confirmed that the two OE lines exhibited significantly increased OsNPF7.3 expression levels compared to the wild type (WT) (Fig. 1 j). Under alkaline stress, the KO mutants showed enhanced tolerance compared to WT, with higher survival rates (Fig. 1 h, k), longer roots and greater plant height (Supplementary Fig. 3a, b). In contrast, the OE lines were more sensitive to alkaline stress than WT (Fig. 1 h, l and Supplementary Fig. 3a, b). Additionally, knockout of OsNPF7.3 homolog in maize also confers enhanced alkaline tolerance (Supplementary Fig. 4). Physiological assays further revealed that the rice KO lines suffered less oxidative damage than WT, while the OE lines showed the opposite physiological response (Supplementary Fig. 3c-f). To investigate the underlying mechanism of osnpf7.3 -mediated alkaline tolerance, we measured Na + contents in both shoot and root tissues. The results showed no significant differences in Na + contents between the mutants and WT (Supplementary Fig. 5), suggesting that OsNPF7.3 regulates alkaline tolerance independently of Na + homeostasis. Additionally, under normal conditions, although individual lines of the transgenic material exhibited higher or lower yield-related traits compared to WT, no significant overall difference was observed between the transgenic materials and WT (Supplementary Fig. 6). Together, these results establish OsNPF7.3 as a negative regulator of alkaline tolerance in rice and confirm it as the candidate gene of qAT4 . Promoter variation and OsDOF11-mediated regulation underlie haplotype-specific expression of OsNPF7.3 We first investigated the spatial expression pattern of OsNPF7.3 . Given that sequence variations of OsNPF7.3 were mainly localized in the promoter region (Supplementary Fig. 7), the 2-kb promoters of OsNPF7.3 Hap1 and OsNPF7.3 Hap2 were cloned to drive GUS expression in transgenic rice for activity comparison. GUS staining results revealed that OsNPF7.3 Hap2 exhibited stronger activity than OsNPF7.3 Hap1 (Fig. 2 a, I), and that OsNPF7.3 was predominantly expressed in the vascular tissues of roots and leaf sheaths (Fig. 2 a, II-IV). Consistently, qRT-PCR analysis confirmed that OsNPF7.3 was mainly expressed in roots during the vegetative stage (Fig. 2 b). Moreover, alkali treatment induced stronger expression of OsNPF7.3 in roots than in shoots (Fig. 2 c and Supplementary Fig. 8a), indicating that the response of OsNPF7.3 to alkaline stress is mainly in roots. Similarly, comparative analysis of OsNPF7.3 expression between the two haplotypes showed that OsNPF7.3 expression levels in roots were significantly higher in OsNPF7.3 Hap2 accessions than in OsNPF7.3 Hap1 accessions (Fig. 2 d), whereas no significant difference was detected in shoots (Supplementary Fig. 8b). These results are consistent with the GUS staining driven by the two haplotypes as well as with the SSD and SAT analyses of rice accessions under alkaline stress (Fig. 2 a and Supplementary Fig. 9). To dissect the impact of the sequence variations on promoter activity, we divided the promoter of OsNPF7.3 into three fragments with short overlapping regions, designated P1, P2 and P3 (Fig. 2 e). Transient expression assays in Nicotiana benthamiana leaves showed that the P2 fragment drove the highest luciferase activity among the three fragments, and that the P1 and P2 fragments of OsNPF7.3 Hap2 exhibited stronger transcriptional activity than those of OsNPF7.3 Hap1 (Fig. 2 f and Supplementary Fig. 10). Based on the binding sites prediction of transcription factors (TFs) with PlantRegMap database ( https://plantregmap.gao-lab.org/ ), we identified three members of OsDOF family that were predicted to bind at a variant site where OsNPF7.3 Hap2 harbors a 7-bp insertion. By comparing the predicted binding sites of OsNPF7.3 Hap1 and OsNPF7.3 Hap2 , we found that OsDOF11 was the only member of the OsDOF family that able to bind specifically to OsNPF7.3 Hap2 . We therefore examined the binding affinity and regulatory effect of OsDOF11 on this cis-regulatory element. Yeast one-hybrid assays confirmed that OsDOF11 could bind to the S2 (P2 fragment of OsNPF7.3 Hap2 ) and R2 (P2 fragment of OsNPF7.3 Hap1 ), with a stronger binding to S2 than to R2 (Fig. 2 g). Electrophoretic mobility shift assay (EMSA) further verified stronger binding affinity of OsDOF11 to S2 than to R2 (Fig. 2 h and Supplementary Fig. 11). Dual-luciferase reporter assays showed that co-expression with OsDOF11 significantly enhanced the transcriptional activity of S2 fragment (Fig. 2 i and Supplementary Fig. 12). Taken together, these results suggested that the 7-bp InDel variation in the P2 region of the OsNPF7.3 promoter is responsible for the differential expression of OsNPF7.3 between the two haplotypes. OsDOF11 functions as a transcriptional activator with stronger regulatory effect on OsNPF7.3 Hap2 , leading to higher expression of OsNPF7.3 in OsNPF7.3 Hap2 accessions, which ultimately results in reduced alkaline tolerance. OsNPF7.3 functions as an NH 4 + transporter regulating nitrogen uptake and homeostasis Comparative analysis of the coding sequence (CDS) showed that OsNPF7.3 Hap1 and OsNPF7.3 Hap2 differ by a single synonymous nucleotide substitution, which does not alter the protein sequence (Supplementary Fig. 13, 14). Subcellular localization analysis of OsNPF7.3 in Nicotiana benthamiana leaves revealed that it co-localized with the vacuolar membrane marker protein INT1 (Fig. 3 a and Supplementary Fig. 15). Given that OsNPF7.3 belongs to the nitrate/oligopeptide transporter and may be involved in nitrogen (N) uptake and translocation, we investigated its transport function by heterologous expression in Xenopus laevis oocytes. The oocytes were incubated with 15 NH 4 + and 15 NO 3 − for 3 h under pH 5.5 and pH 8.0 conditions, respectively. Gas chromatography-mass spectrometry (GC-MS) quantification of ¹⁵N retention showed that oocytes expressing OsNPF7.3 accumulated significantly higher levels of ¹⁵NH₄ + compared to water-injected oocytes under pH 8.0 conditions, whereas no significant difference was observed under pH 5.5 conditions, indicating NH₄ + uptake mediated by OsNPF7.3 is dependent on high pH levels (Fig. 3 b, c). In contrast, ¹⁵NO₃ − accumulation was significantly reduced in OsNPF7.3 -expressing oocytes compared to control oocytes under pH 5.5 conditions, while accumulation levels were approximately equal under pH 8.0 conditions, suggesting OsNPF7.3-mediated NO₃ − efflux is preferentially activated under low pH levels (Supplementary Fig. 16). These results suggest that OsNPF7.3 plays a critical role in regulating nitrogen uptake and homeostasis under alkaline stress. To evaluate the effect of alkali treatment on nitrogen redistribution mediated by OsNPF7.3 in rice, we compared the nitrogen content between WT and KO mutant following exposure to the nutrient solution of (¹⁵NH₄) 2 SO 4 under normal and alkaline stress conditions. Under normal conditions, the KO mutant accumulated significantly higher levels of ¹⁵N in all organs except the stem than WT (Fig. 3 d). Under alkaline stress, however, increased ¹⁵N accumulation was only observed in the 3rd leaf (the youngest leaf) and root of KO mutant compared to WT (Fig. 3 e). Furthermore, the ratio of 15 N content in each organ to that in the whole plant was used as an indicator of nitrogen redistribution. Under normal conditions, the KO mutant showed significantly higher 15 N content ratios of the root and the 2nd leaf compared to WT (Fig. 3 f). Under alkaline stress, however, only the 3rd leaf of the KO mutant exhibited a significantly higher 15 N ratio than that of WT (Fig. 3 g). To further analyze ¹⁵N redistribution after uptake, we compared the ratios of 15 N content between different organs, including root, stem, 1st leaf, 2nd leaf and 3rd leaf. Under normal conditions, the KO mutant showed lower stem/root and 3rd leaf/2nd leaf ratios, higher 1st leaf/stem and 2nd leaf/1st leaf ratios, compared to WT. Interestingly, alkali treatment reversed this pattern, with the KO mutant displaying increased 3rd leaf/2nd leaf, 3rd leaf/1st leaf and 3rd leaf/stem ratios (Fig. 3 h-k and Supplementary Fig. 17). These results suggest that OsNPF7.3 functions as a key regulator of nitrogen redistribution under both normal and alkaline stress conditions. Specifically, loss of OsNPF7.3 function facilitates nitrogen redistribution from older to younger leaves during alkaline stress, thereby enhancing the adaptive capacity of rice to alkaline environments. Loss of OsNPF7.3 triggers transcriptomic and metabolomic responses under alkaline stress To dissect the regulatory network underlying osnpf7.3 -mediated alkaline tolerance, we performed time-course transcriptomic and metabolomic analyses on shoot and root tissues of WT and the OsNPF7.3 -KO mutant at 0 h, 2 h, 1 d, 4 d, and 7 d after alkaline stress treatment. Transcriptomic profiling revealed more differentially expressed genes (DEGs) identified in roots than in shoots between the KO mutant and WT (Supplementary Fig. 18a), indicating that transcriptional response to alkaline stress is predominant in roots. No common DEGs were detected across all time points in shoots, whereas 24 DEGs displayed a consistent transcriptional pattern in roots. Metabolomic analysis identified 109, 84, and 87 differentially expressed metabolites (DEMs) in shoots between KO mutant and WT at 0 h, 1 d, and 7d, respectively (Supplementary Fig. 18b). Among these, 41 DEMs were shared between control and stress conditions, and 10 DEMs were common at the two time points after treatment. In roots, 150, 153, and 194 DEMs were detected at 0 h, 1 d and 7 d, respectively, including 37 common DEMs under both control and stress conditions and 34 overlapping DEMs between 1 d and 7 d after treatment (Supplementary Fig. 18b). Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs highlighted processes related to nitrogen transport and reutilization, and stress-defense, including cysteine/methionine metabolism, glutathione metabolism and arginine/proline metabolism (Supplementary Fig. 18c, d). We identified 17 amino acids and their derivatives significantly associated with nitrogen transport (Fig. 4 a). Based on weighted gene co-expression network analysis (WGCNA), we further identified 10 gene modules significantly associated with 17 nitrogen transport-related metabolites (Fig. 4 b-c). GO/KEGG functional enrichment analysis of the top 50 hub genes from each module (Supplementary Data 6, 7) showed 31 terms related to nitrogen transport and stress responses, including ABA signal perception, metabolite synthesis, stress defense, and substance transport (Fig. 4 d). Among these hub genes, 41 showed significant differential expression between KO mutant and WT, with 38 of these DEGs detected in roots at 1 d after stress treatment (Supplementary Data 8). Five of these DEGs are closely related to nitrogen transport and alkaline stress response, including protein phosphatase 2A regulatory subunit B'k gene ( B'κ-PP2A ) 18 , stress-induced chloroplast degradation gene ( OsCV ) 19 , cysteine synthase gene ( OsRCS2 ), glutathione-dependent oxidoreductase CC-type glutaredoxin gene ( OsGRX6 ) 20 , 21 , 22 , and TFIIIA-type zinc finger protein ( ZFP252 ) 23 (Fig. 4 e). These five genes may together improve rice alkaline tolerance through maintaining ion homeostasis, activating stress-protective pathways, and integrating nitrogen-hormone signaling. These integrated omics data collectively indicate that loss of OsNPF7.3 triggers a “nitrogen assimilation-redistribution-defense” regulatory network under alkaline stress, establishing a link between nitrogen homeostasis and alkaline stress adaptation in rice. Evolutionary divergence and breeding selection signatures of OsNPF7.3 in rice To elucidate the origin of the 7-bp InDel in the P2 promoter region of OsNPF7.3 and clarify its transmission patterns during rice breeding, we conducted comprehensive genomic analyses at this locus using over 10542 rice accessions from the RiceSuperPIRdb ( http://www.ricesuperpir.com/ ). Consistent with haplotype analysis results from the GWAS panel, two major haplotypes ( OsNPF7.3 Hap1 and OsNPF7.3 Hap2 ) were identified in promoter region (Fig. 5 a). Haplotype network analysis indicated that OsNPF7.3 Hap1 represents the ancestral haplotype of OsNPF7.3 in rice (Fig. 5 b). Frequency distribution of the two haplotypes showed that OsNPF7.3 Hap1 is mainly enriched in japonica , while OsNPF7.3 Hap2 is widely present in indica (Fig. 5 c). Moreover, high F st values were observed at OsNPF7.3 between indica and japonica subspecies (Fig. 5 g), suggesting that OsNPF7.3 may contribute to indica - japonica differentiation. To investigate whether artificial selection during modern rice breeding over the past decades has affected the genetic diversity of OsNPF7.3 , we compared the frequency differences of the two major haplotypes between landraces and modern varieties. Notably, the alkali-tolerant haplotype OsNPF7.3 Hap1 was apparently favored by artificial selection during modern breeding because its increased or fixed frequencies in modern indica and japonica varieties compared to landraces (Fig. 5 d). By comparing the nucleotide diversity ( π ) and Tajima's D values within a 200-kb region upstream and downstream of OsNPF7.3 between wild rice and the cultivated rice, we found that japonica rice exhibited the lowest genetic diversity at OsNPF7.3 locus (Fig. 5 e) and significantly lower negative Tajima's D values (~-2.5) compared to wild and indica rice (Fig. 5 f). Cross-population composite likelihood ratio (XP-CLR) tests further confirmed stronger selection signals at OsNPF7.3 locus in japonica (Fig. 5 h). These results suggested that OsNPF7.3 has undergone subspecies-specific positive selection during rice domestication and breeding. Discussion Soil alkalization is a complex abiotic stress that severely constrains crop productivity worldwide. Dissecting the genetic basis of alkaline tolerance is therefore crucial for developing stress-tolerant crop varieties. In this study, we unraveled a novel genetic and molecular mechanism of the NPF member OsNPF7.3 underlying alkaline tolerance in rice. Our findings reveal that OsNPF7.3 acts as a key negative regulator of alkaline tolerance (Fig. 1 h-l), and its expression level is controlled by the binding affinity of OsDOF11 to a conserved subspecies-specific cis-regulatory element within its promoter region (Fig. 2 e-i). The OsDOF11-OsNPF7.3 module orchestrates alkaline stress adaptation via precise regulation of inter-organ nitrogen allocation, thereby establishing a molecular link between nutrient homeostasis and abiotic stress tolerance (Fig. 3 ). Additionally, OsNPF7.3 homolog may exert conserved functions in alkaline stress responses across cereal crops (Supplementary Fig. 4). These findings not only advance our fundamental understanding of plant responses to high-pH stress but also identify OsNPF7.3 as a promising genetic target for crop tolerance improvement. High soil pH (alkaline stress) severely impairs nutrient acquisition in plants due to