2.1 Experimental site

The experiment was conducted at the experimental field of Shenyang Agricultural University, located within the Northeast Region Crop Cultivation Science Observation of the Ministry of Agriculture and Rural Affairs (41°82′N, 123°56′E) from 2022 to 2023. The site has a semiarid climate with continental and monsoonal characteristics. During this period, the average monthly temperature was 19.92°C, with an average monthly precipitation of 44.11 mm (Supporting Information S1: Figure 1A). Brown soil obtained from the 0–20 cm layer of long-term field experiments was used. Each pot was filled with 20 kg of soil after air-drying and sieving through a 2 mm mesh. Before the 2022 experiment, the soil contained 14.62 g kg⁻¹ of organic matter, 180.04 mg kg⁻¹ of available nitrogen, 45.78 mg kg⁻¹ of available phosphorus and 202.36 mg kg⁻¹ of available potassium, with a pH of 6.5. Before the 2023 experiment, the soil contained 14.95 g kg⁻¹ of organic matter, 199.97 mg kg⁻¹ of available nitrogen, 59.07 mg kg⁻¹ of available phosphorus and 219.01 mg kg⁻¹ of available potassium, with a pH of 6.5.

2.2 Experimental design

The pot experiment employed a two-factor (A × B), completely randomized design with three repetitions. Factor A comprised two nitrogen fertilizer treatments: no nitrogen fertilizer treatment (0N) and application of ¹⁵N urea (S¹⁵N, 180 kg hm⁻², atom10.15%, Shanghai Research Institute of Chemical Industry Co., Ltd.). Factor B involved three root separation patterns: (1) intercropping of maize and peanuts separated by wooden borders (IMP-D), (2) intercropping of maize and peanuts separated by a 400-nylon mesh (IMP-H) and (3) intercropping of maize and peanuts (IMP, Supporting Information S1: Figure 1B,D). Twenty days after peanut seedlings were planted (25 June 2022 and 27 June 2023), the peanuts underwent stem injections of (¹⁵NH₄)₂SO₄ solution using the three root separation patterns. The injections, performed over a 9-day period, utilized a concentration of 88 mol L⁻¹ and a 20 μL microsyringe (Xiao et al., 2004). The maize variety was Liangyu 99 (Dandong Denghai Liangyu Seed Industry Co., Ltd.), and the peanut variety was Nonghua 9 (Peanut Research Institute of Shenyang Agricultural University, Shenyang, Liaoning). Seeding was conducted on 18 May 2022 and 19 May 2023. Three maize and peanut seeds were planted in each pot, retaining one plant per crop during the seedling stage. Additional fertilization (Supporting Information S1: Table 1) ensured a nutrient supply, whereas other cultivation practices adhered to local production standards. Ten days after the final injection (14 July 2022 and 16 July 2023), maize and peanut plant samples along with rhizosphere soil samples were collected for relevant indicator measurements.

2.3 Plant sample collection and chemical analysis

After a 10-day injection period in both 2022 and 2023, one maize and one peanut plant per pot were collected from five pots per treatment, totalling five plants in a single replicate, with three replicates in total. The harvested plants were divided into roots, stems, and leaves, blanched at 105°C for 30 min, and subsequently dried at 80°C until reaching a consistent weight. Dry matter accumulation was determined by weighing the dried plants, followed by sieving through a 0.5 mm mesh before digestion. Total plant nitrogen content was analyzed using the Kjeldahl method (Kjeltec 8400; FOSS). After sieving, plant samples weighing 7 mg were analyzed for ¹⁵N abundance using a stable isotope mass spectrometer (Iso ptime100; Elementar).

