Controlled-release urea optimizes the pathway to yield increase via post-anthesis carbon-nitrogen coordination in maize

Controlled-release urea optimizes the pathway to yield increase via post-anthesis carbon-nitrogen coordination in maize | 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 Research Article Controlled-release urea optimizes the pathway to yield increase via post-anthesis carbon-nitrogen coordination in maize Huan Li, Yiming Zhu, Menglin Bai, Yihan Zhang, Huiyang Zhu, Weixiao Hu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8930203/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract The precise regulation of nitrogen supply after flowering for maize can be achieved by blending urea and controlled-release urea (CCU) one-off application. However, the dynamic optimization driving source-sink allocation and mechanisms underlying synergistic carbon–nitrogen regulation remain poorly understood. We investigated the physiological and molecular mechanisms underlying carbon and nitrogen metabolism in maize under various controlled-release urea (CRU) and conventional urea treatments. The experiment included five fertilization treatments: CK (no nitrogen) and four treatments at 180 kg N ha − 1 : U (all Urea-N), C1 (CRU-N: Urea-N = 1:2), C2 (CRU-N: Urea-N = 2:1), and C3 (all CRU-N). Physiological traits were measured, and integrated leaf transcriptomic and metabolomic analyses were conducted. Compared with urea treatment, CCU (C2 treatment) boosted maize yield by up to 18.3–22.8%, synergistically enhancing nitrogen components (N content and soluble protein), carbon metabolites (C content and soluble sugar), and total dry matter. Notably, total dry matter was positively correlated with C/N ratio. CCU optimized carbon–nitrogen allocation by simultaneously increasing grain nitrogen reserves (soluble protein and free amino acids) and carbon storage (non-structural carbohydrates). Integrated transcriptomic and metabolomic analysis revealed CCU-mediated metabolic shifts, activating aromatic amino acid biosynthesis and glyoxylate and dicarboxylate metabolism. Multi-omics integration identified GLT1 and IDH3 as pivotal regulatory factors, whose expression levels showed significantly correlated with alanine, homocitric acid, and dry matter accumulation, thereby linking the crosslink between the TCA cycle and nitrogen assimilation to yield formation. These findings demonstrate that CCU drives the priority distribution of assimilation products to grains through the synergistic regulation of slow nitrogen release and carbon-nitrogen metabolism. This study provides insights into the regulation of maize metabolism and offers guidance for the efficient utilization of nitrogen under one-off application of nitrogen. Carbon metabolism Nitrogen metabolism Comprehensive transcription/metabolism analysis Controlled-release urea and urea combined application Maize (Zea mays L.) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Maize ( Zea mays L.) represents an important crop for global food security and economic development and is widely used as a food, feed, bioenergy source, and industrial raw material (Erenstein et al., 2022 ). Nitrogen (N) plays an important role in crop growth and -yield based on its inclusion in vital components in plants, such as proteins, nucleic acids, and chlorophyll, and participation in metabolic processes such as photosynthesis and respiration (Girón-Calva et al., 2021 ; Li et al., 2022a ). For a long time, farmers have overused nitrogen fertilizer to improve yields, which has massively increased nitrogen waste, soil quality deterioration, and environmental pollution (Chang et al., 2021 ). In recent years, the Chinese government has emphasized the efficient use of fertilizer resources and the pollution caused by fertilizers to ensure food security and environmental sustainability. Fertilizer products need to be transformed and upgraded, and fertilizers need to meet the nutritional needs of crop growth while maintaining an appropriate soil fertilization capacity and reducing environmental pollution (Wu et al., 2023 ). A previous study showed that the proportion of nitrogen absorption in the post-anthesis stage to the total nitrogen absorption at maturity significantly increased from 12% to 32% as the maize yield increased (Meng et al., 2016 ). The distribution of N from the source to the sink organs is affected by the absorption and metabolism of N by the source organs as well as the capacity for source output and sink input. The ear leaves (source) provide the necessary energy for grain (sink) formation (Fan et al., 2023 ; Du et al., 2024 ). Nitrogen fertilizer application can increase crop nitrogen accumulation after anthesis and subsequently improve protein content. During the grain-filling stage, proteins stored in leaves are rapidly broken down into free amino acids (FAAs), which are then transported to the grains through the phloem and ultimately synthesized into storage proteins (Havé et al., 2017; Zhang et al., 2022a ). Nitrogen fertilization can stimulate the allocation of sugars and starches to organic acid pools, thereby promoting amino acid synthesis and assimilation and ultimately enhancing crop yield (Zhang et al., 2022a ). Additionally, nitrogen supply can enhance leaf photosynthesis by providing more nitrogen and carbon sources to the leaves and promoting the transport of many photosynthates from the leaves to the grains (Sun et al., 2013 ; Song et al., 2023 ). This highlights the essential role of nitrogen in carbon capture, synthesis, and transportation from the source to sink organs. During the grain-filling stage, which is a crucial period of grain maturation, nitrogen reuse in source leaves becomes the focus, leading to significant reductions in photosynthesis and nitrogen and carbon assimilation (Tegeder et al., 2018). By integrating transcriptomics and metabolomics data, gaining insights into how genes and metabolic pathways within plants constitute complex regulatory networks is possible (Xin et al., 2019 ; Yang et al., 2023 ). Nitrogen supply significantly enhances carbon fixation during photosynthesis and the activities of key carbon-metabolizing enzymes, such as invertase and amylase, which helps to accelerate the conversion process of starch and sucrose and provides the necessary carbon skeleton for nitrogen metabolism (Foyer et al., 2011; Xin et al., 2019 ). The growth of aboveground rice is regulated by the interaction between carbon and nitrogen, and the availability of carbon and nitrogen has complex regulatory effects on enzymes such as nitrate reductase (NR), glutamine synthetase (GS), and phosphoenolpyruvate carboxylase (PEPC); thus, certain metabolic genes are co-regulated by both carbon and nitrogen levels (Sun et al., 2013 ). This regulation maintains the balance of carbon and nitrogen in plants through growth regulation and metabolic gene expression changes, which are required for plant growth and development (Sun et al., 2013 ). The slow-release properties of controlled-release urea (CRU) aid in enhancing the balance of carbon and nitrogen metabolism by refining the nitrogen supply strategy of crops This improvement not only enhances the photosynthetic capacity of the leaves but also promotes biomass accumulation after the anthesis stage (Li et al., 2022c ; Li et al., 2024 ). The combined application of CRU and urea (CCU) can address the issue of inadequate nitrogen release. in the initial stage of CRU and ensure the continuous availability of nitrogen, thus optimizing the balance between nitrogen supply and demand (Guo et al., 2017; Wu et al., 2023 ; Li et al., 2024 ). CCU can significantly promote the growth, yield, and nitrogen-use efficiency of maize by enhancing nitrogen absorption, promoting photosynthesis, upregulating carbon and nitrogen metabolism-related gene expression, and improving enzyme activity (Li et al., 2022c ). However, the physiological and molecular mechanisms of plant carbon and nitrogen metabolism under CCU treatment require further investigation. Hence, this study sought to investigate the physiological and molecular mechanisms of carbon and nitrogen metabolism in maize under different controlled-release urea and urea treatments. The research objectives were: (1) How does CCU alter the supply-demand balance between maize leaves and grains, and drive carbon and nitrogen allocation? (2) At which metabolic hubs does CCU enhance carbon-nitrogen co-regulation in maize? 2. Materials and Methods 2.1. Study sites, experimental design, and plant material Field experiments were conducted at two years in 2023 and 2024: The Yuanyang Science and Education Park of Henan Agricultural University, located in Xinxiang (113.94°E, 35.11°N). The site experiences a temperate monsoon climate. During the maize growing seasons, average temperatures were 25.80℃ and 25.45℃, with cumulative precipitation measuring 561.17 mm and 687.11 mm in 2023 and 2024, respectively. Precipitation and temperature data from June to October during the maize-growing season are presented in Fig. S1 . The test soil was fluvio-aquic. Initial topsoil (0–20 cm) properties: Organic matter 13.08 g kg⁻¹, total N 0.74 g kg⁻¹, available P 8.2 mg kg⁻¹, available K 133.10 mg kg⁻¹, pH 7.22. A completely randomized block design with three replicates per treatment and a total of 15 microplots was used. Each microblock measured 40 m 2 (5 × 8 m), and the planting density reached 75,000 plants ha − 1 . The major local cultivar used the maize variety Zhengdan958 (ZD958) (Yan et al., 2014 ). The experiment was conducted with two fertilization levels, CK (no fertilization) and 180 kg ha − 1 fertilizer, and four different fertilization conditions were used: U (all Urea-N), C1 (CRU-N: Urea-N = 1:2), C2 (CRU-N: Urea-N = 2:1), and C3 (all CRU-N). The fertilizer in treatments C1, C2, and C3 was uniformly applied once as basal fertilizer. In contrast, the fertilizer in the U treatment was split into two applications: once as basal fertilizer and again at the jointing stage, with a basal-to-topdressing ratio of 2:3. The experimental fertilizers included common urea containing 46% total nitrogen and controlled-release nitrogen with a total nitrogen content of 45%. The release curve of the controlled-release nitrogen fertilizer is shown in Fig. S2 . Urea was sourced from a general market, whereas the controlled-release nitrogen fertilizer was a polyurethane-coated product manufactured by the Anhui Maoshi New Fertilizer Company. Adequate amounts of phosphate (90 kg P 2 O 5 ha − 1 ) and potash (90 kg K 2 O ha − 1 ) fertilizers were applied prior to sowing in each plot. The planting and harvest dates for the maize are presented in Table S1 . Field management measures were regularly implemented to effectively control insects, weeds, and diseases. 