osmotic/ionic disturbances and reduced availability of essential nutrients 24 , 25 . Among nutrient transporters, NPF family proteins have emerged as modulators of abiotic stress tolerance in plant 26 . Overexpression of OsNRT2.4 enhances NO₃⁻ uptake and transport in high pH saline-alkaline soils, alleviating stress-induced growth inhibition 27 . Similarly, NPF2.3 rapidly loads NO₃⁻ into xylem to maintain shoot nitrogen homeostasis under saline-alkaline stress, with loss-of-function mutants displaying reduced salt tolerance 28 . A subset of NPF proteins involved in nitrate transport are localized to the plasma membrane or tonoplast, such as OsNPF4.5, OsNPF7.9, OsNPF6.5/OsNRT1.1B, and OsNPF6.3/NRT1.1A 29–32 . The tonoplast-localized OsNPF7.3 can be induced by organic nitrogen sources, contributing to nitrogen allocation and improving NUE under normal conditions 17 , but reducing NUE under high ammonium supply 33 . In this study, we uncovered the mechanisms underlying these observations and demonstrated that OsNPF7.3 has dual transport activities, facilitating NH₄⁺ uptake under high-pH conditions and NO₃⁻ efflux under normal conditions (Fig. 3 a-c and Supplementary Fig. 16). Physiological and isotopic tracing data further confirmed that the osnpf7.3 knockout mutants avoid excess NH 4 + accumulation by eliminating NH₄⁺-sequestration activity in vacuoles under alkaline stress, and release trapped nitrogen in older leaves and enhance its redistribution to young leaves (Fig. 3 d-k). This suggests that OsNPF7.3 may act as a sensor to high pH (alkaline stress) and then initiate nitrogen remobilization. Transcriptomic and metabolomic profiling of osnpf7.3 knockout mutant revealed the activation of sulfur and glutathione metabolism and accumulation of amino acids and nucleotides, indicating a broad metabolic responses to reinforce alkaline stress adaptation (Fig. 4 and Supplementary Fig. 18). Here, we propose a working model in which the 7-bp natural variation within the OsNPF7.3 promoter distinguishes indica (below) and japonica (above) subspecies, which alters the binding affinity of OsDOF11, leading to differential OsNPF7.3 expression levels, NH₄⁺ transport activity and ultimately distinct alkaline tolerance between subspecies (Fig. 5 i). Our findings support that the ability to avoid excess ammonium accumulation and efficiently redistribute internal nitrogen reserves could be an important adaptive strategy when acquiring nitrogen from the environment is severely impaired due to alkaline stress, highlighting the molecular crosstalk between nutrient signaling and stress responses. OsNPF7.3 exhibits remarkably low genetic diversity in rice germplasm, with only two major haplotypes showing clear differentiation between indica and japonica subspecies. Notably, OsNPF7.3 Hap2 accessions showed significantly higher OsNPF7.3 expression levels than OsNPF7.3 Hap1 accessions in root, indicating a strong positive correlation between OsNPF7.3 expression levels and sensitivity to alkaline stress (Fig. 2 d). This functional divergence between haplotypes is attributed to differential transcriptional activation by OsDOF11. Previous studies reported that OsDOF11 can bind the AAAG cis-element to activate sucrose transporters and SWEET genes, regulating source-sink sucrose partitioning and enhancing nitrogen co-translocation to grains through increased sucrose efflux, achieving high yield and NUE in rice 34 , 35 . In this study, the 7-bp insertion in OsNPF7.3 Hap2 enhances both OsDOF11 binding affinity and promoter transcriptional activity (Fig. 2 e-i), providing a mechanistic explanation for the differential expression and functional divergence between the two haplotypes. Moreover, the phenotypic evaluation of OsDOF11 knockout lines indicated that OsDOF11 negatively regulated rice alkaline tolerance at seedling stage (Supplementary Fig. 19). Collectively, this natural variation in the promoter of OsNPF7.3 appears to be the primary determinant of subspecies-specific differences in alkaline tolerance. The highly conserved coding sequence and subspecies-specific functional divergence promoter region of OsNPF7.3 suggests that strong purifying selection has preserved its core transport function, while its expression levels have been fine-tuned by adaptive evolution of each subspecies for distinct environmental niches (Supplementary Fig. 13, Supplementary Fig. 14 and Fig. 5 a-c). The evolutionary signature of OsNPF7.3 , particularly in japonica accessions from temperate regions prone to soil alkalization, implies that OsNPF7.3 played a key role in rice subspecies divergence by regulating alkaline tolerance (Fig. 5 ). These findings offer valuable insights into the genetic basis of crop environmental adaptation and provide alternative strategies to address future agricultural challenges arising from climate change. Although our experiments were primarily conducted at the seedling stage, a critical developmental phase for rice establishment, future studies should evaluate the role of OsNPF7.3 at the reproductive stage using near-isogenic lines and elucidate the related regulatory network. Together, our findings reveal OsNPF7.3 as a key negative regulator of alkaline tolerance, providing valuable insights and genetic resources for enhancing stress resilience and NUE in crops. Methods Plant materials and growth conditions A total of 306 accessions from the 3K Rice Genomes Project 36 were utilized to evaluate alkaline tolerance at the seedling stage. These accessions included 137 indica , 166 japonica , 1 aus , 1 basmati , and 1 admix accessions (Supplementary Data 1). The wild-type rice variety used in this study was ZH11, which harbored the OsNPF7.3 Hap2 haplotype. The osnpf7.3 knockout mutants were obtained using the CRISPR-Cas9 system. For the construction of OsNPF7.3 overexpression lines, its coding sequence was amplified and cloned into the construct pEXT06/g under the control of ubiquitin gene promoter. Both the OsNPF7.3 overexpression and knockout constructs were transformed into ZH11 background via Agrobacterium-mediated transformation. The vectors were constructed by Wuhan Biorun Bio-Tech Co., Ltd. Rice seeds were surface-sterilized with 1% sodium hypochlorite solution for 30 min, followed by hydroponic germination for 2 d until radicle emergence. For each germplasm and transgenic material, 36 and 72 uniformly germinated seeds were selected for phenotypic evaluation with three replicates (12 and 24 plants per replicate), respectively. The germinated seeds were sown into 96-well PCR plates with perforated bottoms, which were then placed in plastic containers filled with distilled water (pH 5.5). Seedlings grow in a phytotron at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS) in Beijing, under conditions of 14 h light/10 h dark photoperiod (28 ℃/26 ℃) with 70% relative humidity for 7 d, and then the distilled water was replaced with Yoshida nutrient solution 37 . Knockout mutants of Zm00001d026221 , the maize homolog gene of OsNPF7.3 , were also generated using the CRISPR-Cas9 system in the B104 maize inbred line background. The vector was constructed by Wuhan Biorun Bio-Tech Co., Ltd. Seeds were disinfected with a 1% sodium hypochlorite solution for 30 min, then placed in 90 mm petri dishes containing 15 ml of sterile water for germination. For each genotype, 18 germinated seeds were sown in small flower pots (7.5 cm × 7.5 cm) filled with a mixture of nutrient soil and vermiculite (1:1, v/v) for the phenotypic experiment (at least 5 replicates, 3 plants per replicate). Primer sequences used in this study are listed in Supplementary Data 9. Alkaline tolerance evaluation Phenotypic evaluation of alkaline tolerance was carried out following the method described by Chaudhary et al 38 . Briefly, alkaline stress was imposed by Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH 9.5) at the two-leaf-and-one-heart stage, while the control group was maintained in Yoshida nutrient solution (pH 5.5) 37 . The nutrient solutions in both groups were refreshed every 3 d. On the 23rd day of alkaline treatment, SAT of each rice accession was evaluated according to the Standard Evaluation System for Rice 38 . Seedlings were scored into nine grades from SAT 1 (the least damage) to SAT 9 (the most severe damage) based on the severity of stress-induced damage. Accessions with SAT ≤ 3 were classified as alkali-tolerant, those with SAT ≥ 7 as alkali-sensitive. SSD were recorded from the first occurrence of seedling death in each accession until the complete death of all seedlings. VGI was calculated as the ratio of SSD to SAT, which could comprehensively evaluate the alkaline tolerance of rice seedlings. On the 16th day of alkaline stress treatment, 3 seedlings per replicate were randomly selected from each of WT and transgenic materials under both stress and control conditions for measurement of seedling height and root length. On the 18th day of treatment, the phenotypes of WT and transgenic materials were photographed, and the survival rate was recorded. For maize materials, two-leaf and one-heart seedlings were subjected to alkaline stress treatment with 200 mM Na₂CO₃ solution. The survival rate was recorded on the 20th day of the stress treatment. GWAS analysis A total of 2,652,345 SNPs with a missing rate < 0.1 and minor allele frequency (MAF) ≥ 0.05 in the GWAS panel were filtered from the 3K-RG 4.8M SNP dataset 39 by PLINK 40 . The GWAS based on a mixed linear model was performed with EMMAX 41 to identify the associations between SNPs and alkaline tolerance. The kinship matrix was calculated with an identical-by-state matrix using the pruned SNP subset (with the parameter “indep-pairwise 50 10 0.1” in PLINK) as a measure of relatedness between accessions. The eigenvectors of the kinship matrix were calculated using GCTA (with the parameter “-make-grm”) 42 and the first three principal components were used as covariates to control population structure. The effective number of SNPs was calculated by the GEC software 43 , and a suggestive significance threshold of association ( p = 2.51E-06) was determined by the Bonferroni correction method (1/the effective number of SNPs) for claiming significant SNPs. Manhattan plots of the GWAS results were plotted by the R package “qqman” 44 . The significant SNPs within the 300-kb region were considered a locus based on the previously reported linkage disequilibrium (LD) decay in 3KRG 36 . The leading SNP of a locus was defined as the SNP with the lowest p value within a 300-kb region. The locus interval was determined by local LD block analysis of the leading SNP using the LDBlockShow 45 . Physiological measurements At the two-leaf and one-heart stage, ZH11 and transgenic materials were subjected to alkaline stress treatment with Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH = 9.8). Seedlings cultured in Yoshida nutrient solution without Na₂CO₃ served as the control. Both control and stress groups were set up with three biological replicates. After 7 d of treatment, shoot and root samples were collected from both groups. The activities of POD and CAT were determined using assay kits (Suzhou Grace Biotechnology, Suzhou, China) following the manufacturer’s protocols. On the 17th day of alkaline stress, six seedlings of each material under alkaline stress and normal conditions were randomly selected for Na + concentration determination, respectively. The roots were washed to remove residual nutrient solution, then blotted dry with filter paper. Place the shoot and root samples of each material into separate envelopes. Treat at 105 ℃ for 30 min, then adjust the temperature to 80 ℃ for several days, and transfer to test tubes. Add acetic acid solution to the test tubes (20 mL for shoots and 10 mL for roots), and place the test tubes in a constant-temperature shaking water bath at 90 ℃ for 3 h. After cooling, take the supernatant and dilute it. Determine the Na⁺ concentration using a flame photometer. The calculation formula is as follows: Mass fraction of Na⁺ in the sample (mg/g) = C×V×N/(M×1000). C = Element concentration of the sample tested on the instrument (mg/L), V = Volume of acetic acid in the sample extract (mL), N = Dilution factor, M = Sample mass (g). Two biological replicates were set up in the experiment. Agronomic traits measurements Field experiments were conducted with three biological replicates at the Shunyi experimental station of Institute of Crop Sciences, CAAS, in Beijing. Yield-related traits were measured following the methods described by Zhang et al. 46 , including plant height, panicle number per plant (PN), filled grain number per panicle (FGNP), 1000-grain weight (TGW), and grain yield per plant (GYP). At the mature stage, 5 plants per replicate were sampled seperiately for measurement. GYP (g) was defined as the average weight of filled grains per plant. TGW (g) was calculated by dividing GYP by the filled grain number and then multiplying 1000. Three biological replicates were set up in the experiment. RNA extraction and qRT-PCR Total RNA was extracted using TRIzol reagent (Tiangen). 