2.4 Rhizosphere soil sampling and chemical analysis

When collecting plant samples, maize and peanut roots treated with 0N and S¹⁵N were carefully uprooted, shaken to remove loose soil, and closely adhered soil at a depth of 5–15 cm was collected and sifted through a 0.9 mm mesh sieve to obtain clean rhizosphere soil (Supporting Information S1: Figures 1B,D). Composite rhizosphere soil samples were created by mixing soil samples from five pots per treatment (6 treatments × 3 replicates). The sieved rhizosphere soil samples were divided into three parts: one part was air-dried for soil nutrient content analysis, the second part was stored at −4°C for soil enzyme activity evaluation, and the remaining part was stored at −80°C for DNA extraction and metagenomic sequencing. In 2023, rhizosphere soil samples were analyzed for soil total nitrogen (TN) using the Kjeldahl method. Soil ammonium (NH₄⁺-N) and nitrate (NO₃⁻-N) contents were determined using an automated chemical analyzer (Smartchem200; AMS Technologies Ltd.). Soil protease (RRO), dehydrogenase (DHO) and nitrate reductase (NR) activities were determined using ELISA kits (MLBIO). Moreover, metagenomic sequencing was conducted on rhizosphere soil samples from maize and peanuts treated with S¹⁵N under different root interaction patterns (IMP and IMP-D) to examine the diversity of nitrogen cycling genes and functional microorganisms in the soil (Supporting Information S1: Figure 1D).

2.5 Metagenomic assembly and annotation

2.6 Statistical analyses

Plant dry matter, plant N accumulation, rhizosphere soil nutrient content and soil enzyme activities were analyzed using Duncan's multiple comparisons via one-way analysis of variance with SPSS 26.00 (IBM SPSS Inc.). Biological nitrogen fixation in peanuts and nitrogen transfer from peanuts to maize were evaluated using independent sample t tests. A significance level of p < 0.05 was applied. Figures were generated using Origin2023 software (Origin Lab Corporation).

The Shannon index was calculated using the ‘vegan’ package in R and compared between treatments using Student's t test. A circos plot illustrating the species compositional relationships among the samples was constructed using the ‘circlize’ package in R. Linear discriminant analysis (LDA) effect size (Lefse) analyses were performed using the ‘ggtree’ package in R with the LDA threshold specified as 4. Only divergent species with an LDA < 4 were considered biomarker species. A taxonomic cladogram was used to depict the hierarchical distribution of the marker species within each sample group.

The co-occurrence network analysis utilized the SparCC correlation matrix with the WGCNA package in R. The p values of the correlation networks were adjusted using Spearman's correlation analysis. The edges with r < 0.7 and p > 0.01 were eliminated. The resulting network was visualized using Gephi 0.9.4.

Functional units were compared between treatments using the Kruskal–Wallis test for significance analysis and false discovery rate control using the Benjamini-Hochberg method. Fold differences between treatments were obtained for each functional unit and expressed as Log10 (Fold_change_value). Functional units with p < 0.05 were considered to exhibit significant abundance differences.

The ‘linkET’ package for R was utilized to simultaneously depict the connections among the Shannon index, the relative abundance of functional genes related to the nitrogen cycle, and soil physicochemical properties through a combination of correlation heatmaps and network diagrams. This included Pearson correlation analysis of soil physicochemical properties and Mantel test correlation analysis of the Shannon index and the relative abundance of functional genes related to the nitrogen cycle.

The random forest model evaluated the significant predictors affecting biological nitrogen fixation and nitrogen transfer, including soil physicochemical properties, Top10 bacterial genera and key functional genes of the nitrogen cycle. These analyses were conducted using the Random Forest software package. Model significance and predictor importance were verified using the rfUtilities and rfPermute packages in R software, respectively.

Structural equation modelling (SEM) was conducted using the R ‘piecewiseSEM’ package to evaluate the direct and indirect relationships among biological nitrogen fixation, nitrogen transfer, key nitrogen cycling genes (nrfC and nirA), key bacterial genus (Bradyrhizobium) and soil physicochemical properties (NH₄⁺-N, NO₃⁻-N and DHO).

