Emerging Contaminants 12 (2026) 100594 Contents lists available at ScienceDirect Emerging Contaminants journal homepage: www.keaipublishing.com/en/journals/ emerging-contaminants Low nitrogen in biogas slurry application mitigates antibiotic resistance genes in soil Xiang Zhao a, Jian Wang b, c, Yufei Li a, Qianqian Lang a, Jijin Li a, Bensheng Liu a, Guoyuan Zou a, Junxiang Xu a, *, Qinping Sun a, ** a b c Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China National Genomics Data Center, China National Center for Bioinformation, Beijing, China Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China a r t i c l e i n f o a b s t r a c t Article history: Received 26 September 2025 Received in revised form 27 October 2025 Accepted 5 November 2025 Available online 5 November 2025 Agricultural soils treated with biogas slurry (BS) have been extensively recognized as hotspots for the development of antibiotic resistance genes (ARGs). The application of BS has been demonstrated to significantly elevate the levels of ARGs in soil. However, there remains a limited understanding of how different nitrogen contents within BS affect ARG profiles. The objectives of this study were to explore the changes in ARGs in soil, as well as the potential mechanisms between BS application dosages and ARG patterns through Illumina sequencing and high-throughput quantitative PCR under five amounts of BS application according to the nitrogen contents. Results indicated that no significant alterations were noticed in the abundance of ARGs under low BS applications (0–120 kg N ha− 1) comparing to S0, and bacterial networks with different network hubs indicated that significant relationships occurred at high BS treatment (180 kg N ha− 1), as well as the highest abundance of ARGs and bacterial abundance observed. However, when the BS application at 240 kg N ha− 1 which the soil under saturated conditions, the abundance of ARGs decreased in response to a decrease in bacterial number comparing to 180 kg N ha− 1. Structural equation models indicated that the content of NH+ 4 -N in soil was the direct driving factor influencing ARG characterizations in BS-amended soil. In summary, low nitrogen contents within BS (under 180 kg N ha− 1) reduced the increase of ARGs in soil, high nitrogen contents (180–240 kg N ha− 1) could directly elevate the abundance of ARGs through the introduction of amended nitrogen, disinfection effect of BS played a key role in the decrease of ARGs under anaerobic environ­ ments (above 240 kg N ha− 1). These findings enhanced our understanding of BS application with different nitrogen contents on ARGs in soil, with significant environmental implications for the precise application of BS and high value utilization. © 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Keywords: Biogas slurry Antibiotic resistance genes Nitrogen contents Disinfection effects 1. Introduction Antibiotic resistance genes (ARGs) are recognized by the United Nations Environment Programme as one of the six emerging pol­ lutants of concern [1,2], which have attracted global attention due to concerns regarding antibiotic resistant residues and selective pressure on native antibiotic resistant bacteria (ARB) [3–6]. A global monitoring report launched by the World Health * Corresponding author. ** Corresponding author. E-mail addresses: xujunxiang@baafs.net.cn (J. Xu), sunqinping@baafs.net.cn (Q. Sun). Peer review under the responsibility of KeAi Communications Co., Ltd. Organization concluded that there has been a concerning rise in resistance to common bacteria globally [7,8]. The misuse and abuse of antibiotics in the farming industry has led to higher residues of antibiotics in organic fertilizers, resulting in organic fertilizers becoming a major contributor and reservoir for ARGs [9–12]. Organic fertilizers application has increased the risk of resistant pathogens being conveyed to individuals via food supply network [13,14]. However, the land application of organic fertilizer is a common waste treatment technology used to enhance soil fertility and improve food production under the green develop­ ment policy of circular economy [15–17]. ARGs and ARB contained in organic fertilizer can cause strong selective pressure and in­ crease the abundance of ARGs through horizontal gene transfer (HGT) among different microorganism species with the increased https://doi.org/10.1016/j.emcon.2025.100594 2405-6650/© 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). amplification X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 abundance of mobile genetic elements (MGEs) by organic fertilizer application [18,19]. Consequently, within the One Health approach [6], it is critical to study resistance transmission caused by organic fertilizer