2 µg of RNA was used for first-strand cDNA synthesis using the FastingKing One-Step Genomic DNA Removal and cDNA First-Strand Synthesis Premix Reagent (Tiangen). qRT-PCR was carried out on an ABI 7500 real-time PCR system (Applied Biosystems) using 2 × Taq Universal SYBR qPCR Master Mix Kit (Tiangen). The rice UBQ gene was used as an internal reference to normalize gene expression levels. The relative expression of target genes was calculated by the 2 −∆∆CT method 47 . The qRT-PCR primers were listed in Supplementary Data 9. GUS assay The 2-kb genomic upstream region of OsNPF7.3 transcription start site was amplified and cloned into the pBWA(V)HG vector. The vector was constructed by Wuhan Biorun Bio-Tech Co., Ltd. GUS histochemical staining was conducted on T2 homozygous transgenic rice plants. Roots, leaf sheaths, and leaves of germinated seedlings were incubated in a GUS staining solution (CoolaberBiotech, Beijing, China) at 37°C for 12 h, then decolorized in 75% (v/v) ethanol. Paraffin sections prepared from the stained tissues were imaged by an Olympus BX41 Microscope. Subcellular localization The CDS of OsNPF7.3 without a stop codon was amplified from the cDNA of the alkali-sensitive haplotype and fused to the N-terminus of the green fluorescent protein (GFP) gene in the modified pEZR(K)-LC-GFP vector. The vacuolar membrane (RFP-INT1) vector 48 was provided by Biorun Bio-Tech Co., Ltd. After large-scale plasmid extraction, the recombinant vector was transformed into Nicotiana benthamiana leaves by Agrobacterium tumefaciens -mediated infiltration. GFP fluorescence was observed using a confocal microscope (Nikon-A). Dual-luciferase assay Dual-luciferase assay was performed to investigate the transcriptional activity of the promoter sequences of the two OsNPF7.3 haplotypes. Promoter fragments of OsNPF7.3 from alkaline-tolerant ( OsNPF7.3 Hap1 ) and alkaline-sensitive ( OsNPF7.3 Hap2 ) accessions were amplified and fused into the modified pLZ004 vector, which contains the firefly luciferase (LUC) gene as the reporter and the renilla luciferase (REN) gene as the internal control. The full-length CDS of OsDOF11 was cloned into the pEZR(K)-LC-GFP vector to generate the effector construct. The reporter and effector plasmids were co-transformed into Agrobacterium tumefaciens GV3101 and co-infiltrated into Nicotiana benthamiana leaves for 48 h. LUC and REN luciferase activities were surveyed using the Dual-Luciferase Reporter Assay System (Promega). At least four independent infiltrations were performed for each sample, and the relative LUC activity was calculated as the ratio of LUC to REN activity. Transient transactivation with the reporters and the empty vector pLZ004 was used as a control. All primers used for these constructs are listed in Supplementary Data 9. Yeast one-hybrid assay Yeast one-hybrid (Y1H) assay was conducted to verify the direct binding of OsDOF11 to the OsNPF7.3 promoter. The promoters of OsNPF7.3 from alkaline-tolerant ( OsNPF7.3 Hap1 ) and alkaline-sensitive ( OsNPF7.3 Hap2 ) genotypes were inserted into the pLacZi2µ vector to generate the reporter constructs p OsNPF7.3 Hap1 − P2 :: LacZ and p OsNPF7.3 Hap2 − P2 :: LacZ , respectively. The full-length CDS of OsDOF11 was amplified from the cDNA of an alkaline-sensitive accession and cloned into the pB42AD vector to generate the AD-OsDOF11 effector construct. The effector and reporter plasmids were co-transformed into the yeast strain EGY48. Transformants were grown on SD/-Trp-Ura dropout medium supplemented with 20 mg/mL X-gal (5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside) (Clontech). All primers used for these constructs are listed in Supplementary Data 9. EMSA The full-length cDNA of OsDOF11 was cloned into the pET28a vector, which contains a histidine (His) tag for protein purification. The recombinant His-OsDOF11 protein and the empty His-tag protein (negative control) were expressed in Escherichia Coli Strain BL21 (DE3) and purified using genes and according to the manufacturer’s instructions. The 33-bp probes containing the OsDOF11 binding motifs of OsNPF7.3 Hap1 and OsNPF7.3 Hap2 were synthesized and labeled with Cy5 (Beijing Tsingke Biotech Co., Ltd.). EMSA was performed using the Light Shift Chemiluminescent EMSA Kit (Thermo Scientific) following the manufacturer’s protocol. Primer and probe sequences are listed in Supplementary Data 9. Functional analysis of OsNPF7.3 in Xenopus laevis oocytes The CDS of OsNPF7.3 was cloned into the pGEMHE oocyte expression vector. Capped RNA (cRNA) was synthesized from 1 µg of linearized plasmid DNA using the mMESSAGE mMACHINE T7 kit (Ambion) according to the manufacturer’s recommendations. The quality of the synthesized cRNA was checked by agarose gel electrophoresis. Each Xenopus laevis oocyte was injected with 23 ng of cRNA. Injected oocytes were incubated in ND96 solution (96 mM NaCl, 2 mM KCl, 1 mM MgCl 2 , 1.8 mM CaCl 2 , 10 mM HEPES/NaOH, pH 7.4) supplemented with 10 µg l − 1 penicillin and streptomycin at 18°C for 2 d. Oocytes were then incubated for 3 h in ND96 solution containing specific concentrations of 15 N-labeled substrates at a defined pH. After incubation, oocytes were thoroughly washed with ND96 solution, placed in tin boats, and dried at 65°C. Each oocyte was treated as a biological replicate for N content determination 31 , 49 . 15 N uptake assay under alkaline stress Seedlings of ZH11 and osnpf7.3 mutants were cultivated in Yoshida nutrient solution for 20 d. Then, the nutrient solution was replaced with Yoshida nutrient solution containing (¹⁵NH₄)₂SO₄ instead of (NH₄)₂SO₄. After 12 d alkali stress treatment with 0.15% Na 2 CO 3 (pH = 9.5), roots, stems, the 1st, 2nd, and 3rd leaves were collected, dried, and pulverized for ¹⁵N content determination. The samples without alkaline stress treatment served as the control. The ¹⁵N content in each sample was determined using an Isotope Ratio Mass Spectrometer System (Model: Flash 2000 HT; Thermo Fisher Scientific). The ¹⁵N accumulation (µmol 15 N g − 1 dry weight, DW), relative ¹⁵N redistribution ratio ( 15 N ratio, %) 27,31 and the ratios of 15 N concentration between different parts 50 were calculated. Transcriptome and metabolome analysis ZH11 and osnpf7.3 knockout mutant (KO-1) were treated with Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH 9.8) at the two-leaf and one-heart stage. Control seedlings were cultured in Yoshida nutrient solution without Na₂CO₃. Both groups were set up with three biological replicates. Samples were collected at 0 h, 2 h, 1 d, 4 d, and 7 d after alkaline treatment, immediately flash-frozen in liquid nitrogen, and stored at -80°C. For transcriptome, total RNA was extracted with ethanol precipitation and CTAB-PBIOZOL, and dissolved in 50 µL DEPC-treated water. RNA was identified and quantified were determined using a Qubit fluorescence quantifier and a Qsep400 high-throughput biofragment analyzer, respectively. The mRNA library construction and paired-end sequencing on Illumina platform were conducted by Metware Biotechnology Inc., Ltd. Raw sequencing reads were filtered to remove adapters, low-quality reads (Q ≤ 20), and reads containing more than 10% unknown bases. Clean reads were mapped to the Nipponbare reference genome (IRGSP-1.0) using HISAT2 51 software. The number of reads mapped to each gene was counted using featureCounts 52 software. Differentially expressed genes (DEGs) between KO-1 and WT under control and stress conditions were identified using DESeq2 53 software with the criteria of |log2(Fold Change)| ≥ 1 and FDR < 0.05. For metabolome, untargeted metabolomics analysis of samples collected at 0h, 1d, and 7d was performed on an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform by Metware Biotechnology Inc., Ltd. Sample preparation was as follows: 50 mg sample powder extracted with 1200 µL of -20°C pre-cooled 70% (v/v) methanol. Vortex once every 30 min for 30 sec, for a total of 6 times. After centrifugation (rotation speed 12000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 µm pore size) and stored in the injection vial for UPLC-MS/MS analysis. The supernatant was filtered through a 0.22 µm membrane and analyzed by UPLC-MS/MS. Peak detection and metabolite annotation were performed using XCMS 54 software with reference to the Metware database. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to visualize metabolic differences between groups, and Variable Importance in Projection (VIP) values were calculated. Differential metabolites between KO-1 and ZH11 were identified with the criteria of VIP > 1 and p < 0.05. Population genetic analysis SNPs within the 1-kb upstream region of the OsNPF7.3 from the 3K rice germplasm resources were downloaded using the RFGB v2.0 database 55 and used for haplotype analysis. Haplotypes carried by at least 30 rice accessions were defined as the major haplotypes. The haplotype network was constructed based on the 7 bp InDel using the R package geneHapR 56 . Upstream haplotypes of OsNPF7.3 in 5703 rice accessions were retrieved from the Molecular Breeding Knowledge base ( http://www.mbkbase.org/rice ) 57 . We also performed population genetic analysis on the genomic sequences of OsNPF7.3 and its flanking 200-kb regions in 3359 japonica , 5295 indica , and 412 wild rice accessions from the Rice Super-Population Variation Map database 58 . Nucleotide diversity ( π ), fixation index ( F ) between populations, and Tajima’s D were calculated for each 5-kb window with a 1-kb step size using VCFtools software (v0.1.16) 59 . The cross-population composite likelihood ratio test (XP-CLR) was carried out using the Python package XPCLR 60 for each 5-kb window with a 1-kb step size and an LD of 0.95. Declarations Data availability Data supporting the findings of this work are available within the paper and its Supplementary Information files. The transcriptome data have been uploaded to have been deposited in the Genome Sequence Archive 61 in National Genomics Data Center 62 , China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (CRA032825) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa . 3K-RG 4.8M SNP dataset can be downloaded from the Rice SNP-Seek Database [ https://snp-seek.irri.org ]. The data of the rice genomic SNPs used in the genetic diversity analysis can be downloaded from MBKbase [ https://www.mbkbase.org/rice/genotype ]. Source data are provided with this paper. Competing interests The authors declare no competing interests. Author contributions F.Z. directed the project. P.L., J.L., S.M., and X.W. performed the experiments. P.L., J.L., S.M., and G.Z. analyzed the data in GWAS and genetic diversity analysis. P.L., J.L., G.Z., and K.C. conducted and managed the field work. Q.Y., J.J., J.A. M.L., L.T., and Y.W. participated in the experiments. F.Z., W.W., and D.L. planned and designed the research. X.Z., J.X., Z.L., D.L., and F.Z. interpreted the data. P.L., D.L., and F.Z. wrote the paper and finalized the paper. All the coauthors approved the paper. Acknowledgements This work was funded by the National Key Research and Development Program of China (2023YFF1000400), the National Natural Science Foundation of China (32401827), the Nanfan Special Project, CAAS (YBXM2426), the Central Public-interest Scientific Institution Basal Research Fund (Y2025YC13), and the Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-CSIAF-202303). We thank Dr. Legong Li (Capital Normal University) for the technical support in Xenopus laevis oocytes analysis. The funding agencies had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. References Srarfi, F. & Majar, A. Global status of salt-affected soils. 83-107, (2024). Fang, S., Hou, X. & Liang, X. 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Supplementary Files SupplementaryData.xlsx Supplementary Data 1, Supplementary Data 2, Supplementary Data 3, Supplementary Data 4, Supplementary Data 5, Supplementary Data 6, Supplementary Data 7, Supplementary Data 8 SupplementaryFigures.docx Supplementary Figures Additionalinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":5039591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsNPF7.3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e as the candidate gene for alkaline tolerance in rice. a \u003c/strong\u003eManhattan plot of GWAS results for alkaline tolerance (SSD and VGI) at seedling stage. Red arrows point to the major associated locus \u003cem\u003eqAT4\u003c/em\u003e. \u003cstrong\u003eb\u003c/strong\u003e Local manhattan plot and LD block of \u003cem\u003eqAT4\u003c/em\u003e. \u003cstrong\u003ec, d\u003c/strong\u003e Phenotypic distribution of SSD (\u003cstrong\u003ec\u003c/strong\u003e) and VGI (\u003cstrong\u003ed\u003c/strong\u003e) in the two major haplotypes of \u003cem\u003eOsNPF7.3\u003c/em\u003e. \u003cstrong\u003ee\u003c/strong\u003e Frequency distribution of the two haplotypes of \u003cem\u003eOsNPF7.3\u003c/em\u003e in \u003cem\u003eindica\u003c/em\u003eand\u003cem\u003e japonica\u003c/em\u003e subpopulations. \u003cstrong\u003ef\u003c/strong\u003e, \u003cstrong\u003eg\u003c/strong\u003e Phenotypic distribution of SSD (\u003cstrong\u003ef\u003c/strong\u003e) and VGI (\u003cstrong\u003eg\u003c/strong\u003e) in \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003esubpopulations. Statistical significance was calculated by two-tailed Student’s \u003cem\u003et\u003c/em\u003e-tests in \u003cstrong\u003ed-g\u003c/strong\u003e. \u003cstrong\u003eh\u003c/strong\u003e Performance of WT (ZH11), \u003cem\u003eOsNPF7.3\u003c/em\u003e knockout mutants (KO-1, KO-2, KO-3) and \u003cem\u003eOsNPF7.3\u003c/em\u003e overexpression lines (OE-1 and OE-2) under normal and alkaline stress condidtions. Scale bar = 5 cm. \u003cstrong\u003ei\u003c/strong\u003e Sequences of \u003cem\u003eOsNPF7.3\u003c/em\u003eCRISPR-knockout lines. \u003cstrong\u003ej \u003c/strong\u003eExpression level of \u003cem\u003eOsNPF7.3\u003c/em\u003e in OE lines. \u003cstrong\u003ek\u003c/strong\u003e, \u003cstrong\u003el\u003c/strong\u003e. Survival rate of WT, KO and OE lines. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e = 3). Asterisks indicate statistical significance by two-tailed Student’s \u003cem\u003et\u003c/em\u003e-tests (*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/6be11f5ab648c98233aba090.jpg"},{"id":97667413,"identity":"ef14be77-2f73-468b-bd15-2180f20ce600","added_by":"auto","created_at":"2025-12-08 09:23:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3644604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression patterns of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsNPF7.3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and the effect of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsNPF7.3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e promoter variations on OsDOF11 binding affinity. a\u003c/strong\u003e GUS staining. (I) Whole plant. (II) Longitudinal section of root. (III and IV) Cross-section of root and leaf sheath. Scale bar = 50 μm. \u003cstrong\u003eb\u003c/strong\u003e Relative expression levels of \u003cem\u003eOsNPF7.3\u003c/em\u003e in different parts of ZH11 at different growth stages. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e = 3). \u003cstrong\u003ec\u003c/strong\u003e Expression dynamics of \u003cem\u003eOsNPF7.3\u003c/em\u003e in roots of ZH11 under alkaline stress for different time points. Data represent mean ± s.d. (\u003cem\u003en \u003c/em\u003e= 3), Asterisks indicate statistical significance by two-tailed Student’s \u003cem\u003et\u003c/em\u003e-tests (***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003ed\u003c/strong\u003e Expression levels of \u003cem\u003eOsNPF7.3\u003c/em\u003e in roots of accessions carrying \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand\u003csup\u003e \u003c/sup\u003e\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e after alkaline stress for 7 d. Data represent mean ± s.d. (\u003cem\u003en \u003c/em\u003e= 3) Statistical significance was calculated by two-tailed Student’s \u003cem\u003et\u003c/em\u003e-tests. \u003cstrong\u003ee\u003c/strong\u003e A schematic diagram of the \u003cem\u003eOsNPF7.3 \u003c/em\u003epromoter\u003cem\u003e \u003c/em\u003efragments. \u003cstrong\u003ef\u003c/strong\u003e Transient dual-luciferase reporter assays of promoter fragments P1-P3 in \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves. Representative luminescence image was shown on the left and the quantification of LUC/REN ratios was shown on the right. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e = 6). Asterisks indicate statistical significance by two-tailed Student’s\u003cem\u003e t\u003c/em\u003e-tests (**\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003eg\u003c/strong\u003e Yeast one-hybrid assay results showing the interaction between OsDOF11 and the P2 region of \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter. This P2 is from -1006 bp to -850 bp. \u003cstrong\u003eh\u003c/strong\u003e EMSA for the binding of recombinant HIS-TF-DOF11 protein to the P2 region of \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter. \u003cstrong\u003ei\u003c/strong\u003e Transactivation analysis of the \u003cem\u003eOsNPF7.3\u003c/em\u003e-P2 transcription by OsDOF11 in \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e = 5). Asterisks indicate statistical significance by two-tailed Student’s\u003cem\u003e t\u003c/em\u003e-tests (***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, n.s. indicates no significant difference).