3.1 Comparison of dry matter and nitrogen accumulation in maize and peanut plants under different root separation patterns

Regardless of nitrogen fertilization or root separation patterns, both exerted a positive impact on the accumulation of dry matter and nitrogen in maize and peanuts. Increasing nitrogen fertilizer resulted in higher dry matter and nitrogen accumulation in the roots and aboveground parts (stems and leaves) of maize and peanuts, with significant differences observed between years (Supporting Information S1: Tables 2 and 3). Overall, regardless of nitrogen application, maize and peanuts exhibited higher dry matter and nitrogen accumulation in the roots and aboveground parts in the order of IM > HIM > DIM and IP > HIP > DIP. Dry matter in roots and aboveground parts significantly increased under S¹⁵N treatment compared to DIM and DIP, with increases of 18.82% and 64.34% for IM and 55.00% and 70.61% for IP, respectively. Nitrogen accumulation in roots and aboveground parts significantly increased under S¹⁵N treatment compared to DIM and DIP, with increases of 81.97% and 150% for IM and 59.68% and 71.36% for IP, respectively. These findings demonstrated the critical role of nitrogen levels and root interactions in nutrient competition and facilitation within intercropping systems.

3.2 Nitrogen uptake in maize and peanut intercropping: Understanding nitrogen fixation and transfer

The ¹⁵N stem injection labelling experiments revealed that ¹⁵N absorption increased with heightened root interactions in maize and peanut plants. The absorption order was IP > HIP > DIP and IM > HIM > DIM (Figure 1a). Similarly, biological nitrogen fixation in peanut increased with the intensity of root interactions, measured at 33.75, 19.23 and 5.64 mg/pot for IP, HIP and DIP, respectively. These values were 6.0 and 3.4 times higher for IP and HIP compared with DIP, respectively (averaged of 2 years, Figure 1b). In the IMP-D, IMP-H and IMP treatments, nitrogen transferred from peanut plants to maize accounted for 0.00%, 10.33% and 17.39%, respectively, with amounts of 0.00, 143.41 and 331.14 mg/pot, respectively (averaged of 2 years, Figure 1b). Therefore, as the root interaction intensity increased, nitrogen transfer from peanuts to maize increased, indicating that root contact primarily facilitated nitrogen transfer.

The ¹⁵N-labelled soil experiments revealed that, in maize plants, the source of fertilizers and soil nitrogen content ranked as IM > HIM > DIM (Figure 1c–e). The nitrogen derived from peanut nitrogen fixation transfer in the IM was three times higher than that in the HIM (Figure 1c,d). Notably, the amount of nitrogen in the DIM derived from peanut nitrogen fixation transfer was 0 mg (Figure 1e). Similarly, in peanut plants, the source of fertilizers and soil nitrogen content followed the order of IP > HIP > DIP (Figure 1c–e). Peanut's biological nitrogen fixation increased with stronger root interactions, with increments of 17 mg and 41 mg/pot for HIP and IP, respectively, compared to DIP (averaged of 2 years, Figure 1c–e).

3.3 Comparison of nutrient contents and enzyme activities in maize and peanut rhizosphere soil under different root separation patterns

Nitrogen fertilizers and root separation patterns affected TN, NH₄⁺-N, and NO₃⁻-N contents, as well as RRO, DHO and NR activities in maize and peanut rhizosphere soils. The interaction between nitrogen fertilizer and root separation methods significantly affected TN content and activities of RRO, DHO and NR in the rhizosphere soil (Figure 2). Generally, nitrogen content followed IM > HIM > DIM and IP < HIP < DIP (Figure 2a–c), whereas soil enzyme activities followed IM > HIM > DIM and IP > HIP > DIP (Figure 2d–f). Under the S¹⁵N treatment, compared with DIM, TN, NH₄⁺-N and NO₃⁻-N contents in IM significantly increased by 14.37%, 26.54% and 61.76%, respectively, and HIM increased by 6.32%, 1.96% and 57.35%, respectively (Figure 2a–c). Conversely, compared to DIP, TN, NH₄⁺-N and NO₃⁻-N contents in IP decreased by 26.83%, 27.48% and 30.00%, respectively, and HIP decreased by 9.97%, 4.21% and 9.75%, respectively (Figure 2a–c).

In the S¹⁵N treatment, compared with DIM, RRO, DHO and NR activities in the IM rhizosphere soil increased significantly by 9.44%, 12.64% and 14.14%, respectively. In the HIM, these activities increased by 6.57%, 11.40% and 4.74%, respectively, compared with DIM (Figure 2d–f). Compared with DIP, RRO, DHO and NR activities in the IP significantly increased by 16.39%, 2.21% and 13.22%, respectively. In the HIP, RRO and NR activities increased by 14.50% and 3.09%, respectively. These results indicated a significant regulatory effect of root interactions on the physicochemical properties of the rhizosphere soil.