2.2. Sampling and measurements Three representative plants were selected from each plot during the anthesis, filling, and maturation stages. Fresh ear leaf and grain samples from three representative maize plants in each plot were obtained. Soluble proteins were measured using the Coomassie Brilliant Blue staining method (Bradford et al., 1976), whereas FAAs were detected using the ninhydrin coloration method (Yemm et al., 1955; Lyu et al., 2022 ). The leaves and grains of the plants were dried at 105°C for 0.5 h and then oven-dried at 80°C until a constant weight was achieved. After drying, the plant samples were finely ground and sieved through a 1-mm mesh screen. The total nitrogen content was determined using Kjeldahl's method, whereas the total carbon content was measured using the potassium dichromate heating method (Gao et al., 2022 ). Soluble sugars and starch were extracted using alcohol and HCI, respectively, and measured using the anthrone colorimetric method (Hansen and Moller, 1975 ). The sucrose content was determined using the resorcinol method (Hendrix et al., 1993; Li et al., 2023 ). The dried aboveground plant parts were divided into vegetative and reproductive organs at the anthesis, filling, and mature stages. The dry matter of the plants was weighed to calculate the dry matter translocation (DMT), dry matter translocation efficiency (DMTE), and contribution of pre-anthesis dry matter to grain (CDMG) using the formulas provided in Supplementary Table S3 (Papakosta and Gagianas, 1991 ; Pal et al., 2017 ). After the crop reached physiological maturity, a 2.4 m 2 (1.2 × 2 m) square was randomly placed in each plot to collect the ears and establish the number of grain-bearing ears per plant or prolificacy (quotient between harvested ears and the number of plants). Six plants were randomly selected from each plot to determine ear row number and row grain number. After the grains were dried, the grain yield was determined by measuring the grain weight and water content. In the maize filling stage, three maize plants were selected from each treatment, and the ear leaves were promptly frozen at -80 ℃ for transcriptome sequencing and metabolome analysis. Quantitative real-time PCR (qRT-PCR) validation was also performed, with the actin gene ( Zm00001d013367 ) used as an internal reference to normalize the gene expression levels (Table S2 ). 2.3. Transcriptome analysis Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, 15596018), and RNA extraction libraries were constructed for sequencing. The transcriptome was sequenced on an Illumina NovaSeq™ 6000 sequencing platform using the Illumina paired-end RNA-seq method, which generated 1 million paired-end reads of 2×150 bp. The original sequence reads were stored in the NCBI SRA under PRJNA1196689, and the accession numbers for individual samples are provided in Table S4 . After filtering low-quality and adapter-containing reads from the raw data, clean reads were aligned to the maize reference genome using HISAT2 (Kim et al., 2019). After comparison, the gene expression levels were quantified by estimating the per kilobase exon per million reads mapped (FPKM) values, and differentially expressed genes (DEGs) were identified at Log2 (fold change) (Log2FC) ≥ 1 and false discovery rate (FDR) < 0.05 (Benjamini and Hochberg, 1995 ). Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the DEGs. 2.4. Metabolome analysis Maize leaf samples were subjected to the extraction method described by Zhang et al. ( 2024a ), and approximately 20 mg of freeze-dried leaf powder was extracted. Subsequently, an extraction solution (MeOH: ACN: H2O, 2:2:1 (v/v)) was added to the samples, followed by homogenization and ultrasonic treatment. The mixture was incubated at -40°C for 1 h to precipitate proteins and then centrifuged at 12,000 rpm (RCF = 13,800 × g , R = 8.6 cm) for 15 min at 4°C to collect the supernatant. For metabolites, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses were performed using an ultra-high performance liquid chromatography system (Vanquish, Thermo Fisher Scientific) with a Phenomenex Kinetex C18 (2.1 × 50 mm, 2.6 µm) coupled to an Orbitrap Exploris 120 mass spectrometer (Orbitrap MS, Thermo). The mobile phase consisted of 0.01% acetic acid in water as mobile phase A and a mixture of isopropanol (IPA) and acetonitrile (ACN) (1:1, v/v) as mobile phase B. The auto-sampler temperature was maintained at 4 ℃, and the injection volume was set at 2 µL. The Orbitrap Exploris 120 mass spectrometer was used to acquire MS/MS spectra in information-dependent acquisition (IDA) mode under the control of acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluated the full-scan MS spectrum (Want et al., 2010 ; Cai et al., 2015 ). After data preprocessing, metabolite identification was conducted using R packages and BiotreeDB (V3.0) (Zhou et al., 2022 ). Metabolites meeting the criteria of VIP (Variable importance in projection) > 1, p 1 were considered differentially accumulated metabolites (DAMs). DAMs were then subjected to KEGG enrichment analysis. 2.5. Statistical and data analyses IBM SPSS Statistics 26 software was utilized to perform one-way and two-way analyses of variance (ANOVAs) to explore the relationship between variables and their impact on the results. Prior to analysis, all data were subjected to normality and homogeneity tests. Tukey’s post hoc test was used for all data, with statistical significance set at p < 0.05. Origin 2021 (OriginLab, Northampton, MA, USA) and Microsoft Excel 2016 were used to generate charts. Adobe Illustrator 2021 (Adobe, San Jose, CA, USA) was used for chart integration. Metabolomics and transcriptomics analyses were performed using the cloud platform provided by lims2 ( https://biotree.lims2.com/ , Shanghai Baiqu Biomedical Technology Co., LTD). 3. Results 3.1. Yield and dry matter partitioning The use of CRU significantly enhanced the maize yield, and this trend was consistently observed over two years. In 2023, the C1 and C2 treatments increased the yield by 16.3% and 22.8% compared with the U treatment, respectively. Similarly in 2024, the C1 and C2 treatments increased the yield by 10.9% and 18.3% compared with the U treatment, respectively (Fig. 1 ). The data in Table S5 demonstrate that nitrogen application significantly enhanced the accumulation of dry matter in plants. In 2023 and 2024, the dry matter in the C1 and C2 treatments increased by 5.1–19.5% and 7.6–24.8% compared with those under the U treatment, respectively. The application of nitrogen fertilizer from anthesis to maturity resulted in significant increases in DMT, DMTE, and CDMG in the C1, C2, and C3 treatments compared with the U treatment in 2023 and 2024, with improvements ranging from 4.7% to 33.1%, 3.1% to 22.6%, and 0.3% to 17.9%, respectively (Table S5). 3.2. Nitrogen content, soluble protein, and free amino acid in maize leaves and grains The application of controlled-release urea can significantly enhance the nitrogen content of plants. As the growth period of maize progressed, a decreasing curve with a single peak was observed for the total nitrogen of all treated leaves in two years, whereas an upward trend in the nitrogen content was observed for maize grains (Fig. 2 a). The nitrogen content in the leaves and grains significantly increased following the C2 treatment than after the U treatment in 2023 and 2024, with increases ranging from 27.0% to 80.5% and 30.0% to 47.5% for the leaf nitrogen content, respectively, and from 41.5% to 56.6% and 45.5% to 61.5% for the grain nitrogen content, respectively. The time course of changes in soluble proteins and FAAs in the plants is shown in Fig. 2 b, c. The soluble protein and FAA contents peaked at the anthesis stage, followed by a gradual decline, and the changes at the two years were similar. The soluble protein content in the leaves from C2 and C3 in 2023 and 2024 exhibited an increase ranging from 8.2% to 69.0% and 6.7% to 24.8%, respectively, compared with U treatment. Additionally, the soluble protein content in the grains from C2 in 2023 and 2024 increased from 4.6% to 14.4% and 25.7% to 32.4%, respectively, compared with that from U. In two years, the FAA content of the C1 and C2 leaves during anthesis and filling stages were increased by 4.9–8.1% and 6.8–11.5%, respectively, compared with that of U. Furthermore, the FAA content of grains from C2 increased by 3.7–19.6% and 13.0–15.9%. 3.3. Carbon content, soluble sugar, sucrose content and starch in maize leaves and grains The carbon content of plant organs exhibited significant differences (p < 0.05; Fig. 3 a, b). Leaf carbon content peaked at anthesis, which gradually decreased to a stable level during the filling and maturity stages across two years. Conversely, the grain carbon content increased from the filling to maturity stage (Fig. 3 a). Relative to the U treatment, C1 and C2 treatments increased leaf carbon content by 1.6–38.0% and grain carbon by 12.6–35.7% in 2023 and 2024, respectively. Under nitrogen application conditions, the non-structural carbohydrate (NSC) content in the leaves decreased with plant growth (Fig. 3 b-c, S3). In 2023 and 2024, the C2 treatments exhibited 17.2–32.9% and 6.1–25.3% higher soluble sugar content in the leaves and 11.7–27.5% and 4.5–8.4% higher soluble sugar content in the grains, compared with the U treatment, respectively (Fig. 3 b). In two years, the C2 treatment exhibited 17.7–46.2% and 18.0–23.4% higher sucrose content in the leaves and 3.4–19.5% and 11.5–40.6% higher sucrose content in the grains, compared with the U treatment, respectively (Fig. S3 ). The C1 and C2 treatments exhibited 1.8–33.9% and 1.7–16.0% higher starch content in the leaves and grains compared with that after the U treatment, respectively (Fig. 3 c). 3.4. Carbon-nitrogen coordination and correlation analysis After the anthesis stage, the C/N ratio in leaves tended to rise over time; whereas in kernels, it decreased from the grain-filling stage to maturity (Fig. 4 a). In addition, the application of controlled-release urea caused changes in the plant C/N ratio. Compared with that in U, the C/N ratio of the leaves and grains in C2 decreased by 2.3–21.4% and 1.0–18.8% (Fig. 4 a). Correlation analysis revealed that C/N ratio significantly associated with nitrogen components (nitrogen content, soluble protein, free amino acids) and carbon metabolites (carbon content, soluble sugars, sucrose, starch). Total dry matter positively correlated with C/N ratio. Notably, yield was strongly linked to dry matter traits (total dry matter, DMT, DMTE, CDMG) and key indices (nitrogen content, soluble protein, carbon content, soluble sugars) (Fig. 4 b). 3.5. Pathway signatures from transcriptomics and metabolomics Transcriptomic profiling of 12 samples (4 treatments × 3 replicates) revealed high-quality sequencing data (Q30 > 97.46%, inter-replicate r > 0.88; Fig. S4 a, Table S6). PCA confirmed that the expression of genes under various treatment concentrations varied significantly (Fig. S4 b), with qRT-PCR validating RNA-seq reliability (R²=0.786; Fig. S4 c, Table S2 ). Differential gene expression decreased with rising CRU proportion: C1/U (2779 DEGs) > C2/U (1500) > C3/U (565), sharing 1498 core DEGs (Fig. S4 d-e). GO terms and KEGG enrichment analysis ( p < 0.05, q < 0.05) consistently highlighted carbohydrate and amino acid metabolism as top enriched pathways (Fig. 5 a, S5). The expression of metabolites changed significantly under different controlled-release urea treatments, indicating that the identified metabolites were reliable (Fig. S6a, b). There were 944 metabolite-enriched terms shared across all comparisons, and 195, 163, and 134 unique terms were enriched in the C1/U, C2/U, and C3/U comparisons, respectively. Metabolomics confirmed the proportion of controlled-release urea increased increases in phenylalanine/galactose metabolism, in accordance with elevated amino acid and carbohydrate metabolism (Fig. 5 b, S6c). 