\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/0b607125f901707473bb006c.jpg"},{"id":97666446,"identity":"bee3ccbd-ef88-4c2a-941f-7999b71784a4","added_by":"auto","created_at":"2025-12-08 09:21:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3761247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOsNPF7.3 subcellular localization and its effect on NH\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e+ \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003euptake and redistribution of nitrogen under alkaline stress. a\u003c/strong\u003e Subcellular location of OsNPF7.3 in \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves and pixel intensity profile analysis of fluorescence signals. INT1 served as a vacuolar membrane marker. Scale bar = 40 μm. The yellow line indicates the position where the intensity profile was taken. Each point along the yellow line represents a pixel position, and the corresponding intensity is plotted in the right graph. \u003cstrong\u003eb, c\u003c/strong\u003e NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e uptake analysis of OsNPF7.3 in \u003cem\u003eXenopus laevis\u003c/em\u003e oocytes. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e ≥ 5). Asterisks indicate statistical significance by two-tailed Student’s\u003cem\u003e t\u003c/em\u003e-tests (***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003ed, e\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eDetection of the \u003csup\u003e15\u003c/sup\u003eN accumulation in different organs of \u003cem\u003eosnpf7.3\u003c/em\u003e mutant (KO) and WT plants under normal (\u003cstrong\u003ed\u003c/strong\u003e) and alkaline stress conditions (\u003cstrong\u003ee\u003c/strong\u003e). \u003cstrong\u003ef, g\u003c/strong\u003e Relative \u003csup\u003e15\u003c/sup\u003eN redistribution ratio in different organs of KO and WT plants under normal (\u003cstrong\u003ef\u003c/strong\u003e) and alkaline stress conditions (\u003cstrong\u003eg\u003c/strong\u003e). \u003cstrong\u003eh-k\u003c/strong\u003e Ratios of \u003csup\u003e15\u003c/sup\u003eN between different organs. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e ≥ 3) in \u003cstrong\u003ed-k\u003c/strong\u003e. Asterisks indicate statistical significance by two-tailed Student’s\u003cem\u003e t\u003c/em\u003e-tests (*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, n.s. indicates no significant difference).\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/cfe5d46b3bfdeaf26eebef1e.jpg"},{"id":97412048,"identity":"8e48c549-248f-4abc-b27c-3af8af63da0f","added_by":"auto","created_at":"2025-12-04 05:51:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5469384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated multi-omics analysis reveals the regulatory network of nitrogen metabolic responses under alkaline stress. a\u003c/strong\u003e Heatmap of 17 amino acids and their derivatives closely related to nitrogen transport between KO and WT plants in shoot and root metabolites under alkaline stress (0 d, 1 d, 7 d). L, shoot. T, root. \u003cstrong\u003eb \u003c/strong\u003eGene model clustering identified by WGCNA analysis. \u003cstrong\u003ec \u003c/strong\u003eCorrelation between gene model and 17 amino acid and their derivative metabolites. Asterisks indicate significant correlation by Pearson correlation test (*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). \u003cstrong\u003ed \u003c/strong\u003eGO biological process and KEGG pathway enrichment analysis of 404 hub genes from 10 gene modules significant associated with 17 amino acid and their derivative metabolites (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in \u003cstrong\u003ec\u003c/strong\u003e). \u003cstrong\u003ee\u003c/strong\u003e Five genes related to nitrogen transport and alkaline stress that showed significant differences in expression between KO and WT in roots under alkaline stress for 1 d. Data represent mean ± s.d. (\u003cem\u003en\u003c/em\u003e = 3). Asterisks indicate statistical significance by two-tailed Student’s\u003cem\u003e t\u003c/em\u003e-tests (*\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, n.s. indicates no significant difference).\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/bfcea0753bf65f19e9e661e5.jpg"},{"id":97667068,"identity":"0af75423-76ec-435b-9a2a-6003581720bc","added_by":"auto","created_at":"2025-12-08 09:22:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3694091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic diversity and evolutionary analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOsNPF7.3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in rice. a\u003c/strong\u003e Two major haplotypes of \u003cem\u003eOsNPF7.3\u003c/em\u003e. \u003cstrong\u003eb\u003c/strong\u003e Haplotype network of \u003cem\u003eOsNPF7.3\u003c/em\u003e. \u003cstrong\u003ec\u003c/strong\u003e Frequencies of the two major haplotypes of \u003cem\u003eOsNPF7.3\u003c/em\u003e in different rice populations. \u003cstrong\u003ed\u003c/strong\u003e Frequency distribution of the two \u003cem\u003eOsNPF7.3 \u003c/em\u003ehaplotypes in landrace and modern varieties across different rice populations. \u003cstrong\u003ee-h \u003c/strong\u003ePopulation genetics analysis of the genomic region surrounding \u003cem\u003eOsNPF7.3\u003c/em\u003e in different rice populations, including nucleotide diversity (\u003cstrong\u003ee\u003c/strong\u003e), Tajima’s \u003cem\u003eD\u003c/em\u003e (\u003cstrong\u003ef\u003c/strong\u003e), \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (\u003cstrong\u003eg\u003c/strong\u003e), and XP-CLR (\u003cstrong\u003eh\u003c/strong\u003e). Red arrows point to the position of \u003cem\u003eOsNPF7.3\u003c/em\u003e in \u003cstrong\u003ee-h\u003c/strong\u003e. \u003cstrong\u003ei \u003c/strong\u003eThe proposed model in which a 7-bp natural variation in the promoter of \u003cem\u003eOsNPF7.3\u003c/em\u003e between sensitive\u003cem\u003e \u003c/em\u003e(below) and tolerant\u003cem\u003e \u003c/em\u003e(above) alters OsDOF11 binding affinity, leading to differential \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels, NH₄⁺ transport activity, nitrogen redistribution, and ultimately divergent alkaline tolerance phenotypes.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/5e09cb5f2fa2d15a679ef347.jpg"},{"id":98775711,"identity":"0e7fe68f-e52b-448b-ba3c-941be57a9339","added_by":"auto","created_at":"2025-12-22 12:20:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23175583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/d0e081b4-eb2d-40fe-a588-e54835b232b5.pdf"},{"id":97412044,"identity":"5791abcd-ac3d-4e68-8ef1-21dacd76bf0e","added_by":"auto","created_at":"2025-12-04 05:51:25","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1225959,"visible":true,"origin":"","legend":"Supplementary Data 1, Supplementary Data 2, Supplementary Data 3, Supplementary Data 4, Supplementary Data 5, Supplementary Data 6, Supplementary Data 7, Supplementary Data 8","description":"","filename":"SupplementaryData.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/42ab248b5ff0daa79d03645c.xlsx"},{"id":97412054,"identity":"5004d2e5-67b5-4f6a-8291-d3d37a10bfd2","added_by":"auto","created_at":"2025-12-04 05:51:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10716070,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/96468e2b6e7da4b0bcbd3c18.docx"},{"id":97412043,"identity":"b6e78d80-851f-4d45-be9a-19b433f38e5b","added_by":"auto","created_at":"2025-12-04 05:51:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17970,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8236504/v1/b9e5a8469b5a5f4bb4e9995c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Natural variation of OsNPF7.3 regulates alkaline tolerance by modulating ammonium uptake and redistribution in rice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil salinization and alkalization pose a significant threat to the sustainability of global agriculture. Currently, this issue affects approximately 1.38\u0026nbsp;billion hectares (10.7% of the Earth's land surface), resulting in a substantial reduction in crop productivity in affected regions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Climate change-induced water scarcity further intensifies this phenomenon, creating a vicious cycle of environmental degradation and reduced crop yields\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Based on their physicochemical characteristics, saline-alkaline soils can be categorized into two main types: saline soils with excessive soluble salts (e.g., NaCl, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and other neutral salts), and alkaline soils with high pH levels (resulting from NaHCO\u003csub\u003e3\u003c/sub\u003e and Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e). Both soil types induce ionic and osmotic stress in plants, but alkaline soils also create high-pH microenvironments, which are more harmful to plants than salt stress\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The high pH conditions lead to nutrient deficiency, metabolic disorder and disruption of cellular functions in plants\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), as a staple crop worldwide, exhibits high sensitivity to alkaline stress. Despite decades of extensive research, the genetic mechanisms underlying alkali tolerance in rice remain poorly understood. There is an urgent need to identify and mechanistically characterize key alkaline tolerance genes to develop a molecular design strategy for improving the alkaline tolerance of rice varieties. To date, only a few rice alkaline tolerance genes have been reported, which are mainly involved in maintaining Na⁺/K⁺ homeostasis and scavenging ROS. For instance, \u003cem\u003eOsBBX17\u003c/em\u003e, a B-box zinc finger transcription factor, negatively regulates Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e homeostasis by transcriptionally repressing \u003cem\u003eOsHAK2\u003c/em\u003e and \u003cem\u003eOsHAK7\u003c/em\u003e under saline-alkaline stress\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eOsALT1\u003c/em\u003e, encoding a chromatin remodeling ATPase, negatively regulates alkaline tolerance by increasing H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e accumulation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The zinc finger protein OsLOL5 improves alkaline tolerance by boosting salicylic acid synthesis and antioxidant-related gene expression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The calcium/calmodulin-dependent protein kinase OsDMI3 positively regulates promoted root elongation under saline-alkaline stress by reducing root Na\u003csup\u003e+\u003c/sup\u003e and H\u003csup\u003e+\u003c/sup\u003e influx\u003csup\u003e9\u003c/sup\u003e. Recently, genome-wide association studies (GWAS) revealed AT1 as a key regulator of alkaline tolerance across multiple crop species\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEnhanced nitrogen-use efficiency (NUE) significantly improves rice salt tolerance by optimizing nitrogen remobilization processes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Internal recycling of nitrogen becomes particularly critical under stress conditions where external nitrogen uptake is constrained. The spatial and temporal distribution of nitrogen within plants is tightly regulated by specific transporters. The nitrate/peptide transporter family (NPF) (previously called PTR/NRT1 family) plays a central role in nitrogen acquisition and signaling in plants\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that NPF genes may also be involved in abiotic stress responses. For example, transgenic overexpression of cassava \u003cem\u003eMeNPF5.4\u003c/em\u003e in rice simultaneously enhanced grain yield and salt tolerance\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, OsNPF6.1, a regulator of nitrogen-dependent growth in rice, was specifically upregulated in nitrogen-efficient rice varieties under salt stress conditions\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Notably, ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) uptake through NRT1.1 facilitates chloride extrusion and Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e homeostasis maintenance\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. OsNRT1.1B/OsNPF6.5 also functions as an abscisic acid (ABA) receptor that activates ABA-responsive genes and may play an important role in environmental adaptation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the role of NPF members in conferring alkaline tolerance is still unknown, leaving the underlying molecular mechanisms largely uncharacterized.\u003c/p\u003e\u003cp\u003eIn this study, we identified \u003cem\u003eOsNPF7.3\u003c/em\u003e as the candidate gene of \u003cem\u003eqAT4\u003c/em\u003e, a major locus associated with alkaline tolerance at rice seedling stage through GWAS. Functionally, OsNPF7.3 acts as a negative regulator of alkaline tolerance without compromising grain yield under normal conditions. A 7-bp insertion/deletion (InDel) in the \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter region leads to differential transcriptional activity mediated by the transcription factor OsDOF11, thereby contributing to the distinct levels of alkaline tolerance between \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e subspecies. Under alkaline stress, OsNPF7.3 mediates NH₄⁺ uptake and blocks the redistribution of nitrogen from older to younger leaves, promoting vacuole storage of NH₄⁺. This vacuolar accumulation disrupts cellular NH₄⁺ homeostasis and amplifies oxidative damage. Highly conserved coding sequence and subspecies-specific functional divergence promoter region of \u003cem\u003eOsNPF7.3\u003c/em\u003e suggest that adaptive evolution has fine-tuned the expression levels of \u003cem\u003eOsNPF7.3\u003c/em\u003e to suit the distinct environmental niches of each subspecies. These findings highlight the potential value of \u003cem\u003eOsNPF7.3\u003c/em\u003e in developing alkaline-tolerant rice varieties.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eGWAS and haplotype analysis identify\u003c/b\u003e \u003cb\u003eOsNPF7.3\u003c/b\u003e \u003cb\u003eas a candidate gene for alkaline tolerance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo dissect the genetic basis of alkaline tolerance in rice, we evaluated the alkaline tolerance of 306 rice accessions at the seedling stage using three phenotypic indicators, including seedling survival days (SSD), vegetative growth index (VGI), and scores of alkalinity toxicity (SAT) (Supplementary Data 1). GWAS was conducted with 2,652,345 SNPs and the three alkaline tolerance-related traits based on the mixed linear model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Supplementary Fig.