application. Biogas slurry (BS), commonly referred to as digestate, is pro­ duced during the anaerobic digestion of animal manure. This mi­ crobial process decomposes organic material and significantly reduces harmful agents such as insect eggs and pathogens [15,21]. Additionally, ammonium, phosphate and other nutrients are released in this process, making BS a superior fertilizer compared with chemical fertilizers. Furthermore, it is reported that BS has a disinfection effect and its application can suppress a number of soilborne diseases, such as root-knot nematode and Fusarium wilt. The mechanisms of the disinfection effect involves toxic pollut­ ants, such as phenolic compounds, volatile fatty acids, and poly­ cyclic aromatic hydrocarbons, generated during anaerobic digestion exerting negative effects on microbial enzyme activity in soil, thereby suppressing plant pathogens [22]. Data show that BS has been widely used in agricultural soils for over 10 years in some countries [21,23]. Meanwhile, microbial communities in BS play a key role in plant disease suppression. Li et al. [24] found that pig manure BS has suppression on cucumber Fusarium wilt, and functional microbial taxa for disease resistance were observed in BS. In addition to suppressing soilborne diseases, the application of BS affects the abundance of ARGs, primarily due to increased soil nutrient levels—especially nitrogen—that promote microbial proliferation [20,25,26]. Multiple studies have reported varying degrees of ARG enrichment in soils following BS application [15,21,23,27–29]. Lu et al. [23] investigated the effects of long-term (8–10 years) BS application at the amount of 550–800 m3 per hectare on the prevalence of ARGs in agricultural soils. The results indicated that the increase of MGEs (Tn916/1545) became poten­ tial contributors for the increase of soil antibiotic resistance via promoting the enrichment of ARG-associated bacteria. Liu et al. [21] examined the temporal variations in ARGs and bacterial community composition in soils sprayed with BS repeatedly at an amount of 200 m3 per hectare once every 2 months. The results showed that repeated application increased the abundance of ARGs and fostered gene transfer between potential hosts. After 5 years of BS application, the relative abundance of ARGs (ereA, ereF, mefA, sul1, sul2, tetG, and tetO) in soil remained approximately 1.5fold higher than in control soil [21]. Similar results were obtained in a study of large-scale biogas facilities, which reported the in­ crease of the number of ARB and the relative abundance of ARGs, to 270 and 52 times in biogas residue [15]. These studies suggest that the high residual levels of ARGs in BS, along with the intro­ duction of excess nutrients, are the primary drivers of ARG dissemination in soil. Therefore, it is essential to investigate how ARGs in soil are affected by varying BS application rates, with the aim of minimizing the input of both ARGs and nutrients. Therefore, it is crucial to limit the application levels of BS to minimize the threaten of ARGs and maximize the disinfection effect of BS. We hypothesized that reducing nitrogen input by controlling the dosage of BS could effectively mitigate the increase of ARGs in soil. To test this hypothesis, we applied five different BS concentrations based on their nitrogen content and analyzed changes in soil bacterial communities and ARG profiles using Illumina sequencing and high-throughput quantitative PCR (HT qPCR). Our objectives were to: (1) assess the effects of BS appli­ cation with varying nitrogen levels on soil ARGs and bacterial communities, and (2) identify the key factors driving the distri­ bution patterns of ARGs under BS treatment. This study provides new insights into the potential of BS management as a strategy for mitigating the spread of antibiotic resistance in agricultural soils. 2. Material and methods 2.1. Experimental design and sampling This research was conducted in a field experimental site located in Fangshan district, Beijing, China (116.23E, 39.76N). This field has been planted with maize for silage and long-term fertilized with BS annually since 2020. No chemical fertilizer was used. The BS used in this study was collected from the outlet of cattle farm wastewater treated by oxidation ponds for three months. The BS contained 0.66 g/kg total nitrogen (TN), 0.28 g/kg total phosphorus (TP) and 7.76 g/kg organic matter (OM). According to the nitrogen contents, five BS application treatments were conducted including control without application of BS (S0), 60 kg N ha− 1 (S60), 120 kg N ha− 1 (S120), 180 kg N ha− 1 (S180) and 240 kg N ha− 1 (S240) as BS, and no chemical fertilizers were used during the study period. Totally, 15 plots were set in triplicates randomly