3.6 Transcriptomics and metabolomics correlation analysis In Fig. 6 , compared with U, C1, C2, and C3 presented 50, 43, and 31 DEGs associated with amino acids, respectively. Collectively, these treatments regulated 41 enzymes, with 57 upregulated genes and 8 downregulated genes. Core biosynthetic genes exhibited consistent upregulation across all treatments, including glyceraldehyde 3-phosphate dehydrogenase (phosphorylating) (GAPDH), glutamate synthase (GLT1), 5-methyltetrahydropteroyltriglutamate-homocysteine methyltransferase (METE), anthranilate phosphoribosyltransferase (TRPD), S-adenosylmethionine synthetase(METK), shikimate kinase (AROK), indole-3-glycerol phosphate synthase (TRPC), enolase (ENO), glutamine synthetase (GLNA), asparagine synthase (glutamine-hydrolyzing) (ASNB), delta-1-pyrroline-5-carboxylate synthetase (P5CS), LL-diaminopimelate aminotransferase (L-DA), and aspartate kinase (LYSC) (Fig. 6 , Tables S8 and S9). To better understand the variations in carbon metabolism in the C1/U, C2/U, and C3/U comparisons, we conducted a transcriptomics and metabolomics analysis of glyoxylate and dicarboxylate metabolism and amino sugar and nucleotide sugar metabolism in the carbon metabolism network (Fig. 6 , Tables S8 and S9). We identified 58 DEGs and 5 metabolites involved in carbon metabolism. Compared with U, C1, C2, and C3 presented 37, 30, and 23 DEGs associated with carbon metabolism, respectively. Collectively, these treatments regulated 30 enzymes, with 46 upregulated genes and 12 downregulated genes. Specifically, UDP glucose 6-dehydrogenase (UGDH), GLNA, chitinase (CTI), UDP-glucuronate 4-epimerase (GAE), acetate/butyrate-CoA ligase (AAE7), and oxalate-CoA ligase (AAE3) were significantly upregulated in all treatments. The application of controlled-release urea in our study reduced the levels of cis-aconitate, glyceric acid, oxalic acid, glutamate, and glutamine compared with those under the U treatment. Other functional metabolites were also significantly upregulated during the biosynthesis of amino acids, including tyrosine, leucine, phenylalanine, homocitric acid, alanine, 2-Isopropylmalic acid, 3-Isopropylmalic acid, glutamaine and glutamate compared with those under the U treatment (Fig. 6 , Table S7, S8). Correlation analysis (Fig. S7) showed that the C/N ratio was positively associated with Zm00001eb323090 (ICL) and cis-aconitate, but negatively correlated with Zm00001eb382240 (FDH), Zm00001eb028030 (GPT), Zm00001eb360480 (GLT1), glutamate and glutamine. Additionally, the levels of tyrosine, homocitric acid, and alanine in the leaves, as well as the expression of Zm00001eb365520 (AAE3), Zm00001eb424940 (IDH3), and GLT1, were all significantly positively correlated with the total dry matter and yield. We selected several representative genes for qRT-PCR analysis. Compared with the U treatment, the controlled-release urea treatment (especially CCU) significantly upregulated the expression of ACO and GLT1. 4. Discussion 4.1. Effects of CCU on nitrogen content and related indices of nitrogen metabolism in maize leaves and grains An increase in controlled-release urea (CRU) can significantly increase the N concentration in plant leaves and grains, thereby increasing the grain yield, which is consistent with our results (Fig. 1 , 2 a) (Hu et al., 2023 ; Li et al., 2024 ). In our study, the nitrogen content in the leaves gradually decreased during grain filling under the CCU treatments. Moreover, the nitrogen concentration in the grains exhibited a gradual increase (Fig. 2 a), mainly because CCU promoted the absorption and re-migration of nitrogen from the leaf after anthesis and transfer of nitrogen to the grain (Zheng et al., 2023 ; Li et al., 2024 ). Nitrogen application can affect the soluble proteins and FAA and promote nitrogen absorption in plants (Liang et al., 2023 ). In our study, the use of CCU promoted the soluble protein and FAA content in the leaves, which were the highest at the anthesis stage (Fig. 2 b, c). During grain filling, the FAAs levels in grains treated with CCU were markedly higher than that under the common urea treatment, mainly because the FAAs in the grains were redistributed along with nitrogen from the source to the sink and transferred mainly due to the breakdown of proteins stored within nutrient tissues through the phloem (Tegeder et al., 2018; Zhang et al., 2022b ). 4.2 Effects of CCU on the carbon content, dry matter, and non-structural carbohydrate content in maize leaves and grains Leaf nitrogen plays a crucial role in photosynthesis and carbon production following anthesis, serving as a key source of nitrogen for grains (Ning et al., 2018 ). The application of controlled-release urea can increase plant carbon content and promote carbon flow (Jiang et al., 2024 ). Compared with that under the U treatment, the carbon content under the CCU treatment increased significantly, with that in the leaves decreasing the from the anthesis to the maturity stage and then transferring to the grains. At maturity, the maximum carbon content was observed in the grains (Fig. 3 a). Nitrogen application has been reported to enhance the photosynthetic capacity of leaves and increase NSC content, reflecting the balance between carbon uptake and utilization in plants (Liu et al., 2016 ; Ning et al., 2018 ). Our previous study indicated that CRU increased leaf SPAD values and leaf area, along with leaf nitrogen content, thereby establishing the basis for carbon flow (Li et al., 2024 ). In our study, the CCU treatment promoted an increase in soluble sugars, sucrose, and starch in maize (Fig. 3 b-c, S3). The contents of soluble sugars and sucrose in the leaves remained stable or increased from the anthesis to the filling stage before gradually declining. Simultaneously, the starch content of the leaves decreased continuously from anthesis to maturity (Fig. 3 b-c, S3). This change may be attributed to the promotion of photosynthetic carbon fixation by CRU application. A portion of the soluble sugars and sucrose produced via photosynthesis is utilized for leaf metabolism, whereas another portion is directly transported to the grain. CRU also promotes the accumulation of starch in the leaves, which subsequently decomposes into soluble sugars and sucrose before being transported to the grain through vascular tissues (Kumar et al., 2018 ; Li et al., 2022c ; Ma et al., 2024 ). Correlation analysis revealed a significant positive association between the plant carbon and nitrogen contents (Fig. S4 b). Compared with the urea treatment, the application of CCU significantly reduced the plant C/N ratio (Fig. 4 a), indicating enhanced leaf nitrogen assimilation efficiency and improved phloem translocation of protein synthesis products (Tegeder et al., 2018; Zhang et al., 2018 ; Li et al., 2024 ). Specifically, the post-anthesis leaf C/N ratios exhibited a progressive increase (Fig. 4 a), which was likely due to CCU-induced stimulation of photosynthetic activity and subsequent carbon assimilation (Zhang et al., 2020 ). Conversely, the decline in CRU grain C/N ratios during late grain filling suggests preferential nitrogen remobilization towards developing kernels (Zhang et al., 2018 ). These source–sink dynamics align with the "carbon–nitrogen allocation equilibrium theory" proposed by Hu et al. ( 2008 ), whereby biomass variation fundamentally stems from altered C-N partitioning patterns. Supporting data (Table S5) further demonstrate that the CCU treatment markedly improved dry matter translocation efficiency from source to sink organs, consistent with our previous findings (Li et al., 2024 ). 4.3 Amino acid metabolism and carbon metabolism pathways are activated under the CCU application A comprehensive analysis of the transcriptome and metabolome revealed that CRU regulate carbon and nitrogen metabolism in maize leaves (Fig. 6 ). Nitrogen undergoes a series of transfer reactions through amino acids released by protein hydrolysis, particularly glutamic acid and aromatic amino acids (Peoples and Dalling, 1988 ). The CCU application upregulated the expression of such as glutamate synthase (GLT1), and aspartate aminotransferase (GOT1), thereby enhancing the synthesis of glutamate and glutamine in leaves (Fig. 6 ). These two amino acid levels were significantly higher under CCU treatment than under U treatment (Fig. 6 ), and were significantly negatively correlated with the C/N ratio (Fig. S7), indicating that nitrogen was effectively recycled and redistributed to processes such as protein synthesis (Fig. 6 , Fig. S7). Concurrently, the accumulation of the aromatic amino acids phenylalanine and tyrosine was higher under CCU treatment than under urea treatment (C2/U and C3/U), coinciding with activation of downstream flavonoid biosynthesis pathways (Fig. 6 , Table S8). This may suggest that CRU is not only prioritized for primary metabolism (e.g., protein synthesis) but also diverted to structural components and secondary metabolite production (Maeda et al., 2012; Yoo et al., 2013 ; Hildebrandt et al., 2015 ). Notably, the CCU treatment significantly promoted alanine accumulation, and alanine levels were significantly positively correlated with yield (Fig. S7), indicating active nitrogen assimilation and coordinated carbon-nitrogen supply, with external nitrogen effectively integrated into amino acid biosynthesis pathways (Hildebrandt et al., 2015 ; Li et al., 2022b ). In carbon metabolism, CRU significantly enhanced the activity of glycolysis and the tricarboxylic acid cycle (TCA) pathway, by enhancing the activity of rate-limiting enzymes in the HK, PFKA, and PK (Fig. 6 ), thereby sustaining carbon skeleton availability and energy homeostasis (Li et al., 2022a ; Yang et al., 2023 ). In this study, the application of CCU significantly enhanced the activity of the glyoxylate and dicarboxylate pathway. Integrated analysis of amino acid profiles revealed that CCU promotes glutamate biosynthesis and accumulation by stimulating this pathway (Fig. 6 ). At the same time, CCU treatment reduced the levels of cis-aconitate, glyceric acid, and oxalic acid (Fig. 6 ). Notably, cis-aconitate exhibits significant positive correlations with glyceric acid and the C/N ratio (Fig. S7), indicating preferential channeling of carbon precursors into nitrogen assimilation processes, such as α-ketoglutarate utilization for glutamate biosynthesis (Foyer et al., 2003 ; Li et al., 2022b ). This aligns with the reduced leaf C/N ratio (Fig. 4 a), further supporting a nitrogen-sufficient metabolic state in plants, which facilitates enhanced protein synthesis and growth. Furthermore, CCU activated the amino sugar and nucleotide sugar metabolic pathways (Fig. 6 ), which not only provides a sufficient nitrogen supply but also enables its integration into structural biomass synthesis and glycosylation processes (Foyer et al., 2003 ; Figueroa et al., 2021 ; Zhang et al., 2024b ). Overall, CCU enhances the expression of key nitrogen assimilation enzymes (e.g., GLT1) and core enzymes of the TCA cycle (e.g., IDH3 and ACO), promoting the synthesis of glutamate and glutamine. This drives the accumulation of aromatic amino acids and alanine, effectively reducing the leaf C/N ratio and achieving efficient synergy in carbon–nitrogen metabolism. The resulting regulatory network ultimately promotes the synchronous increase in dry matter accumulation and crop yield. In summary, this study identified Zm00001eb360480 (GLT1) and Zm00001eb424940 (IDH3) as candidate genes with high confidence for regulating carbon and nitrogen metabolism under CRU application. GLT1 functions in nitrogen assimilation, with its expression significantly negatively correlated with the C/N ratio and positively correlated with dry matter accumulation and yield; IDH3, as a key enzyme in the TCA cycle, likely provides carbon skeleton support for nitrogen assimilation by supplying α-ketoglutaric acid, and its expression is also significantly positively correlated with dry matter and yield. The two genes link nitrogen and carbon metabolism, respectively, and their expression profiles closely mirror key carbon–nitrogen physiological traits, suggesting that they play an important role in carbon-nitrogen coordination and can be potential targets for molecular breeding or efficient nitrogen utilization. The plant types and experimental regions were limited in this study, and other plants and regions may have specific requirements for the optimal balance between controlled-release urea and urea. To better understand the dynamic changes in plant carbon and nitrogen metabolism under different conditions and optimize fertilizer management strategies, we plan to use multi-omics data to build systems biology-based models to simulate and predict the response of crops to the combined application of controlled-release urea and urea in future studies. 