\u0026nbsp;1a). A total of 106 associated SNPs were identified, including 9, 24, and 73 SNPs significantly associated with SAT, SSD, and VGI, respectively (Supplementary Data 2). To reduce redundancy in association signals, adjacent significant SNPs were merged into a single locus based on local linkage disequilibrium (LD) analysis. Consequently, a total of 18 genomic loci associated with alkaline tolerance were identified. Among these loci, \u003cem\u003eqAT4\u003c/em\u003e, a 125.948-kb interval on chromosome 4 (Chr4: 30091090\u0026ndash;30217038 bp), exhibited the strongest association signals in GWAS (lead SNP \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;9.70E-10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Supplementary Data 3). According to the annotation of the Nipponbare reference genome in the Rice Annotation Project database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rice.plantbiology.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rice.plantbiology.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the \u003cem\u003eqAT4\u003c/em\u003e region contains 24 annotated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Supplementary Data 4). Among them, 6 annotated genes harbor significant SNPs in their promoter regions or nonsynonymous SNPs in their coding sequences. Haplotype analysis revealed that three (\u003cem\u003eLOC_Os04g50950\u003c/em\u003e, \u003cem\u003eLOC_Os04g50970\u003c/em\u003e and \u003cem\u003eLOC_Os04g51009\u003c/em\u003e) of the six genes showed significant differences in alkaline tolerance across different haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d, Supplementary Fig.\u0026nbsp;1c-f and Supplementary Data 5). Of the three genes, only \u003cem\u003eLOC_Os04g50950\u003c/em\u003e showed a significantly higher expression level in the roots of the alkali-sensitive accession C1027 than in the alkali-tolerant accession G480 (Supplementary Fig.\u0026nbsp;1g). \u003cem\u003eLOC_Os04g50950\u003c/em\u003e, also known as \u003cem\u003eOsNPF7.3\u003c/em\u003e, encodes a member of nitrate/peptide transporter family, which is induced by organic nitrogen and plays a role in nitrogen allocation in rice\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy analyzing the genetic variants within the promoter region of \u003cem\u003eOsNPF7.3\u003c/em\u003e, two major haplotypes with distinct subspecies-specific distribution were identified. Specifically, \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e was predominantly detected in \u003cem\u003ejaponica\u003c/em\u003e accessions, whereas \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e was mainly present in \u003cem\u003eindica\u003c/em\u003e accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The two haplotypes were significantly associated with alkaline tolerance in rice. \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e accessions showed significantly higher SSD and VGI under alkaline stress compared to \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d). Similarly, \u003cem\u003ejaponica\u003c/em\u003e accessions showed stronger alkaline tolerance than \u003cem\u003eindica\u003c/em\u003e accessions, as evidenced by their significantly higher SAT, SSD, and VGI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, g and Supplementary Fig.\u0026nbsp;1b). Collectively, these results suggest that \u003cem\u003eOsNPF7.3\u003c/em\u003e is an important candidate gene for \u003cem\u003eqAT4\u003c/em\u003e and may contribute to subspecies divergence in alkaline tolerance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic manipulation demonstrates\u003c/b\u003e \u003cb\u003eOsNPF7.3\u003c/b\u003e \u003cb\u003eas a negative regulator of alkaline tolerance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo verify the function of \u003cem\u003eOsNPF7.3\u003c/em\u003e, knockout (KO) and overexpression (OE) transgenic lines were generated in the ZH11 genetic background. Three KO mutants were obtained, namely KO-1, KO-2, and KO-3. These mutants carried a 13 bp deletion, a 1 bp deletion, and a 1 bp insertion, respectively, resulting in altered and truncated proteins. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei and Supplementary Fig.\u0026nbsp;2). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis confirmed that the two OE lines exhibited significantly increased \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels compared to the wild type (WT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ej). Under alkaline stress, the KO mutants showed enhanced tolerance compared to WT, with higher survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh, k), longer roots and greater plant height (Supplementary Fig.\u0026nbsp;3a, b). In contrast, the OE lines were more sensitive to alkaline stress than WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh, l and Supplementary Fig.\u0026nbsp;3a, b). Additionally, knockout of \u003cem\u003eOsNPF7.3\u003c/em\u003e homolog in maize also confers enhanced alkaline tolerance (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e\u003cp\u003ePhysiological assays further revealed that the rice KO lines suffered less oxidative damage than WT, while the OE lines showed the opposite physiological response (Supplementary Fig.\u0026nbsp;3c-f). To investigate the underlying mechanism of \u003cem\u003eosnpf7.3\u003c/em\u003e-mediated alkaline tolerance, we measured Na\u003csup\u003e+\u003c/sup\u003e contents in both shoot and root tissues. The results showed no significant differences in Na\u003csup\u003e+\u003c/sup\u003e contents between the mutants and WT (Supplementary Fig.\u0026nbsp;5), suggesting that \u003cem\u003eOsNPF7.3\u003c/em\u003e regulates alkaline tolerance independently of Na\u003csup\u003e+\u003c/sup\u003e homeostasis. Additionally, under normal conditions, although individual lines of the transgenic material exhibited higher or lower yield-related traits compared to WT, no significant overall difference was observed between the transgenic materials and WT (Supplementary Fig.\u0026nbsp;6). Together, these results establish \u003cem\u003eOsNPF7.3\u003c/em\u003e as a negative regulator of alkaline tolerance in rice and confirm it as the candidate gene of \u003cem\u003eqAT4\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePromoter variation and OsDOF11-mediated regulation underlie haplotype-specific expression of\u003c/b\u003e \u003cb\u003eOsNPF7.3\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first investigated the spatial expression pattern of \u003cem\u003eOsNPF7.3\u003c/em\u003e. Given that sequence variations of \u003cem\u003eOsNPF7.3\u003c/em\u003e were mainly localized in the promoter region (Supplementary Fig.\u0026nbsp;7), the 2-kb promoters of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e were cloned to drive GUS expression in transgenic rice for activity comparison. GUS staining results revealed that \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e exhibited stronger activity than \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, I), and that \u003cem\u003eOsNPF7.3\u003c/em\u003e was predominantly expressed in the vascular tissues of roots and leaf sheaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, II-IV). Consistently, qRT-PCR analysis confirmed that \u003cem\u003eOsNPF7.3\u003c/em\u003e was mainly expressed in roots during the vegetative stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Moreover, alkali treatment induced stronger expression of \u003cem\u003eOsNPF7.3\u003c/em\u003e in roots than in shoots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Supplementary Fig.\u0026nbsp;8a), indicating that the response of \u003cem\u003eOsNPF7.3\u003c/em\u003e to alkaline stress is mainly in roots. Similarly, comparative analysis of \u003cem\u003eOsNPF7.3\u003c/em\u003e expression between the two haplotypes showed that \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels in roots were significantly higher in \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e accessions than in \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), whereas no significant difference was detected in shoots (Supplementary Fig.\u0026nbsp;8b). These results are consistent with the GUS staining driven by the two haplotypes as well as with the SSD and SAT analyses of rice accessions under alkaline stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Fig.\u0026nbsp;9).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo dissect the impact of the sequence variations on promoter activity, we divided the promoter of \u003cem\u003eOsNPF7.3\u003c/em\u003e into three fragments with short overlapping regions, designated P1, P2 and P3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Transient expression assays in \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves showed that the P2 fragment drove the highest luciferase activity among the three fragments, and that the P1 and P2 fragments of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e exhibited stronger transcriptional activity than those of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef and Supplementary Fig.\u0026nbsp;10). Based on the binding sites prediction of transcription factors (TFs) with PlantRegMap database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plantregmap.gao-lab.org/\u003c/span\u003e\u003cspan address=\"https://plantregmap.gao-lab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we identified three members of OsDOF family that were predicted to bind at a variant site where \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e harbors a 7-bp insertion. By comparing the predicted binding sites of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e, we found that OsDOF11 was the only member of the OsDOF family that able to bind specifically to \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e. We therefore examined the binding affinity and regulatory effect of OsDOF11 on this cis-regulatory element. Yeast one-hybrid assays confirmed that OsDOF11 could bind to the S2 (P2 fragment of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e) and R2 (P2 fragment of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e), with a stronger binding to S2 than to R2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). Electrophoretic mobility shift assay (EMSA) further verified stronger binding affinity of OsDOF11 to S2 than to R2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh and Supplementary Fig.\u0026nbsp;11). Dual-luciferase reporter assays showed that co-expression with OsDOF11 significantly enhanced the transcriptional activity of S2 fragment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei and Supplementary Fig.\u0026nbsp;12). Taken together, these results suggested that the 7-bp InDel variation in the P2 region of the \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter is responsible for the differential expression of \u003cem\u003eOsNPF7.3\u003c/em\u003e between the two haplotypes. OsDOF11 functions as a transcriptional activator with stronger regulatory effect on \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e, leading to higher expression of \u003cem\u003eOsNPF7.3\u003c/em\u003e in \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e accessions, which ultimately results in reduced alkaline tolerance.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eOsNPF7.3 functions as an NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e transporter regulating nitrogen uptake and homeostasis\u003c/h2\u003e\u003cp\u003eComparative analysis of the coding sequence (CDS) showed that \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e differ by a single synonymous nucleotide substitution, which does not alter the protein sequence (Supplementary Fig.\u0026nbsp;13, 14). Subcellular localization analysis of OsNPF7.3 in \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves revealed that it co-localized with the vacuolar membrane marker protein INT1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Supplementary Fig.\u0026nbsp;15). Given that \u003cem\u003eOsNPF7.3\u003c/em\u003e belongs to the nitrate/oligopeptide transporter and may be involved in nitrogen (N) uptake and translocation, we investigated its transport function by heterologous expression in \u003cem\u003eXenopus laevis\u003c/em\u003e oocytes. The oocytes were incubated with \u003csup\u003e15\u003c/sup\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and \u003csup\u003e15\u003c/sup\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e for 3 h under pH 5.5 and pH 8.0 conditions, respectively. Gas chromatography-mass spectrometry (GC-MS) quantification of \u0026sup1;⁵N retention showed that oocytes expressing \u003cem\u003eOsNPF7.3\u003c/em\u003e accumulated significantly higher levels of \u0026sup1;⁵NH₄\u003csup\u003e+\u003c/sup\u003e compared to water-injected oocytes under pH 8.0 conditions, whereas no significant difference was observed under pH 5.5 conditions, indicating NH₄\u003csup\u003e+\u003c/sup\u003e uptake mediated by OsNPF7.3 is dependent on high pH levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, c). In contrast, \u0026sup1;⁵NO₃\u003csup\u003e\u0026minus;\u003c/sup\u003e accumulation was significantly reduced in \u003cem\u003eOsNPF7.3\u003c/em\u003e-expressing oocytes compared to control oocytes under pH 5.5 conditions, while accumulation levels were approximately equal under pH 8.0 conditions, suggesting OsNPF7.3-mediated NO₃\u003csup\u003e\u0026minus;\u003c/sup\u003e efflux is preferentially activated under low pH levels (Supplementary Fig.\u0026nbsp;16). These results suggest that OsNPF7.3 plays a critical role in regulating nitrogen uptake and homeostasis under alkaline stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the effect of alkali treatment on nitrogen redistribution mediated by OsNPF7.3 in rice, we compared the nitrogen content between WT and KO mutant following exposure to the nutrient solution of (\u0026sup1;⁵NH₄)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e under normal and alkaline stress conditions. Under normal conditions, the KO mutant accumulated significantly higher levels of \u0026sup1;⁵N in all organs except the stem than WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Under alkaline stress, however, increased \u0026sup1;⁵N accumulation was only observed in the 3rd leaf (the youngest leaf) and root of KO mutant compared to WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Furthermore, the ratio of \u003csup\u003e15\u003c/sup\u003eN content in each organ to that in the whole plant was used as an indicator of nitrogen redistribution. Under normal conditions, the KO mutant showed significantly higher \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eN content ratios of the root and the 2nd leaf compared to WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Under alkaline stress, however, only the 3rd leaf of the KO mutant exhibited a significantly higher \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eN ratio than that of WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). To further analyze \u0026sup1;⁵N redistribution after uptake, we compared the ratios of \u003csup\u003e15\u003c/sup\u003eN content between different organs, including root, stem, 1st leaf, 2nd leaf and 3rd leaf. Under normal conditions, the KO mutant showed lower stem/root and 3rd leaf/2nd leaf ratios, higher 1st leaf/stem and 2nd leaf/1st leaf ratios, compared to WT. Interestingly, alkali treatment reversed this pattern, with the KO mutant displaying increased 3rd leaf/2nd leaf, 3rd leaf/1st leaf and 3rd leaf/stem ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh-k and Supplementary Fig.