and every plot occupied 45 m2 (5m × 9m). Based on the nitrogen contents of BS and nitrogen requirement, the amount of BS required for each plot was calculated. The total BS application amount was 0.66 m3, 1.32 m3, 1.99 m3, and 2.65 m3 for S60, S120, S180, and S240 treatment, respectively. Through the known flux and speed of the watering pump, the irrigation time for each plot was calculated, which was the application basis for BS. The BS was applied twice through surface irrigation. The first application (1/3 of total BS) was prior to the maize being planted as base fertilizer, and the second application (remaining 2/3 of total BS) was applied in the form of a follow-up fertilizer one month later. After BS application, watering was performed for dilution purpose (to a uniform volume). Soil sampling was collected after the maize harvest. In each replicate, five samples were collected and then merged to one mixed sample, and each site was sampled at three depths: 0–30 cm, 30–60 cm and 60–90 cm. In totally, 45 samples, one background soil sample and one BS were collected. Samples for physical and chemical characterizations were kept at 4 ◦ C while the samples for DNA extraction were frozen at − 80 ◦ C. 2.2. Physical and chemical characterizations The physical and chemical properties of soil samples, including pH, electrical conductivity (EC), organic matter (OM), total carbon − (TC), total nitrogen (TN), NH+ 4 -N, nitrate (NO3 -N) and available phosphorus (AP), were measured. Soil pH was measured at a ratio of 1:2.5 (w/w) by a pH meter (PHS-3C-01, China). EC was analyzed at a ratio of 1:5 (w/w) by a conductivity meter (DDS-307, China). OM was measured by the K2Cr2O7 oxidation-reduction colori­ metric protocol. An elemental analyzer was used to analyze TC and TN (Vario MAX cube, Elementar, Germany). Soil NO−3 -N and NH+ 4 -N were extracted with 1 M KCl and determined by a continuous flow analyzer (AA3, SEAL analytical, Germany). Soil AP was determined by the molybdenum blue colorimetric protocol. 2.3. HT qPCR of ARGs and MGEs Total DNA was extracted from a 0.25 g soil sample using the DNeasy PowerSoil Kit (QIANGEN GmbH, Germany). The concen­ trations and purity of the extracted DNA was evaluated with a NanoDrop 2000 spectrophotometer. HT qPCR included a total of 296 primer sets designed to target 285 distinct ARGs, 10 MGEs including 8 transposases genes and two class 1 integron genes, and one 16S rRNA gene [22,30]. The Wafergen SmartChip Real-time PCR system (Fremont, CA, USA) was used to perform HT qPCR in 100 nL reaction volume prepared with TB Green Premix Ex TaqII (TaKaRa Bio Inc., Japan). The settings of the PCR 2 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 thermal cycling were set: an initial denaturation at 95 ◦ C for 10 min, followed by 40 cycles at 95 ◦ C for 30 s and extension 30 s at 60 ◦ C. A negative control was conducted utilizing RNA-free water. Effective amplification has to requirements: threshold cycle value (CT) lower than 31, and the amplification efficiency maintained between 1.7 and 2.3 [31]. The abundance of ARGs and MGEs in each sample was determined using the following method: treatments were calculated using Cytoscape (v3.7.1) with CoNet plugged in. Gephi (v0.9.2) was used for network visualization and topological parameter calculation. The network nodes that exhibited high closeness centrality and degree were designated as − network hubs. The data of the soil properties (EC, NH+ 4 -N, NO3 -N, and TC), bacterial community (ACE richness and bacterial abun­ dance), and relative abundance of genes were imported into AMOS 17.0 (SPSS Inc., Chicago, USA) to conduct structural equation models (SEMs). Standardized direct and indirect effects were calculated automatically by the SEMs based on whether the rela­ − tionship between one variable (EC, NH+ 4 -N, NO3 -N, TC, ACE rich­ ness, bacterial abundance, relative abundance of MGEs) and target variable (relative abundance of ARGs) is direct or indirect. ΔCT = CT detected ARGs or MGEs − CT 16S rRNA gene Relative abundance = 2− ΔCT Absolute abundance = 16S rRNA gene absolute abundance × 2− ΔCT 3. Results where the absolute abundance of 16S rRNA gene was measured by qPCR system utilizing the primer pair BACT1369F/PROK1492R along with the TaqMan probe TM1389F [32]. 