3.4 Comparison of microbial community relative abundance and diversity in maize and peanut rhizosphere soil under different root separation patterns

Alpha diversity analyses demonstrated higher microbial diversity in the IM than in the DIM, whereas microbial diversity in the IP was lower than that in the DIP (Figure 3a). In the maize rhizosphere soil, the dominant genera were Sphingomonas (20%), Nocardioide (14%), Bradyrhizobium (10%) and Candidatus_Sulfopaludibacter (10%, Figure 3b). Compared with DIM, IM exhibited an increased relative abundance of Sphingomonas, Nocardioide, Bradyrhizobium, Phenylobacterium and Occallatibacter. In the peanut rhizosphere soil, the dominant genera were Sphingomonas (32%), followed by Nocardioide (13%) and Bradyrhizobium (10%, Figure 3c). Compared with DIP, IP showed an increased relative abundance of Bradyrhizobium, Candidatus_Sulfopaludibacter, Phenylobacterium, Occallatibacter, Candidatus_Angelobacter and Humisphaera. The Lefse analysis highlighted significant differences in bacteria between DIM and IM, particularly in Bradyrhizobium (Figure 3d), and between DIP and IP, particularly in Sphingomonas, Candidatus_Sulfopaludibacte and Gemmatirosa (Figure 3e). These findings suggested that root interactions affected the growth and relative abundance of specific bacterial genera. Bradyrhizobium and Candidatus_Sulfopaludibacte were positively correlated with soil RRO, DHO and NR, whereas Gemmatirosa and Sphingomonas exhibited positive correlations with soil NR and significant negative correlations with soil RRO and DHO. Overall, NO₃⁻-N, DHO and NR accounted for 68.59%, 68.4% and 68.2% of the bacterial community, respectively (Supporting Information S1: Figure 2).

3.5 Nitrogen cycling genes identified and screened

Through sequencing, we identified 43 genes involved in nitrogen cycling, encompassing pathways such as anammox, assimilatory nitrate reduction, denitrification, dissimilatory nitrate reduction, nitrification and nitrogen fixation (Supporting Information S1: Table 4). The co-occurrence network analysis revealed greater complexity in the DIM VS IM network than in the DIP VS IP network. DIM VS IM comprised eight modules featuring genes such as nirK, nasB, narB, narG and nifH, which interacted with multiple functional genes (Figure 4a), whereas DIP VS IP contained six modules with genes such as narH, narZ, amoA_B, hzsC and amoC_B interacting similarly (Figure 4b). Differential functional genes implicated in dissimilatory nitrate reduction (nrfC), assimilatory nitrate reduction (nirA) and nitrification (amoB_A) were detected in DIM VS IM (Figure 4c), whereas ammonia oxidation (hzsB and hzsC), nitrification (nxrB), nitrogen fixation (nifH), assimilatory nitrate reduction (narB) and denitrification/dissimilatory nitrate reduction (napA) were identified in DIP VS IP (Figure 4d). These nine differentially expressed genes were designated as key genes and their relative abundances across various samples were investigated. Notably, variations in the relative abundances of genes associated with the same nitrogen cycling pathways were observed among the samples (Figure 4e), indicating a significant impact of root interactions on the complexity and stability of nitrogen cycling gene networks. Soil physicochemical factors such as TN, PRO and NH₄⁺-N were identified to explain variations of 29.81%, 22.15% and 18.59% of functional genes, respectively. Genes related to pathways such as nitrification, denitrification, nitrate reduction and nitrogen fixation exhibited significant positive correlations with NH₄⁺-N and NO₃⁻-N and negative correlations with TN, PRO, DHO and NR (Supporting Information S1: Figure 3).