\u0026nbsp;17). These results suggest that OsNPF7.3 functions as a key regulator of nitrogen redistribution under both normal and alkaline stress conditions. Specifically, loss of \u003cem\u003eOsNPF7.3\u003c/em\u003e function facilitates nitrogen redistribution from older to younger leaves during alkaline stress, thereby enhancing the adaptive capacity of rice to alkaline environments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLoss of\u003c/b\u003e \u003cb\u003eOsNPF7.3\u003c/b\u003e \u003cb\u003etriggers transcriptomic and metabolomic responses under alkaline stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo dissect the regulatory network underlying \u003cem\u003eosnpf7.3\u003c/em\u003e-mediated alkaline tolerance, we performed time-course transcriptomic and metabolomic analyses on shoot and root tissues of WT and the \u003cem\u003eOsNPF7.3\u003c/em\u003e-KO mutant at 0 h, 2 h, 1 d, 4 d, and 7 d after alkaline stress treatment. Transcriptomic profiling revealed more differentially expressed genes (DEGs) identified in roots than in shoots between the KO mutant and WT (Supplementary Fig.\u0026nbsp;18a), indicating that transcriptional response to alkaline stress is predominant in roots. No common DEGs were detected across all time points in shoots, whereas 24 DEGs displayed a consistent transcriptional pattern in roots. Metabolomic analysis identified 109, 84, and 87 differentially expressed metabolites (DEMs) in shoots between KO mutant and WT at 0 h, 1 d, and 7d, respectively (Supplementary Fig.\u0026nbsp;18b). Among these, 41 DEMs were shared between control and stress conditions, and 10 DEMs were common at the two time points after treatment. In roots, 150, 153, and 194 DEMs were detected at 0 h, 1 d and 7 d, respectively, including 37 common DEMs under both control and stress conditions and 34 overlapping DEMs between 1 d and 7 d after treatment (Supplementary Fig.\u0026nbsp;18b). Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs highlighted processes related to nitrogen transport and reutilization, and stress-defense, including cysteine/methionine metabolism, glutathione metabolism and arginine/proline metabolism (Supplementary Fig.\u0026nbsp;18c, d). We identified 17 amino acids and their derivatives significantly associated with nitrogen transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Based on weighted gene co-expression network analysis (WGCNA), we further identified 10 gene modules significantly associated with 17 nitrogen transport-related metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-c). GO/KEGG functional enrichment analysis of the top 50 hub genes from each module (Supplementary Data 6, 7) showed 31 terms related to nitrogen transport and stress responses, including ABA signal perception, metabolite synthesis, stress defense, and substance transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Among these hub genes, 41 showed significant differential expression between KO mutant and WT, with 38 of these DEGs detected in roots at 1 d after stress treatment (Supplementary Data 8). Five of these DEGs are closely related to nitrogen transport and alkaline stress response, including protein phosphatase 2A regulatory subunit B'k gene (\u003cem\u003eB'κ-PP2A\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, stress-induced chloroplast degradation gene (\u003cem\u003eOsCV\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, cysteine synthase gene (\u003cem\u003eOsRCS2\u003c/em\u003e), glutathione-dependent oxidoreductase CC-type glutaredoxin gene (\u003cem\u003eOsGRX6\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and TFIIIA-type zinc finger protein (\u003cem\u003eZFP252\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). These five genes may together improve rice alkaline tolerance through maintaining ion homeostasis, activating stress-protective pathways, and integrating nitrogen-hormone signaling. These integrated omics data collectively indicate that loss of \u003cem\u003eOsNPF7.3\u003c/em\u003e triggers a \u0026ldquo;nitrogen assimilation-redistribution-defense\u0026rdquo; regulatory network under alkaline stress, establishing a link between nitrogen homeostasis and alkaline stress adaptation in rice.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvolutionary divergence and breeding selection signatures of\u003c/b\u003e \u003cb\u003eOsNPF7.3\u003c/b\u003e \u003cb\u003ein rice\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the origin of the 7-bp InDel in the P2 promoter region of \u003cem\u003eOsNPF7.3\u003c/em\u003e and clarify its transmission patterns during rice breeding, we conducted comprehensive genomic analyses at this locus using over 10542 rice accessions from the RiceSuperPIRdb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ricesuperpir.com/\u003c/span\u003e\u003cspan address=\"http://www.ricesuperpir.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Consistent with haplotype analysis results from the GWAS panel, two major haplotypes (\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e) were identified in promoter region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Haplotype network analysis indicated that \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e represents the ancestral haplotype of \u003cem\u003eOsNPF7.3\u003c/em\u003e in rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Frequency distribution of the two haplotypes showed that \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e is mainly enriched in \u003cem\u003ejaponica\u003c/em\u003e, while \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e is widely present in \u003cem\u003eindica\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Moreover, high \u003cem\u003eF\u003c/em\u003est values were observed at \u003cem\u003eOsNPF7.3\u003c/em\u003e between \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e subspecies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg), suggesting that \u003cem\u003eOsNPF7.3\u003c/em\u003e may contribute to \u003cem\u003eindica\u003c/em\u003e-\u003cem\u003ejaponica\u003c/em\u003e differentiation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo investigate whether artificial selection during modern rice breeding over the past decades has affected the genetic diversity of \u003cem\u003eOsNPF7.3\u003c/em\u003e, we compared the frequency differences of the two major haplotypes between landraces and modern varieties. Notably, the alkali-tolerant haplotype \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e was apparently favored by artificial selection during modern breeding because its increased or fixed frequencies in modern \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e varieties compared to landraces (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). By comparing the nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e) and Tajima's \u003cem\u003eD\u003c/em\u003e values within a 200-kb region upstream and downstream of \u003cem\u003eOsNPF7.3\u003c/em\u003e between wild rice and the cultivated rice, we found that \u003cem\u003ejaponica\u003c/em\u003e rice exhibited the lowest genetic diversity at \u003cem\u003eOsNPF7.3\u003c/em\u003e locus (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) and significantly lower negative Tajima's \u003cem\u003eD\u003c/em\u003e values (~-2.5) compared to wild and \u003cem\u003eindica\u003c/em\u003e rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Cross-population composite likelihood ratio (XP-CLR) tests further confirmed stronger selection signals at \u003cem\u003eOsNPF7.3\u003c/em\u003e locus in \u003cem\u003ejaponica\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). These results suggested that \u003cem\u003eOsNPF7.3\u003c/em\u003e has undergone subspecies-specific positive selection during rice domestication and breeding.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSoil alkalization is a complex abiotic stress that severely constrains crop productivity worldwide. Dissecting the genetic basis of alkaline tolerance is therefore crucial for developing stress-tolerant crop varieties. In this study, we unraveled a novel genetic and molecular mechanism of the NPF member \u003cem\u003eOsNPF7.3\u003c/em\u003e underlying alkaline tolerance in rice. Our findings reveal that \u003cem\u003eOsNPF7.3\u003c/em\u003e acts as a key negative regulator of alkaline tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh-l), and its expression level is controlled by the binding affinity of OsDOF11 to a conserved subspecies-specific cis-regulatory element within its promoter region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-i). The OsDOF11-OsNPF7.3 module orchestrates alkaline stress adaptation via precise regulation of inter-organ nitrogen allocation, thereby establishing a molecular link between nutrient homeostasis and abiotic stress tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, \u003cem\u003eOsNPF7.3\u003c/em\u003e homolog may exert conserved functions in alkaline stress responses across cereal crops (Supplementary Fig.\u0026nbsp;4). These findings not only advance our fundamental understanding of plant responses to high-pH stress but also identify \u003cem\u003eOsNPF7.3\u003c/em\u003e as a promising genetic target for crop tolerance improvement.\u003c/p\u003e\u003cp\u003eHigh soil pH (alkaline stress) severely impairs nutrient acquisition in plants due to osmotic/ionic disturbances and reduced availability of essential nutrients\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Among nutrient transporters, NPF family proteins have emerged as modulators of abiotic stress tolerance in plant\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Overexpression of \u003cem\u003eOsNRT2.4\u003c/em\u003e enhances NO₃⁻ uptake and transport in high pH saline-alkaline soils, alleviating stress-induced growth inhibition\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Similarly, NPF2.3 rapidly loads NO₃⁻ into xylem to maintain shoot nitrogen homeostasis under saline-alkaline stress, with loss-of-function mutants displaying reduced salt tolerance\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A subset of NPF proteins involved in nitrate transport are localized to the plasma membrane or tonoplast, such as OsNPF4.5, OsNPF7.9, OsNPF6.5/OsNRT1.1B, and OsNPF6.3/NRT1.1A\u003csup\u003e29\u0026ndash;32\u003c/sup\u003e. The tonoplast-localized OsNPF7.3 can be induced by organic nitrogen sources, contributing to nitrogen allocation and improving NUE under normal conditions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, but reducing NUE under high ammonium supply\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In this study, we uncovered the mechanisms underlying these observations and demonstrated that \u003cem\u003eOsNPF7.3\u003c/em\u003e has dual transport activities, facilitating NH₄⁺ uptake under high-pH conditions and NO₃⁻ efflux under normal conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c and Supplementary Fig.\u0026nbsp;16). Physiological and isotopic tracing data further confirmed that the \u003cem\u003eosnpf7.3\u003c/em\u003e knockout mutants avoid excess NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e accumulation by eliminating NH₄⁺-sequestration activity in vacuoles under alkaline stress, and release trapped nitrogen in older leaves and enhance its redistribution to young leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-k). This suggests that \u003cem\u003eOsNPF7.3\u003c/em\u003e may act as a sensor to high pH (alkaline stress) and then initiate nitrogen remobilization. Transcriptomic and metabolomic profiling of \u003cem\u003eosnpf7.3\u003c/em\u003e knockout mutant revealed the activation of sulfur and glutathione metabolism and accumulation of amino acids and nucleotides, indicating a broad metabolic responses to reinforce alkaline stress adaptation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Fig.\u0026nbsp;18). Here, we propose a working model in which the 7-bp natural variation within the \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter distinguishes \u003cem\u003eindica\u003c/em\u003e (below) and \u003cem\u003ejaponica\u003c/em\u003e (above) subspecies, which alters the binding affinity of OsDOF11, leading to differential \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels, NH₄⁺ transport activity and ultimately distinct alkaline tolerance between subspecies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei). Our findings support that the ability to avoid excess ammonium accumulation and efficiently redistribute internal nitrogen reserves could be an important adaptive strategy when acquiring nitrogen from the environment is severely impaired due to alkaline stress, highlighting the molecular crosstalk between nutrient signaling and stress responses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eOsNPF7.3\u003c/em\u003e exhibits remarkably low genetic diversity in rice germplasm, with only two major haplotypes showing clear differentiation between \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e subspecies. Notably, \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003eHap2\u003c/sup\u003e accessions showed significantly higher \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels than \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003eHap1\u003c/sup\u003e accessions in root, indicating a strong positive correlation between \u003cem\u003eOsNPF7.3\u003c/em\u003e expression levels and sensitivity to alkaline stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This functional divergence between haplotypes is attributed to differential transcriptional activation by OsDOF11. Previous studies reported that OsDOF11 can bind the AAAG cis-element to activate sucrose transporters and SWEET genes, regulating source-sink sucrose partitioning and enhancing nitrogen co-translocation to grains through increased sucrose efflux, achieving high yield and NUE in rice\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, the 7-bp insertion in \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003eHap2\u003c/sup\u003e enhances both OsDOF11 binding affinity and promoter transcriptional activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-i), providing a mechanistic explanation for the differential expression and functional divergence between the two haplotypes. Moreover, the phenotypic evaluation of \u003cem\u003eOsDOF11\u003c/em\u003e knockout lines indicated that \u003cem\u003eOsDOF11\u003c/em\u003e negatively regulated rice alkaline tolerance at seedling stage (Supplementary Fig.