3.1. Impacts of BS application on ARGs and MGEs in soil 167 ARGs and 10 MGEs were detected totally in 45 soil samples. As shown in Fig. 1a, the highest total detected number of ARGs and MGEs (105) occurred in S180 treatment of 0–30 cm, while the lowest total detected count of ARGs and MGEs (83) occurred in the S0 and S60 treatments of 60–90 cm. The observed counts of ARGs and MGEs decreased significantly (P < 0.05) with increased soil depth. Compared with S0 treatment, the number of ARGs first decreased and then increased with higher BS application rates. The highest abundance of ARGs was observed under the S180 treat­ ment in the 0–30 cm soil layer, while the peak ARG levels occurred under the S240 treatment in the 30–60 cm layer. In the 60–90 cm layer, the abundance of ARGs began to rise starting from the S120 treatment. The relative abundance of ARGs and MGEs were presented in Fig. 1b. The highest relative abundance (0.65 copies at 0–30 cm, 0.70 copies at 30–60 cm, and 1.07 copies at 60–90 cm) occurred in the S180 treatment, which demonstrated that there was a sub­ stantial variation in comparison with other treatments (P < 0.05). The classes of aminoglycoside, FCA (fluoroquinolone, quinolone, florfenicol, chloramphenicol, and amphenicol), multidrug, and sulfonamide resistance genes all showed the same trend. Notably, 120 unique ARGs and 10 MGEs were present in BS, significantly more than those in the soil samples (Fig. S1). Furthermore, the relative abundance of ARGs and MGEs in BS was 2.21 copies, multiplying the concentrations observed in the soil samples. 2.4. Bacterial community analysis via Illumina sequencing The bacterial 16S rRNA gene (V4-V5 region) was amplified for bacterial community characterization using the barcode primer pair 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′CCGTCAATTCMTTTRAGTTT-3′) [33] at Majorbio BioPharm Tech­ nology Co. Ltd., Shanghai, China with an Illumina Miseq PE250 platform. The PCR protocol involved the thermal cycling program at 95 ◦ C for 3 min, succeeded by 26 cycles of 95 ◦ C for 30 s, 59 ◦ C for 30 s, and 72 ◦ C for 30 s, 72 ◦ C for 7 min to final extension step. The 25 μL reaction mixture contained 12.5 μL of TB Green Premix Ex TaqII (TaKaRa Bio Inc., Japan), 1 μL of DNA template, 1 μL of forward and reverse primers respectively, and 9.5 μL of RNA-free water. A negative control and three technical replicates were conducted. The initial sequence data, devoid of barcode and primer se­ quences, were subjected to quality control to remove any poor quality. The trimmed sequences were merged with the forward and reverse reads after truncating, and the chimeras were removed in Usearch (version 10.0) [34,35]. Operational taxonomic units (OTUs) were formed by clustering sequence that based on a 97 % sequence similarity threshold. A single, representative sequence from each OTU was selected for taxonomic classification using BLAST [36] and then aligned with the SILVA138 database within QIIME to ensure optimal sequence homology. We collected a total of 15,478,596 high-quality 16S rRNA gene sequences, averaging 128,459 sequences per sample, which were clustered into 27,030 OTUs. Bacterial alpha diversity indices (ACE richness), and phylogenetic diversity, were calculated using QIIME, as well as beta-diversity based on the weighted UniFrac distance. In this study, ACE richness was selected to illustrate the alpha diversity of bacterial community. The raw bacterial sequences have been archived in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and are accessible under the accession number PRJNA1148815 (SRP526797). 3.2. Influence of BS application on core ARGs in soil To illustrate the influence of BS utilization on soil ARGs in detail, core ARGs were screened as typical ARGs in this study. In total, nine ARGs (aac(6’)-Ib-03, aadA-02, aadA1, aadA2-03, aphA1, mexF, qacEdelta1-01, qacEdelta1-02, and sul1) were defined as core ARGs, which accounted for more than 70 % (71.0 %) of the relative abundance of ARGs. These core ARGs were dominated by genes associated with aminoglycoside, multidrug, and sulfonamide resistance genes in Fig. 2a. With the increase of nitrogen contents of the BS from treatments S0 to S180, nearly all the relative abundance of core ARGs increased, such as sul1, qacEdelta1-02, qacEdelta1-01, mexF, aphA1, aadA2-03, aadA1, and aadA-02. In the S240 treatment, the total relative abundance of the core ARGs decreased to the S0 level. Compared with the core ARGs in soil, more core ARGs, including aadA1, aadA2-01, aadA2-02, aadA2-03, blaGES, qacEdelta1-01, qacEdelta1-02, sul1, tetA-02, tetM-01, and tetR-02, were detected in the BS (Fig. S2). These 11 core ARGs were superseded by genes linked to aminoglycoside, beta lactamase, multidrug, sulfonamide, and tetracycline resistance genes. In this study, four resistance mechanisms were determined, as shown in 2.5. Statistical analysis Microsoft Excel 2010 was used to conducted basic math oper­ ations of the raw data such as the sum, average, and multiply. Difference between the variables was determined using one-way ANOVA (P < 0.05) in SPSS (v 20.0, IBM, USA). A bar plot and a box plot were visualized by the ImageGP platform [37]. Nonmetric multidimensional scaling (NMDS) were performed using the vegan package in R 3.5.2. The networks in the different 3 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 Fig. 1. Diversity (a) and relative abundance (b) of ARGs and MGEs in different treatments. S0, S60, S120, S180 and S240 represent the treatments amended with biogas slurry with different nitrogen contents. 