5. Conclusion In conclusion, CCU Optimized C and N allocation in maize by elevating leaf and grain C/N ratios and metabolite pools (soluble proteins, FAAs, non-structural carbohydrates), with accelerated dry matter transport to reproductive organs quantitatively explaining the yield gains (> 18.3% under C2). Key metabolic pathway analysis reveals CCU simultaneously regulates N assimilation (amino acid synthesis) and carbon metabolism (glyoxylate and dicarboxylate, and amino sugar and nucleotide pathways), achieving synchronized C-N supply through metabolic nodes like alanine accumulation. CCU optimizes nutrient supply and utilization while clarifying maize source-sink relationships via coordinated C-N metabolic mechanisms. Declarations Acknowledgements The authors would like to thank Editage (www.editage.cn) for the English language editing. Funding This research was financially supported by National Natural Science Foundation of China, grant number 32573155. Author contributions Huan Li: Conceptualization, Investigation, Methodology, Software, Writing –original draft. Yiming Zhu: Resources. Menglin Bai: Writing – review & editing. Yihan Zhang : Writing – review & editing. Huiyang Zhu: Writing – review & editing. Weixiao Hu: Writing – review & editing. Yongchao Wang: Conceptualization, Methodology, Writing – review & editing. Wushuai Zhang: Conceptualization, Methodology, Writing – review & editing. XinPing Chen: Conceptualization, Methodology, Writing – review & editing. Qinghua Yang: Conceptualization, Investigation, Methodology, Writing – review & editing. Supervision, Funding acquisition. Jiameng Guo: Conceptualization, Methodology, Writing – review & editing, Supervision, Funding acquisition. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. Data availability The datasets generated and/or analysed during the current study are available from the website of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov) in the NCBI Sequence Read Archive (SRA) repository under the BioProject accession number PRJNA1196689. The SRA accession numbers for individual samples are SRR31690063, SRR31690062, SRR31690061, SRR31690070, SRR31690068, SRR31690060, SRR31690059, SRR31690064, SRR31690066, SRR31690065, SRR31690069, and SRR31690067, and are also provided in Table S4. Data will be made available on request if additional information is needed. Competing interests The authors state no conflict of interest. References Benjamini Y, Hochberg Y. 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CK, no fertilisation; U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N. Error bars represent SE. Values with different letters are significantly different at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 within the same parameter.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/65261c7cbac37d6626fd2404.png"},{"id":104258053,"identity":"5be82fed-7be1-474c-ac60-197cc93e03fd","added_by":"auto","created_at":"2026-03-09 17:32:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410428,"visible":true,"origin":"","legend":"\u003cp\u003eResponses of the nitrogen content (a), soluble protein (b), and free amino acid (c) of the maize leaves and grains to nitrogen application in 2023 and 2024. Leaves are shown in line charts and grains in bar charts. CK, no fertilisation; U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N. Error bars represent SE. Values with different letters are significantly different at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 within the same parameter.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/919f8dfa7310b5e03c3b7c4c.png"},{"id":104405138,"identity":"1f0b0607-1e90-4231-89a9-9fb48eea6ce9","added_by":"auto","created_at":"2026-03-11 12:21:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":407865,"visible":true,"origin":"","legend":"\u003cp\u003eResponses in the carbon content (a), soluble sugar content (b), and starch content (c) of maize leaves and grains to nitrogen application in 2023 and 2024. Leaves are shown in line charts and grains in bar charts. CK, no fertilisation; U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N. Error bars represent SE. Values with different letters are significantly different at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 within the same parameter.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/49f1e49da15ebadc32f086be.png"},{"id":104405200,"identity":"d1415125-1875-4064-b545-ed13aae35aec","added_by":"auto","created_at":"2026-03-11 12:22:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":613630,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Temporal dynamics of C/N ratio in maize leaves (line chart) and grains (bar chart) under nitrogen fertilization. (b) Spearman correlation matrix of physiological traits responding to integrated agronomic management (* \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01; *** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). DMT, dry matter translocation; DMTE, dry matter translocation efficiency efficiency; CDMG, Contribution of dry matter to grain; U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N. Error bars represent SE. Values with different letters are significantly different at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 within the same parameter.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/9bfd11cf5cf2c3832a9f4add.png"},{"id":104404798,"identity":"2a801f2e-8426-4281-8339-905ad9f5aff2","added_by":"auto","created_at":"2026-03-11 12:21:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":730872,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Gene Ontology (GO) functional classification of differentially expressed genes (DEGs) in maize leaves under different fertilizer conditions. The horizontal coordinate represents the three major categories analysed. The vertical coordinates represent the number of DEGs under each term. (b) Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis was performed on differentially accumulated metabolites (DAMs) in maize leaves under various fertilizer conditions. The horizontal coordinate represents the percentage of differential metabolites annotated in one pathway out of all annotated differential metabolites. The vertical coordinates represent the names of the enriched KEGG metabolic pathways. U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/a77c3449ec1c5438b886c473.png"},{"id":104258047,"identity":"610c6b17-394e-4d44-a3f9-5c9c4521d810","added_by":"auto","created_at":"2026-03-09 17:32:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":535333,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of the correlation between differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs) associated with carbon metabolism and nitrogen metabolism. The Log2 ratios of fold changes observed during the filling stage are represented by varying shades of red (indicating upregulation of metabolites and genes) or green (indicating downregulation of metabolites and genes), as depicted in the provided colour scale bar. Abbreviations: AAE3, oxalate---CoA ligase; ACO, aconitate hydratase; ADT, arogenate/prephenate dehydratase; AROF, 3-deoxy-7-phosphoheptulonate synthase; AROK, shikimate kinase; CHIB, basic endochitinase B; CM, chorismate mutase; CTI, chitinase; FDH, formate dehydrogenase; GAE, UDP-glucuronate 4-epimerase; GALE, UDP-glucose 4-epimerase; GAUT, alpha-1, 4-galacturonosyltransferase; GLMS, glutamine---fructose-6-phosphate transaminase (isomerizing); GLNA, glutamine synthetase; GLT1, glutamate synthase (NADH); GLYK, D-glycerate 3-kinase; GMPP, mannose-1-phosphate guanylyltransferase; GNA1, glucosamine-phosphate N-acetyltransferase; GOT1, aspartate aminotransferase, cytoplasmic; GPT, alanine transaminase; HAO, (S)-2-hydroxy-acid oxidase; HEXA_B, hexosaminidase; HK, hexokinase; HPR1, glycerate dehydrogenase; HPR2_3, glyoxylate/hydroxypyruvate reductase; ICL, isocitrate/methylisocitrate lyase; IDH1, isocitrate dehydrogenase; IDH3, isocitrate dehydrogenase (NAD+); ILVE, branched-chain amino acid aminotransferase; ILVH, acetolactate synthase I/III small subunit; KATE, catalase; PAT, bifunctional aspartate aminotransferase and glutamate/aspartate-prephenate aminotransferase; RGP, reversibly glycosylated polypeptide / UDP-arabinopyranose mutase; TYRAAT, arogenate dehydrogenase (NADP+), plant ; UAP1, UDP-N-acetylglucosamine/UDP-N-acetylgalactosamine diphosphorylase; UGDH, UDPglucose 6-dehydrogenase; UXE, UDP-arabinose 4-epimerase; UXS1, UDP-glucuronate decarboxylase. U, all Urea-N; C1, CRU-N: Urea-N=1:2; C2, CRU-N: Urea-N=2:1; C3, all CRU-N.