3.6 Nitrogen fixation and transfer depend on nitrogen cycling genes and microbial diversity

Root interactions can induce nitrogen fixation and transfer, which are closely correlated with soil physicochemical properties, functional microbes and nitrogen cycling genes. The Mantel tests revealed a significant positive correlation between microbial diversity (Shannon index) and soil NH₄⁺-N and DHO (Figure 5a), indicating the notable impact of soil environmental factors on microbial communities. The random forest model identified DHO, Bradyrhizobium, nrfC and nirA as key factors in predicting biological nitrogen fixation and transfer (Supporting Information S1: Figure 4). The SEM analysis demonstrated that the primary factors affecting Bradyrhizobium in maize–peanut intercropping were NH₄⁺-N and NO₃⁻-N (Figure 5b). Among the assessed factors, DHO exerted a significantly positive direct impact on nirA, and a significantly negative direct impact on nrfC. Bradyrhizobium exhibited significant indirect positive effects on nitrogen transfer through DHO, with the greatest negative indirect effect on nitrogen fixation. Bradyrhizobium also had an indirect positive effect on nitrogen fixation through nirA and an indirect negative effect on nitrogen fixation and transfer through nrfC (Figure 5b).

4.1 Root interactions enhanced peanut biological nitrogen fixation and nitrogen transfer

This study revealed that intensified root interactions in intercropping systems enhanced dry matter and nitrogen accumulation in maize and peanut plants (Supporting Information S1: Tables 2 and 3). This indicated that root interactions were conducive to dry matter and nitrogen accumulation. This improvement suggests that intercropping promotes nitrogen exchange and symbiotic nitrogen fixation, thereby improving nitrogen utilization efficiency (Raza et al., 2023; Salgado et al., 2021; Shao et al., 2021). For instance, a study on chickpea-flax intercropping demonstrated that flax obtained 8%–22% of its fixed nitrogen from chickpeas (Reid et al., 2023). Similarly, in a maize-alfalfa intercropping study, alfalfa contributed 28% of the biological nitrogen fixed in maize without nitrogen fertilization but only 7% with 112 or 224 kg ha⁻¹ nitrogen fertilizer (Osterholz et al., 2023). The ¹⁵N stem injection labelling experiment revealed that increased root interactions led to increased ¹⁵N uptake and nitrogen fixation in maize and peanut plants, validating the nitrogen transfer from peanuts to maize (Figure 1a,b). Previous studies have confirmed the efficacy of ¹⁵N steam injection in labelling plant stalks (Yasmin et al., 2006). Moreover, Götz and Herzog (2006) observed that during the pod filling stage, root ¹⁵N concentration in cowpeas was only 2%, whereas that in stems, leaves, and pods was 24%, 11% and 62%, respectively. Furthermore, Xiao et al. (2004) confirmed nitrogen transfer from fababean to wheat through ¹⁵N petiole injection labelling in wheat-faba bean intercropping, noting a minimal possibility of nitrogen secretion from the roots into the transfer pathway, leading to potential underestimation. Further, Wang et al. (2020) quantified rhizosphere N deposition as 25, 51, 20 and 63 kg N ha⁻¹ for four legume crops (peanut, soybean, mungbean and adzuki bean) using the cotton-wick method. They found that wheat utilized 13%–85% of the rhizosphere N deposition from legumes and contributed to 4%–20% of the total nitrogen uptake of wheat. However, in this study, although the ¹⁵N stem injection labelling method provided valuable insights, there remains a risk of underestimation of the estimated values due to nitrogen absorption primarily by plant roots before transfer to stems. Therefore, the actual contribution of roots to nitrogen absorption and transfer may be higher than the current estimates, warranting further research to elucidate their precise role in these processes.