0–30, 30–60 and 60–90 represent different soil depth. The above number represent the detected number of ARGs and the below one represent the detected number of MGEs. Different letters above the plot represent a significant difference (P < 0.05) among different treatments. Fig. 2. Relative abundance of core ARGs (a) and mechanisms (b) in different treatments. S0, S60, S120, S180 and S240 represent the treatments amended with biogas slurry with different nitrogen contents. 0–30, 30–60 and 60–90 represent different soil depth. The numbers on pie chart represent detected number and percentage of ARGs. The outer and inner circles represent total detected ARGs and core ARGs, respectively. Fig. 2b. For the core ARGs, the resistance mechanisms were distributed in antibiotic deactivation (55.6 %), efflux pumps (33.3 %), and cellular protection (11.1 %), similar to the percentage of resistance mechanisms in the total ARGs. with S180 treatment, while it decreased significantly under the S240 treatment relative to the S180 across all soil depths (P < 0.05) (Fig. 3a). According to Fig. 3a, the bacterial abundance decreased significantly with the applied amount of BS from S0 to S120 treatments at 0–30 cm, and the opposite results were observed at 60–90 cm. In addition, the bacterial abundance decreased followed by increase from S0 to S120 treatments at 30–60 cm. ACE richness was selected to measure community richness within alpha diversity in this study, and a high ACE richness is usually associated with high alpha diversity. The bacterial alphadiversity in the soil samples decreased significantly with 3.3. Impacts of BS application on bacterial community structure and abundance The bacterial abundance in the soil samples was presented as the 16S rRNA copies per gram of dry soil. Interestingly, the highest bacterial abundance values in the three soil depths all occurred Fig. 3. Bacterial abundance (a) and alpha diversity (b) in different treatments. S0, S60, S120, S180 and S240 represent the treatments amended with biogas slurry with different nitrogen contents. Different letters above the plot represent a significant difference (P < 0.05) among different treatments. 4 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 increasing soil depth (P < 0.05) (Fig. 3b). The results of bacterial composition at the phylum level suggested that Proteobacteria, Actinobacteria, and Acidobacteriota were the top three dominant phyla followed by Chloroflexi, Planctomycetota, and others (Fig. S3a). These results differed significantly from the BS, where Firmicutes, Bacteroidota, and Proteobacteria were the dominant phyla (Fig. S3b). With increased soil depth, the relative abundance of Proteobacteria and Actinobacteria phyla decreased. Distinct patterns in the distribution of bacteria were shown by the NMDS ordination analysis between the treatments and sample depths. The bacterial pattern of the soil samples showed clear divergence from the three soil depths (R2 = 0.59, P < 0.01) (Fig. S4). To illustrate the phenomenon of co-presence within bacterial communities, bacterial network analysis were applied to different treatments (Fig. 4). Compared to S0 treatment, there was a notable decrease in the average degrees of the network from 9.68 in treatment S0 to 7.52, 8.14, and 8.05 in treatments S60, S120, and S240, respectively, and increased to 11.37 in the treatment S180. Meanwhile, bacterial associations were significantly complicated in the S180 and S240 treatments, with greater effects observed in the S180 treatment (Fig. 4a). The network edges were markedly reduced from 1694 in the S0 treatment to 1508 and 1562 in treatments S60 and S120, respectively, and increased to 2160 and 1780 in treatments S180 and S240, respectively. The percentage of mutual exclusion edges dropped in all soils amended with BS than in the control. Network hubs were further defined for each network with a high value of degree (>50) and closeness centrality (>0.3) in the network (Fig. 4b). The results showed a decrease in network hubs from 4 to 0 and 1 as the amount of BS increased from S0 to S60 and S120 treatments, respectively. The highest number of network hubs (21) occurred in the S180 treatment. No network hub was found in the S60 treatment. slurry application from S60 to S240, the microbial potential hosts are Thermoclostridium (9), Verrucomicrobiota (14), Elusimicrobiota (17), and Novibacillus (16). 