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/2248a5f2a0150f0aa91cc2b0.png"},{"id":104408816,"identity":"1bb52094-4059-4b31-bb3c-b98c33a04a50","added_by":"auto","created_at":"2026-03-11 12:43:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3938632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/00b96afb-dcbe-478d-8a73-6b68a19dbab5.pdf"},{"id":104404583,"identity":"687e393e-c4c0-4fc7-a3a3-8af2aea7e43c","added_by":"auto","created_at":"2026-03-11 12:20:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3907710,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/8d723dca1c2079ab6c2961e9.docx"},{"id":104404786,"identity":"d6931fa6-f686-403c-9396-500c575cadea","added_by":"auto","created_at":"2026-03-11 12:21:04","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":299520,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.keggenrichpathway.xls","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/bd7da41a45a011c1dbc75cd0.xls"},{"id":104404612,"identity":"1187a299-ce9f-491d-b5a8-4723b43a6211","added_by":"auto","created_at":"2026-03-11 12:20:38","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":71680,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.keggenrichpathwayGene.xls","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/576b7dcc40d1c8a139929703.xls"},{"id":104258054,"identity":"a619da70-b0fb-408e-b7d0-6fc0af61b1d4","added_by":"auto","created_at":"2026-03-09 17:32:24","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1160964,"visible":true,"origin":"","legend":"","description":"","filename":"file.tif","url":"https://assets-eu.researchsquare.com/files/rs-8930203/v1/13fb991acf6ece2cf484e9f1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eControlled-release urea optimizes the pathway to yield increase via post-anthesis carbon-nitrogen coordination in maize\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) represents an important crop for global food security and economic development and is widely used as a food, feed, bioenergy source, and industrial raw material (Erenstein et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nitrogen (N) plays an important role in crop growth and -yield based on its inclusion in vital components in plants, such as proteins, nucleic acids, and chlorophyll, and participation in metabolic processes such as photosynthesis and respiration (Gir\u0026oacute;n-Calva et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). For a long time, farmers have overused nitrogen fertilizer to improve yields, which has massively increased nitrogen waste, soil quality deterioration, and environmental pollution (Chang et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent years, the Chinese government has emphasized the efficient use of fertilizer resources and the pollution caused by fertilizers to ensure food security and environmental sustainability. Fertilizer products need to be transformed and upgraded, and fertilizers need to meet the nutritional needs of crop growth while maintaining an appropriate soil fertilization capacity and reducing environmental pollution (Wu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA previous study showed that the proportion of nitrogen absorption in the post-anthesis stage to the total nitrogen absorption at maturity significantly increased from 12% to 32% as the maize yield increased (Meng et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution of N from the source to the sink organs is affected by the absorption and metabolism of N by the source organs as well as the capacity for source output and sink input. The ear leaves (source) provide the necessary energy for grain (sink) formation (Fan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nitrogen fertilizer application can increase crop nitrogen accumulation after anthesis and subsequently improve protein content. During the grain-filling stage, proteins stored in leaves are rapidly broken down into free amino acids (FAAs), which are then transported to the grains through the phloem and ultimately synthesized into storage proteins (Hav\u0026eacute; et al., 2017; Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Nitrogen fertilization can stimulate the allocation of sugars and starches to organic acid pools, thereby promoting amino acid synthesis and assimilation and ultimately enhancing crop yield (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Additionally, nitrogen supply can enhance leaf photosynthesis by providing more nitrogen and carbon sources to the leaves and promoting the transport of many photosynthates from the leaves to the grains (Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This highlights the essential role of nitrogen in carbon capture, synthesis, and transportation from the source to sink organs.\u003c/p\u003e \u003cp\u003eDuring the grain-filling stage, which is a crucial period of grain maturation, nitrogen reuse in source leaves becomes the focus, leading to significant reductions in photosynthesis and nitrogen and carbon assimilation (Tegeder et al., 2018). By integrating transcriptomics and metabolomics data, gaining insights into how genes and metabolic pathways within plants constitute complex regulatory networks is possible (Xin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nitrogen supply significantly enhances carbon fixation during photosynthesis and the activities of key carbon-metabolizing enzymes, such as invertase and amylase, which helps to accelerate the conversion process of starch and sucrose and provides the necessary carbon skeleton for nitrogen metabolism (Foyer et al., 2011; Xin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The growth of aboveground rice is regulated by the interaction between carbon and nitrogen, and the availability of carbon and nitrogen has complex regulatory effects on enzymes such as nitrate reductase (NR), glutamine synthetase (GS), and phosphoenolpyruvate carboxylase (PEPC); thus, certain metabolic genes are co-regulated by both carbon and nitrogen levels (Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This regulation maintains the balance of carbon and nitrogen in plants through growth regulation and metabolic gene expression changes, which are required for plant growth and development (Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe slow-release properties of controlled-release urea (CRU) aid in enhancing the balance of carbon and nitrogen metabolism by refining the nitrogen supply strategy of crops This improvement not only enhances the photosynthetic capacity of the leaves but also promotes biomass accumulation after the anthesis stage (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The combined application of CRU and urea (CCU) can address the issue of inadequate nitrogen release. in the initial stage of CRU and ensure the continuous availability of nitrogen, thus optimizing the balance between nitrogen supply and demand (Guo et al., 2017; Wu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). CCU can significantly promote the growth, yield, and nitrogen-use efficiency of maize by enhancing nitrogen absorption, promoting photosynthesis, upregulating carbon and nitrogen metabolism-related gene expression, and improving enzyme activity (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e). However, the physiological and molecular mechanisms of plant carbon and nitrogen metabolism under CCU treatment require further investigation. Hence, this study sought to investigate the physiological and molecular mechanisms of carbon and nitrogen metabolism in maize under different controlled-release urea and urea treatments. The research objectives were:\u003c/p\u003e \u003cp\u003e(1) How does CCU alter the supply-demand balance between maize leaves and grains, and drive carbon and nitrogen allocation?\u003c/p\u003e \u003cp\u003e(2) At which metabolic hubs does CCU enhance carbon-nitrogen co-regulation in maize?\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study sites, experimental design, and plant material\u003c/h2\u003e \u003cp\u003eField experiments were conducted at two years in 2023 and 2024: The Yuanyang Science and Education Park of Henan Agricultural University, located in Xinxiang (113.94\u0026deg;E, 35.11\u0026deg;N). The site experiences a temperate monsoon climate. During the maize growing seasons, average temperatures were 25.80℃ and 25.45℃, with cumulative precipitation measuring 561.17 mm and 687.11 mm in 2023 and 2024, respectively. Precipitation and temperature data from June to October during the maize-growing season are presented in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The test soil was fluvio-aquic. Initial topsoil (0\u0026ndash;20 cm) properties: Organic matter 13.08 g kg⁻\u0026sup1;, total N 0.74 g kg⁻\u0026sup1;, available P 8.2 mg kg⁻\u0026sup1;, available K 133.10 mg kg⁻\u0026sup1;, pH 7.22.\u003c/p\u003e \u003cp\u003eA completely randomized block design with three replicates per treatment and a total of 15 microplots was used. Each microblock measured 40 m\u003csup\u003e2\u003c/sup\u003e (5 \u0026times; 8 m), and the planting density reached 75,000 plants ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The major local cultivar used the maize variety Zhengdan958 (ZD958) (Yan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The experiment was conducted with two fertilization levels, CK (no fertilization) and 180 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e fertilizer, and four different fertilization conditions were used: U (all Urea-N), C1 (CRU-N: Urea-N\u0026thinsp;=\u0026thinsp;1:2), C2 (CRU-N: Urea-N\u0026thinsp;=\u0026thinsp;2:1), and C3 (all CRU-N). The fertilizer in treatments C1, C2, and C3 was uniformly applied once as basal fertilizer. In contrast, the fertilizer in the U treatment was split into two applications: once as basal fertilizer and again at the jointing stage, with a basal-to-topdressing ratio of 2:3. The experimental fertilizers included common urea containing 46% total nitrogen and controlled-release nitrogen with a total nitrogen content of 45%. The release curve of the controlled-release nitrogen fertilizer is shown in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Urea was sourced from a general market, whereas the controlled-release nitrogen fertilizer was a polyurethane-coated product manufactured by the Anhui Maoshi New Fertilizer Company. Adequate amounts of phosphate (90 kg P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and potash (90 kg K\u003csub\u003e2\u003c/sub\u003eO ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) fertilizers were applied prior to sowing in each plot. The planting and harvest dates for the maize are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Field management measures were regularly implemented to effectively control insects, weeds, and diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling and measurements\u003c/h2\u003e \u003cp\u003eThree representative plants were selected from each plot during the anthesis, filling, and maturation stages. Fresh ear leaf and grain samples from three representative maize plants in each plot were obtained. Soluble proteins were measured using the Coomassie Brilliant Blue staining method (Bradford et al., 1976), whereas FAAs were detected using the ninhydrin coloration method (Yemm et al., 1955; Lyu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The leaves and grains of the plants were dried at 105\u0026deg;C for 0.5 h and then oven-dried at 80\u0026deg;C until a constant weight was achieved. After drying, the plant samples were finely ground and sieved through a 1-mm mesh screen. The total nitrogen content was determined using Kjeldahl's method, whereas the total carbon content was measured using the potassium dichromate heating method (Gao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Soluble sugars and starch were extracted using alcohol and HCI, respectively, and measured using the anthrone colorimetric method (Hansen and Moller, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The sucrose content was determined using the resorcinol method (Hendrix et al., 1993; Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dried aboveground plant parts were divided into vegetative and reproductive organs at the anthesis, filling, and mature stages. The dry matter of the plants was weighed to calculate the dry matter translocation (DMT), dry matter translocation efficiency (DMTE), and contribution of pre-anthesis dry matter to grain (CDMG) using the formulas provided in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e (Papakosta and Gagianas, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Pal et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter the crop reached physiological maturity, a 2.4 m\u003csup\u003e2\u003c/sup\u003e (1.2 \u0026times; 2 m) square was randomly placed in each plot to collect the ears and establish the number of grain-bearing ears per plant or prolificacy (quotient between harvested ears and the number of plants). Six plants were randomly selected from each plot to determine ear row number and row grain number. After the grains were dried, the grain yield was determined by measuring the grain weight and water content.