The ¹⁵N labelling soil experiment was conducted to identify nitrogen changes in the soil, fertilizer, and atmosphere, thereby deeply exploring the effects of root interactions on nitrogen cycling in maize–peanut intercropping. The findings revealed that across various root separation patterns, the nitrogen content from fertilizer and soil in maize followed the trend IM > HIM > DIM (Figure 1c–e, aim 1). Notably, the nitrogen content in maize from peanuts was three times higher in the IM than in the HIM. Similarly, the nitrogen content in peanuts from the soil, fertilizer, and atmosphere followed the sequence IP > HIP > DIP (Figure 1c–e, aim 1). Moreover, the biological nitrogen fixation increased by 17 mg and 41 mg/pot in HIP and IP, respectively, compared with DIP. This indicates that nitrogen transfer was promoted to a higher level when the root systems were fully interactive. This phenomenon may be attributed to the enhanced nitrogen transfer facilitated by the types and quantities of root exudates coupled with the increased diversity and abundance of rhizosphere microorganisms. For example, organic acids can facilitate nitrogen transfer from alfalfa to maize, with 15.4%–21.5% of alfalfa-fixed nitrogen being translocated to maize (Wang et al., 2019; Zhang et al., 2019). Hu et al. (2021) observed that maize root exudates induced enrichment of faba bean rhizosphere nitrogen-fixing bacteria (Agromyces, Arthrobacter, Bacillus, Lysobacter and Paenibacillus), promoting nitrogen fixation in faba bean. A recent study elucidated that plant root contact in maize–peanut intercropping systems modulates the rhizosphere microbiome which enriched Pseudomonas with its siderophore (pyoverdine) to improve iron nutrition in intercropped peanuts (Wang et al., 2024). These findings highlight that root interactions can indirectly influence nitrogen fixation and transfer in intercropping systems by optimizing the composition and structure of the rhizosphere microbial communities. Overall, root interactions may alter soil microorganism composition and metabolism, enhance soil nitrogen flow, and facilitate plant nitrogen uptake (Lu et al., 2024; Zou et al., 2023). In addition, agronomic management and crop varieties affect soil nutrient status (Gao et al., 2019; Jiang et al., 2022; Wang et al., 2022). The ‘apparent transfer’ phenomenon may pose a risk of overestimating nitrogen transfer when utilizing ¹⁵N-labelled urea in the soil, complicating the distinction between non-labelled nitrogen transfer and nitrogen fixation from peanuts (Cheng et al., 2015; Davidson et al., 2018; Xiao et al., 2004; Xie et al., 2015). Considering these findings, future studies should focus on optimizing isotope-labelling techniques to precisely quantify nitrogen transfer. These findings are important for unravelling nitrogen transfer mechanisms in soil-plant systems and improving agricultural sustainability.

4.2 Root interactions enhanced nutrient contents and enzyme activities in maize and peanut rhizosphere soils

In our study, we observed a significant increase in the contents of TN, NH₄⁺-N, and NO₃⁻-N in maize rhizosphere soil as the intensity of root interactions increased, whereas these parameters notably decreased in the peanut rhizosphere soil (Figure 2a–c). Additionally, the activities of RRO, DHO and NR in the maize and peanut rhizosphere soils increased (Figure 2d–f). Notably, the nitrogen levels in strip intercropping patterns, such as maize/wheat, maize/fava bean and wheat/fava bean, were higher than those in sole cropping practices (Cong et al., 2014). Correspondingly, studies on maize–soybean strip intercropping (Shen et al., 2023) and sugarcane–soybean intercropping have confirmed this trend, presenting the role of intercropping in enhancing soil fertility by optimizing nutrient utilization through mutual complementation and competition (Zhang, Tang, et al., 2023). The intercropping system of proso millet/mung beans enhanced the activities of conversion enzymes, urease, and catalase in the proso millet rhizosphere soil (Gong et al., 2019). Similarly, maize/soybean intercropping contributed to increased activities of urease, conversion enzymes, acid phosphatase, and catalase in maize and soybean rhizosphere soils (Lu et al., 2023). These results corroborate the benefits of root interactions in improving rhizosphere soil nutrient and enzyme activities. However, previous studies have indicated a shift in factors affecting rhizosphere soil physicochemical properties from crop varieties (Yu et al., 2018) to root exudates (Sun et al., 2019), rhizosphere soil microbial community composition (Lin et al., 2022), and expression levels of functional genes in biogeochemical cycles (Duan et al., 2023; Li et al., 2023). Overall, this underscores the importance of further investigation of the impact of diverse root interaction patterns on the expression of crucial functional genes in nitrogen cycling and the role of soil microbes in nitrogen transformation processes, contributing to a comprehensive understanding of nitrogen cycling. In conclusion, these findings lay the groundwork for exploring the potential genes involved in nitrogen cycling using molecular biology and bioinformatics tools.