\u0026nbsp;19). Collectively, this natural variation in the promoter of \u003cem\u003eOsNPF7.3\u003c/em\u003e appears to be the primary determinant of subspecies-specific differences in alkaline tolerance. The highly conserved coding sequence and subspecies-specific functional divergence promoter region of \u003cem\u003eOsNPF7.3\u003c/em\u003e suggests that strong purifying selection has preserved its core transport function, while its expression levels have been fine-tuned by adaptive evolution of each subspecies for distinct environmental niches (Supplementary Fig.\u0026nbsp;13, Supplementary Fig.\u0026nbsp;14 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). The evolutionary signature of \u003cem\u003eOsNPF7.3\u003c/em\u003e, particularly in \u003cem\u003ejaponica\u003c/em\u003e accessions from temperate regions prone to soil alkalization, implies that \u003cem\u003eOsNPF7.3\u003c/em\u003e played a key role in rice subspecies divergence by regulating alkaline tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings offer valuable insights into the genetic basis of crop environmental adaptation and provide alternative strategies to address future agricultural challenges arising from climate change.\u003c/p\u003e\u003cp\u003eAlthough our experiments were primarily conducted at the seedling stage, a critical developmental phase for rice establishment, future studies should evaluate the role of \u003cem\u003eOsNPF7.3\u003c/em\u003e at the reproductive stage using near-isogenic lines and elucidate the related regulatory network. Together, our findings reveal \u003cem\u003eOsNPF7.3\u003c/em\u003e as a key negative regulator of alkaline tolerance, providing valuable insights and genetic resources for enhancing stress resilience and NUE in crops.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials and growth conditions\u003c/h2\u003e\u003cp\u003eA total of 306 accessions from the 3K Rice Genomes Project\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e were utilized to evaluate alkaline tolerance at the seedling stage. These accessions included 137 \u003cem\u003eindica\u003c/em\u003e, 166 \u003cem\u003ejaponica\u003c/em\u003e, 1 \u003cem\u003eaus\u003c/em\u003e, 1 \u003cem\u003ebasmati\u003c/em\u003e, and 1 admix accessions (Supplementary Data 1). The wild-type rice variety used in this study was ZH11, which harbored the \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e haplotype. The \u003cem\u003eosnpf7.3\u003c/em\u003e knockout mutants were obtained using the CRISPR-Cas9 system. For the construction of \u003cem\u003eOsNPF7.3\u003c/em\u003e overexpression lines, its coding sequence was amplified and cloned into the construct pEXT06/g under the control of ubiquitin gene promoter. Both the \u003cem\u003eOsNPF7.3\u003c/em\u003e overexpression and knockout constructs were transformed into ZH11 background via Agrobacterium-mediated transformation. The vectors were constructed by Wuhan Biorun Bio-Tech Co., Ltd. Rice seeds were surface-sterilized with 1% sodium hypochlorite solution for 30 min, followed by hydroponic germination for 2 d until radicle emergence. For each germplasm and transgenic material, 36 and 72 uniformly germinated seeds were selected for phenotypic evaluation with three replicates (12 and 24 plants per replicate), respectively. The germinated seeds were sown into 96-well PCR plates with perforated bottoms, which were then placed in plastic containers filled with distilled water (pH 5.5). Seedlings grow in a phytotron at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS) in Beijing, under conditions of 14 h light/10 h dark photoperiod (28 ℃/26 ℃) with 70% relative humidity for 7 d, and then the distilled water was replaced with Yoshida nutrient solution\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eKnockout mutants of \u003cem\u003eZm00001d026221\u003c/em\u003e, the maize homolog gene of \u003cem\u003eOsNPF7.3\u003c/em\u003e, were also generated using the CRISPR-Cas9 system in the B104 maize inbred line background. The vector was constructed by Wuhan Biorun Bio-Tech Co., Ltd. Seeds were disinfected with a 1% sodium hypochlorite solution for 30 min, then placed in 90 mm petri dishes containing 15 ml of sterile water for germination. For each genotype, 18 germinated seeds were sown in small flower pots (7.5 cm \u0026times; 7.5 cm) filled with a mixture of nutrient soil and vermiculite (1:1, v/v) for the phenotypic experiment (at least 5 replicates, 3 plants per replicate). Primer sequences used in this study are listed in Supplementary Data 9.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAlkaline tolerance evaluation\u003c/h3\u003e\n\u003cp\u003ePhenotypic evaluation of alkaline tolerance was carried out following the method described by Chaudhary et al\u003csup\u003e38\u003c/sup\u003e. Briefly, alkaline stress was imposed by Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH 9.5) at the two-leaf-and-one-heart stage, while the control group was maintained in Yoshida nutrient solution (pH 5.5)\u003csup\u003e37\u003c/sup\u003e. The nutrient solutions in both groups were refreshed every 3 d. On the 23rd day of alkaline treatment, SAT of each rice accession was evaluated according to the Standard Evaluation System for Rice\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Seedlings were scored into nine grades from SAT 1 (the least damage) to SAT 9 (the most severe damage) based on the severity of stress-induced damage. Accessions with SAT\u0026thinsp;\u0026le;\u0026thinsp;3 were classified as alkali-tolerant, those with SAT\u0026thinsp;\u0026ge;\u0026thinsp;7 as alkali-sensitive. SSD were recorded from the first occurrence of seedling death in each accession until the complete death of all seedlings. VGI was calculated as the ratio of SSD to SAT, which could comprehensively evaluate the alkaline tolerance of rice seedlings.\u003c/p\u003e\u003cp\u003eOn the 16th day of alkaline stress treatment, 3 seedlings per replicate were randomly selected from each of WT and transgenic materials under both stress and control conditions for measurement of seedling height and root length. On the 18th day of treatment, the phenotypes of WT and transgenic materials were photographed, and the survival rate was recorded.\u003c/p\u003e\u003cp\u003eFor maize materials, two-leaf and one-heart seedlings were subjected to alkaline stress treatment with 200 mM Na₂CO₃ solution. The survival rate was recorded on the 20th day of the stress treatment.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGWAS analysis\u003c/h2\u003e\u003cp\u003eA total of 2,652,345 SNPs with a missing rate\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.05 in the GWAS panel were filtered from the 3K-RG 4.8M SNP dataset\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e by PLINK\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The GWAS based on a mixed linear model was performed with EMMAX\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e to identify the associations between SNPs and alkaline tolerance. The kinship matrix was calculated with an identical-by-state matrix using the pruned SNP subset (with the parameter \u0026ldquo;indep-pairwise 50 10 0.1\u0026rdquo; in PLINK) as a measure of relatedness between accessions. The eigenvectors of the kinship matrix were calculated using GCTA (with the parameter \u0026ldquo;-make-grm\u0026rdquo;)\u003csup\u003e42\u003c/sup\u003e and the first three principal components were used as covariates to control population structure. The effective number of SNPs was calculated by the GEC software\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, and a suggestive significance threshold of association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51E-06) was determined by the Bonferroni correction method (1/the effective number of SNPs) for claiming significant SNPs. Manhattan plots of the GWAS results were plotted by the R package \u0026ldquo;qqman\u0026rdquo;\u003csup\u003e44\u003c/sup\u003e. The significant SNPs within the 300-kb region were considered a locus based on the previously reported linkage disequilibrium (LD) decay in 3KRG\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The leading SNP of a locus was defined as the SNP with the lowest \u003cem\u003ep\u003c/em\u003e value within a 300-kb region. The locus interval was determined by local LD block analysis of the leading SNP using the LDBlockShow\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhysiological measurements\u003c/h3\u003e\n\u003cp\u003eAt the two-leaf and one-heart stage, ZH11 and transgenic materials were subjected to alkaline stress treatment with Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH\u0026thinsp;=\u0026thinsp;9.8). Seedlings cultured in Yoshida nutrient solution without Na₂CO₃ served as the control. Both control and stress groups were set up with three biological replicates. After 7 d of treatment, shoot and root samples were collected from both groups. The activities of POD and CAT were determined using assay kits (Suzhou Grace Biotechnology, Suzhou, China) following the manufacturer\u0026rsquo;s protocols.\u003c/p\u003e\u003cp\u003eOn the 17th day of alkaline stress, six seedlings of each material under alkaline stress and normal conditions were randomly selected for Na\u003csup\u003e+\u003c/sup\u003e concentration determination, respectively. The roots were washed to remove residual nutrient solution, then blotted dry with filter paper. Place the shoot and root samples of each material into separate envelopes. Treat at 105 ℃ for 30 min, then adjust the temperature to 80 ℃ for several days, and transfer to test tubes. Add acetic acid solution to the test tubes (20 mL for shoots and 10 mL for roots), and place the test tubes in a constant-temperature shaking water bath at 90 ℃ for 3 h. After cooling, take the supernatant and dilute it. Determine the Na⁺ concentration using a flame photometer. The calculation formula is as follows: Mass fraction of Na⁺ in the sample (mg/g)\u0026thinsp;=\u0026thinsp;C\u0026times;V\u0026times;N/(M\u0026times;1000). C\u0026thinsp;=\u0026thinsp;Element concentration of the sample tested on the instrument (mg/L), V\u0026thinsp;=\u0026thinsp;Volume of acetic acid in the sample extract (mL), N\u0026thinsp;=\u0026thinsp;Dilution factor, M\u0026thinsp;=\u0026thinsp;Sample mass (g). Two biological replicates were set up in the experiment.\u003c/p\u003e\n\u003ch3\u003eAgronomic traits measurements\u003c/h3\u003e\n\u003cp\u003eField experiments were conducted with three biological replicates at the Shunyi experimental station of Institute of Crop Sciences, CAAS, in Beijing. Yield-related traits were measured following the methods described by Zhang et al.\u003csup\u003e46\u003c/sup\u003e, including plant height, panicle number per plant (PN), filled grain number per panicle (FGNP), 1000-grain weight (TGW), and grain yield per plant (GYP). At the mature stage, 5 plants per replicate were sampled seperiately for measurement. GYP (g) was defined as the average weight of filled grains per plant. TGW (g) was calculated by dividing GYP by the filled grain number and then multiplying 1000. Three biological replicates were set up in the experiment.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRNA extraction and qRT-PCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using TRIzol reagent (Tiangen). 2 \u0026micro;g of RNA was used for first-strand cDNA synthesis using the FastingKing One-Step Genomic DNA Removal and cDNA First-Strand Synthesis Premix Reagent (Tiangen). qRT-PCR was carried out on an ABI 7500 real-time PCR system (Applied Biosystems) using 2 \u0026times; Taq Universal SYBR qPCR Master Mix Kit (Tiangen). The rice \u003cem\u003eUBQ\u003c/em\u003e gene was used as an internal reference to normalize gene expression levels. The relative expression of target genes was calculated by the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The qRT-PCR primers were listed in Supplementary Data 9.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGUS assay\u003c/h2\u003e\u003cp\u003eThe 2-kb genomic upstream region of \u003cem\u003eOsNPF7.3\u003c/em\u003e transcription start site was amplified and cloned into the pBWA(V)HG vector. The vector was constructed by Wuhan Biorun Bio-Tech Co., Ltd. GUS histochemical staining was conducted on T2 homozygous transgenic rice plants. Roots, leaf sheaths, and leaves of germinated seedlings were incubated in a GUS staining solution (CoolaberBiotech, Beijing, China) at 37\u0026deg;C for 12 h, then decolorized in 75% (v/v) ethanol. Paraffin sections prepared from the stained tissues were imaged by an Olympus BX41 Microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSubcellular localization\u003c/h2\u003e\u003cp\u003eThe CDS of \u003cem\u003eOsNPF7.3\u003c/em\u003e without a stop codon was amplified from the cDNA of the alkali-sensitive haplotype and fused to the N-terminus of the green fluorescent protein (GFP) gene in the modified pEZR(K)-LC-GFP vector. The vacuolar membrane (RFP-INT1) vector\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e was provided by Biorun Bio-Tech Co., Ltd. After large-scale plasmid extraction, the recombinant vector was transformed into \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves by \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e-mediated infiltration. GFP fluorescence was observed using a confocal microscope (Nikon-A).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDual-luciferase assay\u003c/h2\u003e\u003cp\u003eDual-luciferase assay was performed to investigate the transcriptional activity of the promoter sequences of the two \u003cem\u003eOsNPF7.3\u003c/em\u003e haplotypes. Promoter fragments of \u003cem\u003eOsNPF7.3\u003c/em\u003e from alkaline-tolerant (\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e) and alkaline-sensitive (\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e) accessions were amplified and fused into the modified pLZ004 vector, which contains the firefly luciferase (LUC) gene as the reporter and the renilla luciferase (REN) gene as the internal control. The full-length CDS of \u003cem\u003eOsDOF11\u003c/em\u003e was cloned into the pEZR(K)-LC-GFP vector to generate the effector construct. The reporter and effector plasmids were co-transformed into \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e GV3101 and co-infiltrated into \u003cem\u003eNicotiana benthamiana\u003c/em\u003e leaves for 48 h. LUC and REN luciferase activities were surveyed using the Dual-Luciferase Reporter Assay System (Promega). At least four independent infiltrations were performed for each sample, and the relative LUC activity was calculated as the ratio of LUC to REN activity. Transient transactivation with the reporters and the empty vector pLZ004 was used as a control. All primers used for these constructs are listed in Supplementary Data 9.