\u003c/p\u003e \u003cp\u003eIn the maize filling stage, three maize plants were selected from each treatment, and the ear leaves were promptly frozen at -80 ℃ for transcriptome sequencing and metabolome analysis. Quantitative real-time PCR (qRT-PCR) validation was also performed, with the actin gene (\u003cem\u003eZm00001d013367\u003c/em\u003e) used as an internal reference to normalize the gene expression levels (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Transcriptome analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, 15596018), and RNA extraction libraries were constructed for sequencing. The transcriptome was sequenced on an Illumina NovaSeq\u0026trade; 6000 sequencing platform using the Illumina paired-end RNA-seq method, which generated 1\u0026nbsp;million paired-end reads of 2\u0026times;150 bp. The original sequence reads were stored in the NCBI SRA under PRJNA1196689, and the accession numbers for individual samples are provided in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. After filtering low-quality and adapter-containing reads from the raw data, clean reads were aligned to the maize reference genome using HISAT2 (Kim et al., 2019). After comparison, the gene expression levels were quantified by estimating the per kilobase exon per million reads mapped (FPKM) values, and differentially expressed genes (DEGs) were identified at Log2 (fold change) (Log2FC)\u0026thinsp;\u0026ge;\u0026thinsp;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Benjamini and Hochberg, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Metabolome analysis\u003c/h2\u003e \u003cp\u003eMaize leaf samples were subjected to the extraction method described by Zhang et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e), and approximately 20 mg of freeze-dried leaf powder was extracted. Subsequently, an extraction solution (MeOH: ACN: H2O, 2:2:1 (v/v)) was added to the samples, followed by homogenization and ultrasonic treatment. The mixture was incubated at -40\u0026deg;C for 1 h to precipitate proteins and then centrifuged at 12,000 rpm (RCF\u0026thinsp;=\u0026thinsp;13,800 \u0026times;\u003cem\u003eg\u003c/em\u003e, R\u0026thinsp;=\u0026thinsp;8.6 cm) for 15 min at 4\u0026deg;C to collect the supernatant. For metabolites, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses were performed using an ultra-high performance liquid chromatography system (Vanquish, Thermo Fisher Scientific) with a Phenomenex Kinetex C18 (2.1 \u0026times; 50 mm, 2.6 \u0026micro;m) coupled to an Orbitrap Exploris 120 mass spectrometer (Orbitrap MS, Thermo). The mobile phase consisted of 0.01% acetic acid in water as mobile phase A and a mixture of isopropanol (IPA) and acetonitrile (ACN) (1:1, v/v) as mobile phase B. The auto-sampler temperature was maintained at 4 ℃, and the injection volume was set at 2 \u0026micro;L. The Orbitrap Exploris 120 mass spectrometer was used to acquire MS/MS spectra in information-dependent acquisition (IDA) mode under the control of acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluated the full-scan MS spectrum (Want et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). After data preprocessing, metabolite identification was conducted using R packages and BiotreeDB (V3.0) (Zhou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Metabolites meeting the criteria of VIP (Variable importance in projection)\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and | Log2FC) | \u0026gt; 1 were considered differentially accumulated metabolites (DAMs). DAMs were then subjected to KEGG enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical and data analyses\u003c/h2\u003e \u003cp\u003eIBM SPSS Statistics 26 software was utilized to perform one-way and two-way analyses of variance (ANOVAs) to explore the relationship between variables and their impact on the results. Prior to analysis, all data were subjected to normality and homogeneity tests. Tukey\u0026rsquo;s post hoc test was used for all data, with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Origin 2021 (OriginLab, Northampton, MA, USA) and Microsoft Excel 2016 were used to generate charts. Adobe Illustrator 2021 (Adobe, San Jose, CA, USA) was used for chart integration. Metabolomics and transcriptomics analyses were performed using the cloud platform provided by lims2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biotree.lims2.com/\u003c/span\u003e\u003cspan address=\"https://biotree.lims2.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Shanghai Baiqu Biomedical Technology Co., LTD).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Yield and dry matter partitioning\u003c/h2\u003e \u003cp\u003eThe use of CRU significantly enhanced the maize yield, and this trend was consistently observed over two years. In 2023, the C1 and C2 treatments increased the yield by 16.3% and 22.8% compared with the U treatment, respectively. Similarly in 2024, the C1 and C2 treatments increased the yield by 10.9% and 18.3% compared with the U treatment, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data in Table S5 demonstrate that nitrogen application significantly enhanced the accumulation of dry matter in plants. In 2023 and 2024, the dry matter in the C1 and C2 treatments increased by 5.1\u0026ndash;19.5% and 7.6\u0026ndash;24.8% compared with those under the U treatment, respectively. The application of nitrogen fertilizer from anthesis to maturity resulted in significant increases in DMT, DMTE, and CDMG in the C1, C2, and C3 treatments compared with the U treatment in 2023 and 2024, with improvements ranging from 4.7% to 33.1%, 3.1% to 22.6%, and 0.3% to 17.9%, respectively (Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Nitrogen content, soluble protein, and free amino acid in maize leaves and grains\u003c/h2\u003e \u003cp\u003eThe application of controlled-release urea can significantly enhance the nitrogen content of plants. As the growth period of maize progressed, a decreasing curve with a single peak was observed for the total nitrogen of all treated leaves in two years, whereas an upward trend in the nitrogen content was observed for maize grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The nitrogen content in the leaves and grains significantly increased following the C2 treatment than after the U treatment in 2023 and 2024, with increases ranging from 27.0% to 80.5% and 30.0% to 47.5% for the leaf nitrogen content, respectively, and from 41.5% to 56.6% and 45.5% to 61.5% for the grain nitrogen content, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe time course of changes in soluble proteins and FAAs in the plants is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c. The soluble protein and FAA contents peaked at the anthesis stage, followed by a gradual decline, and the changes at the two years were similar. The soluble protein content in the leaves from C2 and C3 in 2023 and 2024 exhibited an increase ranging from 8.2% to 69.0% and 6.7% to 24.8%, respectively, compared with U treatment. Additionally, the soluble protein content in the grains from C2 in 2023 and 2024 increased from 4.6% to 14.4% and 25.7% to 32.4%, respectively, compared with that from U. In two years, the FAA content of the C1 and C2 leaves during anthesis and filling stages were increased by 4.9\u0026ndash;8.1% and 6.8\u0026ndash;11.5%, respectively, compared with that of U. Furthermore, the FAA content of grains from C2 increased by 3.7\u0026ndash;19.6% and 13.0\u0026ndash;15.9%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Carbon content, soluble sugar, sucrose content and starch in maize leaves and grains\u003c/h2\u003e \u003cp\u003eThe carbon content of plant organs exhibited significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Leaf carbon content peaked at anthesis, which gradually decreased to a stable level during the filling and maturity stages across two years. Conversely, the grain carbon content increased from the filling to maturity stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Relative to the U treatment, C1 and C2 treatments increased leaf carbon content by 1.6\u0026ndash;38.0% and grain carbon by 12.6\u0026ndash;35.7% in 2023 and 2024, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder nitrogen application conditions, the non-structural carbohydrate (NSC) content in the leaves decreased with plant growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c, S3). In 2023 and 2024, the C2 treatments exhibited 17.2\u0026ndash;32.9% and 6.1\u0026ndash;25.3% higher soluble sugar content in the leaves and 11.7\u0026ndash;27.5% and 4.5\u0026ndash;8.4% higher soluble sugar content in the grains, compared with the U treatment, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In two years, the C2 treatment exhibited 17.7\u0026ndash;46.2% and 18.0\u0026ndash;23.4% higher sucrose content in the leaves and 3.4\u0026ndash;19.5% and 11.5\u0026ndash;40.6% higher sucrose content in the grains, compared with the U treatment, respectively (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The C1 and C2 treatments exhibited 1.8\u0026ndash;33.9% and 1.7\u0026ndash;16.0% higher starch content in the leaves and grains compared with that after the U treatment, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Carbon-nitrogen coordination and correlation analysis\u003c/h2\u003e \u003cp\u003eAfter the anthesis stage, the C/N ratio in leaves tended to rise over time; whereas in kernels, it decreased from the grain-filling stage to maturity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In addition, the application of controlled-release urea caused changes in the plant C/N ratio. Compared with that in U, the C/N ratio of the leaves and grains in C2 decreased by 2.3\u0026ndash;21.4% and 1.0\u0026ndash;18.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Correlation analysis revealed that C/N ratio significantly associated with nitrogen components (nitrogen content, soluble protein, free amino acids) and carbon metabolites (carbon content, soluble sugars, sucrose, starch). Total dry matter positively correlated with C/N ratio. Notably, yield was strongly linked to dry matter traits (total dry matter, DMT, DMTE, CDMG) and key indices (nitrogen content, soluble protein, carbon content, soluble sugars) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Pathway signatures from transcriptomics and metabolomics\u003c/h2\u003e \u003cp\u003eTranscriptomic profiling of 12 samples (4 treatments \u0026times; 3 replicates) revealed high-quality sequencing data (Q30\u0026thinsp;\u0026gt;\u0026thinsp;97.46%, inter-replicate r\u0026thinsp;\u0026gt;\u0026thinsp;0.88; Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea, Table S6). PCA confirmed that the expression of genes under various treatment concentrations varied significantly (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eb), with qRT-PCR validating RNA-seq reliability (R\u0026sup2;=0.786; Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ec, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Differential gene expression decreased with rising CRU proportion: C1/U (2779 DEGs) \u0026gt; C2/U (1500) \u0026gt; C3/U (565), sharing 1498 core DEGs (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ed-e). GO terms and KEGG enrichment analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) consistently highlighted carbohydrate and amino acid metabolism as top enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe expression of metabolites changed significantly under different controlled-release urea treatments, indicating that the identified metabolites were reliable (Fig. S6a, b). There were 944 metabolite-enriched terms shared across all comparisons, and 195, 163, and 134 unique terms were enriched in the C1/U, C2/U, and C3/U comparisons, respectively. Metabolomics confirmed the proportion of controlled-release urea increased increases in phenylalanine/galactose metabolism, in accordance with elevated amino acid and carbohydrate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, S6c).