4.3 Root interactions promote nitrogen fixation and transfer by recruiting nitrogen-cycling functional microorganisms

This study highlights the significant impact of root interactions on bacterial communities, particularly on the relative abundance and composition of bacterial genera. First, alpha diversity analysis revealed increased diversity in the IM rhizosphere soil and decreased diversity in the IP rhizosphere soil (Figure 3a), suggesting that root interactions and resource sharing enhanced soil microbial diversity. However, reduced diversity in intercropped peanut rhizosphere soils may result from interspecific competition (Duchene et al., 2017). For example, Zhang et al. (2024) suggested the importance of intercropping for enhancing crop productivity, stress mitigation, soil-borne disease control and erosion reduction. Furthermore, interspecies interactions in maize–soybean intercropping systems can alter the abundance of saprotrophic fungi and improve network stability and complexity, thereby improving nutrient cycling in maize (Zhang, Yang, et al., 2023). Research on various crop rotation patterns has demonstrated that root interactions can affect the diversity of soil bacteria in the rhizosphere, potentially influencing the soil ecosystem function (Koskey et al., 2023; Liu, Schroeder, et al., 2023; Liu et al., 2024). Furthermore, changes in abundance and composition revealed an increase in Sphingomonas and Bradyrhizobium in the IM and peanut rhizosphere soils (Figure 3b,c), and Lefse analysis results confirmed significant variations in the distribution of specific bacterial genera under root-interaction conditions (Figure 3d,e). Hence, plant interactions can influence the growth of specific bacterial genera in the soil, thereby altering community composition and ultimately affecting plant growth, development, and nitrogen cycling. Previous studies have highlighted the crucial roles of Bradyrhizobium (Correa et al., 2023; Msaddak et al., 2023) and Sphingomonas (Mazoyon et al., 2023; Su et al., 2024) in nitrogen cycling, providing plants with readily available nitrogen. Bradyrhizobium secretes indole acetic acid, which stimulates root development, enhances nutrient uptake, and promotes plant growth. Sphingomonas, a potential soil nitrifying and denitrifying bacterium, provides plants with accessible nitrogen. This finding highlights the pivotal role of root interactions in promoting microbial diversity and optimizing microbial community composition. This process recruits the enrichment of key microorganisms, such as Bradyrhizobium and Sphingomonas, involved in the nitrogen cycle, thereby promoting nitrogen fixation and transfer. This study laid the foundation for investigating the molecular regulatory networks of specific bacterial genera that interact with the root system and elucidating key signalling molecules and pathways affecting the nitrogen cycle.