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eYeast one-hybrid assay\u003c/h2\u003e\u003cp\u003eYeast one-hybrid (Y1H) assay was conducted to verify the direct binding of OsDOF11 to the \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter. The promoters of \u003cem\u003eOsNPF7.3\u003c/em\u003e from alkaline-tolerant (\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e) and alkaline-sensitive (\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e) genotypes were inserted into the pLacZi2\u0026micro; vector to generate the reporter constructs p\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u0026thinsp;\u0026minus;\u0026thinsp;P2\u003c/em\u003e\u003c/sup\u003e::\u003cem\u003eLacZ\u003c/em\u003e and p\u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u0026thinsp;\u0026minus;\u0026thinsp;P2\u003c/em\u003e\u003c/sup\u003e::\u003cem\u003eLacZ\u003c/em\u003e, respectively. The full-length CDS of \u003cem\u003eOsDOF11\u003c/em\u003e was amplified from the cDNA of an alkaline-sensitive accession and cloned into the pB42AD vector to generate the AD-OsDOF11 effector construct. The effector and reporter plasmids were co-transformed into the yeast strain EGY48. Transformants were grown on SD/-Trp-Ura dropout medium supplemented with 20 mg/mL X-gal (5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside) (Clontech). All primers used for these constructs are listed in Supplementary Data 9.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eEMSA\u003c/h2\u003e\u003cp\u003eThe full-length cDNA of \u003cem\u003eOsDOF11\u003c/em\u003e was cloned into the pET28a vector, which contains a histidine (His) tag for protein purification. The recombinant His-OsDOF11 protein and the empty His-tag protein (negative control) were expressed in \u003cem\u003eEscherichia Coli\u003c/em\u003e Strain BL21 (DE3) and purified using genes and according to the manufacturer\u0026rsquo;s instructions. The 33-bp probes containing the OsDOF11 binding motifs of \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap1\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eOsNPF7.3\u003c/em\u003e\u003csup\u003e\u003cem\u003eHap2\u003c/em\u003e\u003c/sup\u003e were synthesized and labeled with Cy5 (Beijing Tsingke Biotech Co., Ltd.). EMSA was performed using the Light Shift Chemiluminescent EMSA Kit (Thermo Scientific) following the manufacturer\u0026rsquo;s protocol. Primer and probe sequences are listed in Supplementary Data 9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional analysis of OsNPF7.3 in\u003c/b\u003e \u003cb\u003eXenopus laevis\u003c/b\u003e \u003cb\u003eoocytes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CDS of \u003cem\u003eOsNPF7.3\u003c/em\u003e was cloned into the pGEMHE oocyte expression vector. Capped RNA (cRNA) was synthesized from 1 \u0026micro;g of linearized plasmid DNA using the mMESSAGE mMACHINE T7 kit (Ambion) according to the manufacturer\u0026rsquo;s recommendations. The quality of the synthesized cRNA was checked by agarose gel electrophoresis. Each \u003cem\u003eXenopus laevis\u003c/em\u003e oocyte was injected with 23 ng of cRNA. Injected oocytes were incubated in ND96 solution (96 mM NaCl, 2 mM KCl, 1 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 1.8 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 10 mM HEPES/NaOH, pH 7.4) supplemented with 10 \u0026micro;g l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e penicillin and streptomycin at 18\u0026deg;C for 2 d. Oocytes were then incubated for 3 h in ND96 solution containing specific concentrations of \u003csup\u003e15\u003c/sup\u003eN-labeled substrates at a defined pH. After incubation, oocytes were thoroughly washed with ND96 solution, placed in tin boats, and dried at 65\u0026deg;C. Each oocyte was treated as a biological replicate for N content determination\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e15\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eN uptake assay under alkaline stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeedlings of ZH11 and \u003cem\u003eosnpf7.3\u003c/em\u003e mutants were cultivated in Yoshida nutrient solution for 20 d. Then, the nutrient solution was replaced with Yoshida nutrient solution containing (\u0026sup1;⁵NH₄)₂SO₄ instead of (NH₄)₂SO₄. After 12 d alkali stress treatment with 0.15% Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e (pH\u0026thinsp;=\u0026thinsp;9.5), roots, stems, the 1st, 2nd, and 3rd leaves were collected, dried, and pulverized for \u0026sup1;⁵N content determination. The samples without alkaline stress treatment served as the control. The \u0026sup1;⁵N content in each sample was determined using an Isotope Ratio Mass Spectrometer System (Model: Flash 2000 HT; Thermo Fisher Scientific). The \u0026sup1;⁵N accumulation (\u0026micro;mol \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eN g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e dry weight, DW), relative \u0026sup1;⁵N redistribution ratio (\u003csup\u003e15\u003c/sup\u003eN ratio, %)\u003csup\u003e27,31\u003c/sup\u003e and the ratios of \u003csup\u003e15\u003c/sup\u003eN concentration between different parts\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e were calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptome and metabolome analysis\u003c/h2\u003e\u003cp\u003eZH11 and \u003cem\u003eosnpf7.3\u003c/em\u003e knockout mutant (KO-1) were treated with Yoshida nutrient solution containing 0.15% Na₂CO₃ (pH 9.8) at the two-leaf and one-heart stage. Control seedlings were cultured in Yoshida nutrient solution without Na₂CO₃. Both groups were set up with three biological replicates. Samples were collected at 0 h, 2 h, 1 d, 4 d, and 7 d after alkaline treatment, immediately flash-frozen in liquid nitrogen, and stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor transcriptome, total RNA was extracted with ethanol precipitation and CTAB-PBIOZOL, and dissolved in 50 \u0026micro;L DEPC-treated water. RNA was identified and quantified were determined using a Qubit fluorescence quantifier and a Qsep400 high-throughput biofragment analyzer, respectively. The mRNA library construction and paired-end sequencing on Illumina platform were conducted by Metware Biotechnology Inc., Ltd. Raw sequencing reads were filtered to remove adapters, low-quality reads (Q\u0026thinsp;\u0026le;\u0026thinsp;20), and reads containing more than 10% unknown bases. Clean reads were mapped to the Nipponbare reference genome (IRGSP-1.0) using HISAT2\u003csup\u003e51\u003c/sup\u003e software. The number of reads mapped to each gene was counted using featureCounts\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e software. Differentially expressed genes (DEGs) between KO-1 and WT under control and stress conditions were identified using DESeq2\u003csup\u003e53\u003c/sup\u003e software with the criteria of |log2(Fold Change)| \u0026ge; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eFor metabolome, untargeted metabolomics analysis of samples collected at 0h, 1d, and 7d was performed on an ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform by Metware Biotechnology Inc., Ltd. Sample preparation was as follows: 50 mg sample powder extracted with 1200 \u0026micro;L of -20\u0026deg;C pre-cooled 70% (v/v) methanol. Vortex once every 30 min for 30 sec, for a total of 6 times. After centrifugation (rotation speed 12000 rpm, 3 min), the supernatant was aspirated, and the sample was filtered through a microporous membrane (0.22 \u0026micro;m pore size) and stored in the injection vial for UPLC-MS/MS analysis. The supernatant was filtered through a 0.22 \u0026micro;m membrane and analyzed by UPLC-MS/MS. Peak detection and metabolite annotation were performed using XCMS\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e software with reference to the Metware database. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to visualize metabolic differences between groups, and Variable Importance in Projection (VIP) values were calculated. Differential metabolites between KO-1 and ZH11 were identified with the criteria of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePopulation genetic analysis\u003c/h2\u003e\u003cp\u003eSNPs within the 1-kb upstream region of the \u003cem\u003eOsNPF7.3\u003c/em\u003e from the 3K rice germplasm resources were downloaded using the RFGB v2.0 database\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and used for haplotype analysis. Haplotypes carried by at least 30 rice accessions were defined as the major haplotypes. The haplotype network was constructed based on the 7 bp InDel using the R package geneHapR\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Upstream haplotypes of \u003cem\u003eOsNPF7.3\u003c/em\u003e in 5703 rice accessions were retrieved from the Molecular Breeding Knowledge base (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mbkbase.org/rice\u003c/span\u003e\u003cspan address=\"http://www.mbkbase.org/rice\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e57\u003c/sup\u003e. We also performed population genetic analysis on the genomic sequences of \u003cem\u003eOsNPF7.3\u003c/em\u003e and its flanking 200-kb regions in 3359 \u003cem\u003ejaponica\u003c/em\u003e, 5295 \u003cem\u003eindica\u003c/em\u003e, and 412 wild rice accessions from the Rice Super-Population Variation Map database\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Nucleotide diversity (\u003cem\u003eπ\u003c/em\u003e), fixation index (\u003cem\u003eF\u003c/em\u003e) between populations, and Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e were calculated for each 5-kb window with a 1-kb step size using VCFtools software (v0.1.16)\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The cross-population composite likelihood ratio test (XP-CLR) was carried out using the Python package XPCLR\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e for each 5-kb window with a 1-kb step size and an LD of 0.95.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this work are available within the paper and its Supplementary Information files. The transcriptome data have been uploaded to have been deposited in the Genome Sequence Archive\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e in National Genomics Data Center\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (CRA032825) that are publicly accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/gsa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 3K-RG 4.8M SNP dataset can be downloaded from the Rice SNP-Seek Database [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snp-seek.irri.org\u003c/span\u003e\u003cspan address=\"https://snp-seek.irri.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The data of the rice genomic SNPs used in the genetic diversity analysis can be downloaded from MBKbase [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mbkbase.org/rice/genotype\u003c/span\u003e\u003cspan address=\"https://www.mbkbase.org/rice/genotype\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Source data are provided with this paper.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eF.Z. directed the project. P.L., J.L., S.M., and X.W. performed the experiments. P.L., J.L., S.M., and G.Z. analyzed the data in GWAS and genetic diversity analysis. P.L., J.L., G.Z., and K.C. conducted and managed the field work. Q.Y., J.J., J.A. M.L., L.T., and Y.W. participated in the experiments. F.Z., W.W., and D.L. planned and designed the research. X.Z., J.X., Z.L., D.L., and F.Z. interpreted the data. P.L., D.L., and F.Z. wrote the paper and finalized the paper. All the coauthors approved the paper.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis work was funded by the National Key Research and Development Program of China (2023YFF1000400), the National Natural Science Foundation of China (32401827), the Nanfan Special Project, CAAS (YBXM2426), the Central Public-interest Scientific Institution Basal Research Fund (Y2025YC13), and the Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-CSIAF-202303). We thank Dr. Legong Li (Capital Normal University) for the technical support in \u003cem\u003eXenopus laevis\u003c/em\u003e oocytes analysis. The funding agencies had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSrarfi, F. \u0026amp; Majar, A. 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Res.\u003c/em\u003e \u003cstrong\u003e53,\u003c/strong\u003e D30-D44, (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alkaline tolerance, OsNPF7.3, haplotype analysis, nitrogen homeostasis, rice","lastPublishedDoi":"10.21203/rs.3.rs-8236504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8236504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil alkalization is one of the most severe abiotic stresses constraining rice yields. However, the genetic basis underlying alkaline tolerance of rice remains poorly understood. Here, we used genome-wide association analysis to identify \u003cem\u003eOsNPF7.3\u003c/em\u003e as the candidate gene for \u003cem\u003eqAT4\u003c/em\u003e, which is a major locus associated with alkaline tolerance at rice seedling stage. \u003cem\u003eOsNPF7.3\u003c/em\u003e encodes a nitrate/oligopeptide transporter and acts as a negative regulator of rice alkaline tolerance. A natural variation of 7-bp insertion/deletion in the \u003cem\u003eOsNPF7.3\u003c/em\u003e promoter, affecting the binding affinity of transcription factor OsDOF11, mainly contributes to differential transcriptional levels of \u003cem\u003eOsNPF7.3\u003c/em\u003e, and thus leads to differential alkaline tolerance between \u003cem\u003ejaponica\u003c/em\u003e and \u003cem\u003eindica\u003c/em\u003e subspecies. OsNPF7.3 localizes to the vacuolar membrane and mediates nitrogen transport from older to younger leaves under alkaline stress. Loss of \u003cem\u003eOsNPF7.3\u003c/em\u003e significantly upregulated the expression of nitrogen metabolism-related genes and metabolites, suggesting its regulatory role in nitrogen allocation. Together, these findings reveal an OsDOF11-\u003cem\u003eOsNPF7.3\u003c/em\u003e-nitrogen metabolism regulatory module that connects nitrogen homeostasis to alkaline tolerance, providing a promising target for the development of alkaline-tolerant rice varieties.\u003c/p\u003e","manuscriptTitle":"Natural variation of OsNPF7.3 regulates alkaline tolerance by modulating ammonium uptake and redistribution in rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 05:51:21","doi":"10.21203/rs.3.rs-8236504/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a308ecc0-1084-4556-8fbf-240b21ff37c8","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58994127,"name":"Biological sciences/Plant sciences/Natural variation in plants"},{"id":58994128,"name":"Biological sciences/Plant sciences/Plant stress responses/Abiotic"}],"tags":[],"updatedAt":"2025-12-19T23:15:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 05:51:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8236504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8236504","identity":"rs-8236504","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}