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Transcriptomics and metabolomics correlation analysis\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, compared with U, C1, C2, and C3 presented 50, 43, and 31 DEGs associated with amino acids, respectively. Collectively, these treatments regulated 41 enzymes, with 57 upregulated genes and 8 downregulated genes. Core biosynthetic genes exhibited consistent upregulation across all treatments, including glyceraldehyde 3-phosphate dehydrogenase (phosphorylating) (GAPDH), glutamate synthase (GLT1), 5-methyltetrahydropteroyltriglutamate-homocysteine methyltransferase (METE), anthranilate phosphoribosyltransferase (TRPD), S-adenosylmethionine synthetase(METK), shikimate kinase (AROK), indole-3-glycerol phosphate synthase (TRPC), enolase (ENO), glutamine synthetase (GLNA), asparagine synthase (glutamine-hydrolyzing) (ASNB), delta-1-pyrroline-5-carboxylate synthetase (P5CS), LL-diaminopimelate aminotransferase (L-DA), and aspartate kinase (LYSC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Tables S8 and S9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo better understand the variations in carbon metabolism in the C1/U, C2/U, and C3/U comparisons, we conducted a transcriptomics and metabolomics analysis of glyoxylate and dicarboxylate metabolism and amino sugar and nucleotide sugar metabolism in the carbon metabolism network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Tables S8 and S9). We identified 58 DEGs and 5 metabolites involved in carbon metabolism. Compared with U, C1, C2, and C3 presented 37, 30, and 23 DEGs associated with carbon metabolism, respectively. Collectively, these treatments regulated 30 enzymes, with 46 upregulated genes and 12 downregulated genes. Specifically, UDP glucose 6-dehydrogenase (UGDH), GLNA, chitinase (CTI), UDP-glucuronate 4-epimerase (GAE), acetate/butyrate-CoA ligase (AAE7), and oxalate-CoA ligase (AAE3) were significantly upregulated in all treatments.\u003c/p\u003e \u003cp\u003eThe application of controlled-release urea in our study reduced the levels of cis-aconitate, glyceric acid, oxalic acid, glutamate, and glutamine compared with those under the U treatment. Other functional metabolites were also significantly upregulated during the biosynthesis of amino acids, including tyrosine, leucine, phenylalanine, homocitric acid, alanine, 2-Isopropylmalic acid, 3-Isopropylmalic acid, glutamaine and glutamate compared with those under the U treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S7, S8).\u003c/p\u003e \u003cp\u003eCorrelation analysis (Fig. S7) showed that the C/N ratio was positively associated with \u003cem\u003eZm00001eb323090\u003c/em\u003e(ICL) and cis-aconitate, but negatively correlated with \u003cem\u003eZm00001eb382240\u003c/em\u003e (FDH), \u003cem\u003eZm00001eb028030\u003c/em\u003e (GPT), \u003cem\u003eZm00001eb360480\u003c/em\u003e (GLT1), glutamate and glutamine. Additionally, the levels of tyrosine, homocitric acid, and alanine in the leaves, as well as the expression of \u003cem\u003eZm00001eb365520\u003c/em\u003e (AAE3), \u003cem\u003eZm00001eb424940\u003c/em\u003e (IDH3), and GLT1, were all significantly positively correlated with the total dry matter and yield. We selected several representative genes for qRT-PCR analysis. Compared with the U treatment, the controlled-release urea treatment (especially CCU) significantly upregulated the expression of ACO and GLT1.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cb\u003e4.1. Effects of CCU on nitrogen content and related indices of nitrogen metabolism in maize leaves and grains\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn increase in controlled-release urea (CRU) can significantly increase the N concentration in plant leaves and grains, thereby increasing the grain yield, which is consistent with our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) (Hu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, the nitrogen content in the leaves gradually decreased during grain filling under the CCU treatments. Moreover, the nitrogen concentration in the grains exhibited a gradual increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), mainly because CCU promoted the absorption and re-migration of nitrogen from the leaf after anthesis and transfer of nitrogen to the grain (Zheng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNitrogen application can affect the soluble proteins and FAA and promote nitrogen absorption in plants (Liang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, the use of CCU promoted the soluble protein and FAA content in the leaves, which were the highest at the anthesis stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). During grain filling, the FAAs levels in grains treated with CCU were markedly higher than that under the common urea treatment, mainly because the FAAs in the grains were redistributed along with nitrogen from the source to the sink and transferred mainly due to the breakdown of proteins stored within nutrient tissues through the phloem (Tegeder et al., 2018; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 Effects of CCU on the carbon content, dry matter, and non-structural carbohydrate content in maize leaves and grains\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLeaf nitrogen plays a crucial role in photosynthesis and carbon production following anthesis, serving as a key source of nitrogen for grains (Ning et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The application of controlled-release urea can increase plant carbon content and promote carbon flow (Jiang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Compared with that under the U treatment, the carbon content under the CCU treatment increased significantly, with that in the leaves decreasing the from the anthesis to the maturity stage and then transferring to the grains. At maturity, the maximum carbon content was observed in the grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Nitrogen application has been reported to enhance the photosynthetic capacity of leaves and increase NSC content, reflecting the balance between carbon uptake and utilization in plants (Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ning et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our previous study indicated that CRU increased leaf SPAD values and leaf area, along with leaf nitrogen content, thereby establishing the basis for carbon flow (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, the CCU treatment promoted an increase in soluble sugars, sucrose, and starch in maize (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c, S3). The contents of soluble sugars and sucrose in the leaves remained stable or increased from the anthesis to the filling stage before gradually declining. Simultaneously, the starch content of the leaves decreased continuously from anthesis to maturity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c, S3). This change may be attributed to the promotion of photosynthetic carbon fixation by CRU application. A portion of the soluble sugars and sucrose produced via photosynthesis is utilized for leaf metabolism, whereas another portion is directly transported to the grain. CRU also promotes the accumulation of starch in the leaves, which subsequently decomposes into soluble sugars and sucrose before being transported to the grain through vascular tissues (Kumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022c\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed a significant positive association between the plant carbon and nitrogen contents (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eb). Compared with the urea treatment, the application of CCU significantly reduced the plant C/N ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), indicating enhanced leaf nitrogen assimilation efficiency and improved phloem translocation of protein synthesis products (Tegeder et al., 2018; Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, the post-anthesis leaf C/N ratios exhibited a progressive increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), which was likely due to CCU-induced stimulation of photosynthetic activity and subsequent carbon assimilation (Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, the decline in CRU grain C/N ratios during late grain filling suggests preferential nitrogen remobilization towards developing kernels (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These source\u0026ndash;sink dynamics align with the \"carbon\u0026ndash;nitrogen allocation equilibrium theory\" proposed by Hu et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), whereby biomass variation fundamentally stems from altered C-N partitioning patterns. Supporting data (Table S5) further demonstrate that the CCU treatment markedly improved dry matter translocation efficiency from source to sink organs, consistent with our previous findings (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Amino acid metabolism and carbon metabolism pathways are activated under the CCU application\u003c/h2\u003e \u003cp\u003eA comprehensive analysis of the transcriptome and metabolome revealed that CRU regulate carbon and nitrogen metabolism in maize leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Nitrogen undergoes a series of transfer reactions through amino acids released by protein hydrolysis, particularly glutamic acid and aromatic amino acids (Peoples and Dalling, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The CCU application upregulated the expression of such as glutamate synthase (GLT1), and aspartate aminotransferase (GOT1), thereby enhancing the synthesis of glutamate and glutamine in leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These two amino acid levels were significantly higher under CCU treatment than under U treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), and were significantly negatively correlated with the C/N ratio (Fig. S7), indicating that nitrogen was effectively recycled and redistributed to processes such as protein synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig. S7). Concurrently, the accumulation of the aromatic amino acids phenylalanine and tyrosine was higher under CCU treatment than under urea treatment (C2/U and C3/U), coinciding with activation of downstream flavonoid biosynthesis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S8). This may suggest that CRU is not only prioritized for primary metabolism (e.g., protein synthesis) but also diverted to structural components and secondary metabolite production (Maeda et al., 2012; Yoo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hildebrandt et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, the CCU treatment significantly promoted alanine accumulation, and alanine levels were significantly