4.4 Root interactions induce functional gene expression for nitrogen cycling in peanut rhizosphere soil

This study identified 43 nitrogen cycling functional genes in maize and peanut rhizosphere soils that participate in six nitrogen metabolic pathways (Supporting Information S1: Table 4). Efficient annotation of metagenomic functions demonstrated the importance of these genes in critical processes, such as ammonia and nitrate reduction and nitrogen fixation (Culligan & Sleator, 2016; Kim et al., 2024; Navgire et al., 2022). The co-occurrence networks revealed variations in functional gene interactions in maize and peanut rhizosphere soils, indicating different effects of root interactions on nitrogen cycling regulation (Figure 4a,b). The presence and expression changes in these differentially functional genes among different crops reflect the differential response mechanisms of crops to the nitrogen cycle. In maize rhizosphere soil, nrfC and nirA genes were identified as key players in nitrate reduction, facilitating the conversion of nitrate to ammonia, thereby supplying essential nitrogen resources to plants (Figure 4c, Yu et al., 2021). Additionally, amoB_A played a crucial role in the ammonia oxidation process by converting ammonia to nitrite and contributing to nitrification, thereby facilitating nitrogen absorption and utilization by plants (Figure 4c, Bossolani et al., 2023). The narB and nxrB genes facilitated nitrate reduction and nitrification, thereby enhancing nitrogen utilization and transformation in the soil (Figure 4d, Grassmann et al., 2022). The nifH gene helped convert atmospheric nitrogen into ammonia nitrogen, thereby increasing the soil nitrogen content (Zhang et al., 2006; Zhao et al., 2017). However, the abundance of nifH in the intercropped peanut rhizosphere soil reduced (Figure 4d). The following factors may contribute to this. (1) Competition from maize for soil resources, such as nutrients and water can lead to decreased expression of the nitrogen fixation nifH gene in peanuts (Benchuan et al., 2022; Yang et al., 2022; Yu et al., 2022; Zhang et al., 2022). (2) Rhizosphere effects, where root interactions result in differences in the composition of the rhizosphere microbiota, can influence the expression of nitrogen cycling functional genes and their regulatory mechanisms (Dijkstra et al., 2010). Nevertheless, a greater number of genes associated with ammonia, nitrification, nitrogen fixation, and nitrate reduction were identified in the peanut rhizosphere soil (Figure 4d), indicating a more complex nitrogen cycling and transformation process. Previous studies have demonstrated that the composition and concentration of plant root exudates influence the growth and metabolic activity of different microbial communities, thereby affecting nitrogen cycling genes in the rhizosphere soil (Ulbrich et al., 2022; Yue et al., 2023; Zhou et al., 2023). Based on this, we hypothesized that root interactions recruit the enrichment of nitrogen-cycling microorganisms. These microorganisms activate the expression of functional nitrogen-cycling genes in the peanut rhizosphere soil through various pathways, including ammonia oxidation, nitrification, nitrogen fixation, and nitrate reduction. This process enhances biological nitrogen fixation in peanuts and facilitates nitrogen transfer from maize (aim 2). These functional genes provide novel avenues for investigating nitrogen fixation and transfer between crops at the molecular level. However, owing to the limited sample size and experimental constraints, further research can be necessary across different crop species, seasons, and soil types to comprehensively understand these processes.

4.5 Nitrogen cycling functional microorganisms and functional genes co-drive mechanisms of nitrogen fixation and transfer

Biological nitrogen fixation and transfer are jointly influenced by various factors including soil physicochemical properties, microbial communities, and functional genes. In particular, a close relationship exists between the soil physicochemical properties, microbial community composition, and functional genes. Mantel tests revealed a significant positive correlation between microbial diversity and soil NH₄⁺-N and DHO (Figure 5a), indicating the role of soil environmental factors in shaping microbial community diversity. Specifically, the NH₄⁺-N content and DHO in the soil may create more favourable habitat conditions for certain microorganisms, thereby influencing microbial diversity and composition (Cuartero et al., 2022; Duan et al., 2023; Zhang, Zhu, et al., 2023). For instance, soil pH, NH₄⁺-N, available phosphorus, and total potassium significantly affect the composition and structure of microbial communities in maize-soybean strip intercropping systems (Yu et al., 2018). Soil properties, such as TN, organic matter and pH, greatly influenced nitrogen cycling genes, explaining up to 86.5% of the variability (Zhang & Lv, 2021). The random forest model predictions identified factors such as the bacterial genus Bradyrhizobium and the key functional genes of the nitrogen cycle, nrfC and nirA, as potentially significant influencers of biological nitrogen fixation and transfer in peanuts (Figure S2B–S2D). This underscores the close correlation between functional microorganisms and key nitrogen cycle genes that are jointly involved in soil nitrogen fixation and transfer processes. SEM modelling further explored the potential mechanisms of these microorganisms and genes in soil nitrogen cycling (Figure 5b). The results showed that soil NH₄⁺-N and NO₃⁻-N contents significantly affected the relative abundance of Bradyrhizobium (Figure 5b), suggesting that soil chemical properties regulate soil microbial community composition, thereby influencing nitrogen fixation and transfer processes. We speculate that nrfC and nirA play crucial roles in the regulation of nitrogen fixation and transfer. Moreover, Bradyrhizobium's role in this process, influenced by soil nitrogen content and DHO, indirectly affected nitrogen fixation and transfer efficiency (Figure 5b, aim 3). These findings highlight the intricate interplay between nitrogen-cycling microorganisms and nitrogen-cycling functional genes in regulating nitrogen fixation and transfer, suggesting the need for further investigation of the molecular mechanisms of these genes in nitrogen-cycling regulation.