positively correlated with yield (Fig. S7), indicating active nitrogen assimilation and coordinated carbon-nitrogen supply, with external nitrogen effectively integrated into amino acid biosynthesis pathways (Hildebrandt et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn carbon metabolism, CRU significantly enhanced the activity of glycolysis and the tricarboxylic acid cycle (TCA) pathway, by enhancing the activity of rate-limiting enzymes in the HK, PFKA, and PK (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), thereby sustaining carbon skeleton availability and energy homeostasis (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, the application of CCU significantly enhanced the activity of the glyoxylate and dicarboxylate pathway. Integrated analysis of amino acid profiles revealed that CCU promotes glutamate biosynthesis and accumulation by stimulating this pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). At the same time, CCU treatment reduced the levels of cis-aconitate, glyceric acid, and oxalic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, cis-aconitate exhibits significant positive correlations with glyceric acid and the C/N ratio (Fig. S7), indicating preferential channeling of carbon precursors into nitrogen assimilation processes, such as α-ketoglutarate utilization for glutamate biosynthesis (Foyer et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). This aligns with the reduced leaf C/N ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), further supporting a nitrogen-sufficient metabolic state in plants, which facilitates enhanced protein synthesis and growth. Furthermore, CCU activated the amino sugar and nucleotide sugar metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which not only provides a sufficient nitrogen supply but also enables its integration into structural biomass synthesis and glycosylation processes (Foyer et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Figueroa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Overall, CCU enhances the expression of key nitrogen assimilation enzymes (e.g., GLT1) and core enzymes of the TCA cycle (e.g., IDH3 and ACO), promoting the synthesis of glutamate and glutamine. This drives the accumulation of aromatic amino acids and alanine, effectively reducing the leaf C/N ratio and achieving efficient synergy in carbon\u0026ndash;nitrogen metabolism. The resulting regulatory network ultimately promotes the synchronous increase in dry matter accumulation and crop yield.\u003c/p\u003e \u003cp\u003eIn summary, this study identified \u003cem\u003eZm00001eb360480\u003c/em\u003e (GLT1) and \u003cem\u003eZm00001eb424940\u003c/em\u003e (IDH3) as candidate genes with high confidence for regulating carbon and nitrogen metabolism under CRU application. GLT1 functions in nitrogen assimilation, with its expression significantly negatively correlated with the C/N ratio and positively correlated with dry matter accumulation and yield; IDH3, as a key enzyme in the TCA cycle, likely provides carbon skeleton support for nitrogen assimilation by supplying α-ketoglutaric acid, and its expression is also significantly positively correlated with dry matter and yield. The two genes link nitrogen and carbon metabolism, respectively, and their expression profiles closely mirror key carbon\u0026ndash;nitrogen physiological traits, suggesting that they play an important role in carbon-nitrogen coordination and can be potential targets for molecular breeding or efficient nitrogen utilization.\u003c/p\u003e \u003cp\u003eThe plant types and experimental regions were limited in this study, and other plants and regions may have specific requirements for the optimal balance between controlled-release urea and urea. To better understand the dynamic changes in plant carbon and nitrogen metabolism under different conditions and optimize fertilizer management strategies, we plan to use multi-omics data to build systems biology-based models to simulate and predict the response of crops to the combined application of controlled-release urea and urea in future studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, CCU Optimized C and N allocation in maize by elevating leaf and grain C/N ratios and metabolite pools (soluble proteins, FAAs, non-structural carbohydrates), with accelerated dry matter transport to reproductive organs quantitatively explaining the yield gains (\u0026gt;\u0026thinsp;18.3% under C2). Key metabolic pathway analysis reveals CCU simultaneously regulates N assimilation (amino acid synthesis) and carbon metabolism (glyoxylate and dicarboxylate, and amino sugar and nucleotide pathways), achieving synchronized C-N supply through metabolic nodes like alanine accumulation. CCU optimizes nutrient supply and utilization while clarifying maize source-sink relationships via coordinated C-N metabolic mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Editage (www.editage.cn) for the English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by National Natural Science Foundation of China, grant number 32573155.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuan Li:\u003c/strong\u003e Conceptualization, Investigation, Methodology, Software, Writing \u0026ndash;original draft. \u003cstrong\u003eYiming Zhu:\u003c/strong\u003e Resources. \u003cstrong\u003eMenglin Bai:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYihan Zhang\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eHuiyang Zhu:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eWeixiao Hu:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYongchao Wang:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eWushuai Zhang:\u003c/strong\u003e Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eXinPing Chen:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eQinghua Yang:\u0026nbsp;\u003c/strong\u003eConceptualization, Investigation, Methodology, Writing \u0026ndash; review \u0026amp; editing. Supervision, Funding acquisition.\u003cstrong\u003e\u0026nbsp;Jiameng Guo:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the website of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov) in the NCBI Sequence Read Archive (SRA) repository under the BioProject accession number PRJNA1196689. The SRA accession numbers for individual samples are SRR31690063, SRR31690062, SRR31690061, SRR31690070, SRR31690068, SRR31690060, SRR31690059, SRR31690064, SRR31690066, SRR31690065, SRR31690069, and SRR31690067, and are also provided in Table S4. Data will be made available on request if additional information is needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. 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Nat Commun. 2022;13(1):6656. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-34537-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-34537-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carbon metabolism, Nitrogen metabolism, Comprehensive transcription/metabolism analysis, Controlled-release urea and urea combined application, Maize (Zea mays L.)","lastPublishedDoi":"10.21203/rs.3.rs-8930203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8930203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe precise regulation of nitrogen supply after flowering for maize can be achieved by blending urea and controlled-release urea (CCU) one-off application. However, the dynamic optimization driving source-sink allocation and mechanisms underlying synergistic carbon\u0026ndash;nitrogen regulation remain poorly understood. We investigated the physiological and molecular mechanisms underlying carbon and nitrogen metabolism in maize under various controlled-release urea (CRU) and conventional urea treatments. The experiment included five fertilization treatments: CK (no nitrogen) and four treatments at 180 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e: U (all Urea-N), C1 (CRU-N: Urea-N\u0026thinsp;=\u0026thinsp;1:2), C2 (CRU-N: Urea-N\u0026thinsp;=\u0026thinsp;2:1), and C3 (all CRU-N). Physiological traits were measured, and integrated leaf transcriptomic and metabolomic analyses were conducted. Compared with urea treatment, CCU (C2 treatment) boosted maize yield by up to 18.3\u0026ndash;22.8%, synergistically enhancing nitrogen components (N content and soluble protein), carbon metabolites (C content and soluble sugar), and total dry matter. Notably, total dry matter was positively correlated with C/N ratio. CCU optimized carbon\u0026ndash;nitrogen allocation by simultaneously increasing grain nitrogen reserves (soluble protein and free amino acids) and carbon storage (non-structural carbohydrates). Integrated transcriptomic and metabolomic analysis revealed CCU-mediated metabolic shifts, activating aromatic amino acid biosynthesis and glyoxylate and dicarboxylate metabolism. Multi-omics integration identified GLT1 and IDH3 as pivotal regulatory factors, whose expression levels showed significantly correlated with alanine, homocitric acid, and dry matter accumulation, thereby linking the crosslink between the TCA cycle and nitrogen assimilation to yield formation. These findings demonstrate that CCU drives the priority distribution of assimilation products to grains through the synergistic regulation of slow nitrogen release and carbon-nitrogen metabolism. This study provides insights into the regulation of maize metabolism and offers guidance for the efficient utilization of nitrogen under one-off application of nitrogen.\u003c/p\u003e","manuscriptTitle":"Controlled-release urea optimizes the pathway to yield increase via post-anthesis carbon-nitrogen coordination in maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 17:32:13","doi":"10.21203/rs.3.rs-8930203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T07:05:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"93914052608283274056000612454716063802","date":"2026-04-21T05:04:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T02:54:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T09:39:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34440355495941130738144838607206576168","date":"2026-04-20T09:04:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T17:26:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303382032016404473572876830142630921646","date":"2026-04-19T09:57:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100475161250571853018764325523229753265","date":"2026-04-19T08:26:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147032544060016913235653704820775850572","date":"2026-04-19T06:10:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79700698326152472759094816779540096150","date":"2026-04-19T04:41:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307987871767649458356081037111663988842","date":"2026-04-19T03:00:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T22:48:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T10:27:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-27T07:13:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T20:24:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-02-26T09:58:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e883f4a-d9e4-47ac-bd2c-6b7ca07c315c","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T07:11:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 17:32:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8930203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8930203","identity":"rs-8930203","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}