3.5. Contributions of soil properties, bacteria and MGEs to ARG characterizations amended with BS After biogas slurry application, the pH values in soil of 0–30 cm and 30–60 cm depth were showed in Table S1. From the table, no significant differences were found between different treatments in one soil depth (P > 0.05). As for AP and OM, Therefore, soil prop­ − erties (EC, NH+ 4 -N, NO3 -N, and TC), bacterial community and abundance, and MGEs were selected to construct SEMs to evaluate the effects on ARG profiles with different BS applications (Fig. 6a). All of these factors explained 99.6 % of the ARG patterns in soil (R2 = 0.996). The critical factor was MGEs that directly impacted the relative abundance of ARGs (Fig. 6b), with a significant positive relationship (λ = 1.018, P < 0.001). In addition, EC in the soil indirectly impacted the relative abundance of ARGs (Fig. 6b) by affecting the relative quantities of MGEs, a substantial positive association was established (λ = 0.815, P < 0.001). The concen­ tration of NH+ 4 -N also directly influenced the profile of the ARGs (λ = − 0.073, P < 0.001), and the concentration of NO−3 -N was indirectly impacted by the abundance of the MGEs (λ = 0.267, P < 0.01) and bacterial abundance (λ = 0.331, P < 0.05). Bacterial ACE richness exhibited an indirect yet robustly significant associ­ ation with the ARGs (λ = − 0.558, P < 0.001). Similarly, bacterial abundance showed a negative and significantly strong relationship with ARGs (λ = − 0.125, P < 0.001). However, the total effect (direct and indirect effects) of bacterial abundance on ARGs was positive (Fig. 6b). 4. Discussion 3.4. Relationships among bacteria and ARGs and MGEs in different treatments In this study, BS of different concentrations were applied to evaluate the impacts of BS concentration on the abundance of ARGs. No significant relationships were found between either AP and ARGs or OM and ARGs in soil (R2 = 0.0136 and 0.0863, respectively) (Fig. S5), so nitrogen is considered to be the main influencing factor of ARGs abundance among different BS contents. The abundance of ARGs did not immediately increase with increasing BS application; however, significant fluctuations were observed at S180 treatment. These findings partially diverge from earlier research, which suggested that the application of biogas slurry tends to elevate ARG concentrations in agricultural soils. [15,23,27,29]. However, the studies differed in the amounts of BS applied—for example, 550–800 m3 per hectare, 150 mg N kg− 1, or 750 kg ha− 1—making direct comparisons difficult. Variations in ARG abundance across studies may be attributed to these differ­ ences in BS application rates. Different BS dosages introduce varying levels of nutrients, which can lead to distinct changes in soil ARG profiles. The underlying mechanisms likely involve a combination of disinfection effects and nutrient inputs, both of which influence ARG dynamics. The balance between these two factors—pathogen suppression and nutrient enrichment—appears to be key to determining the overall ARG response in soils treated with BS of differing nitrogen content. Co-occurrence networks between the ARGs, MGEs, and bacte­ rial phylum for the S0, S60, S120, S180, and S240 treatments, revealing the relationship between the genes and potential bac­ terial hosts (Fig. 5). The topological indices of the co-occurrence networks between the bacteria, MGEs, and ARGs were also found in the different treatments (Table 1). We observed 466 nodes and 968 edges in the S0 treatment. Among these, gene nodes accounted for 20.39 %, including 89 ARG nodes and 6 MGE nodes, and the tetL-02 resistance gene had a maximum degree of 68. Compared to the S0 treatment, significantly simpler network relationships occurred in the S60 and S120 treatments with 232 nodes and 316 edges, 321 nodes and 553 edges, respectively. In addition, the maximum degrees were 26 (sul2 resistance gene) and 37 (tetL-02 resistance gene) in the S60 and S120 treatments, respectively. Higher numbers of mutual exclusion were found in the S60 and S120 treatments (70.89 and 83.00) than in the S180 treatment (57.11), indicating more severe competition between the microorganisms in low BS treatments. However, the ratio of genes increased (2 %–7 %) when the ratio of bacterial taxa decreased. Interestingly, similar network complexity was observed between the S180, S240, and S0 treatments, a higher maximum degree occurred in the S180 and S240 treatments at 112 (ttgA resistance gene) and 128 (marR-01 resistance gene), respectively. Notably, the minimum ratio of gene nodes and the maximum ratio of co-presence were both revealed in the S180 treatment. Across various biogas slurry application treatments, ARGs exhibit distinct potential hosts. Notably, the genus of Dethiobacter predominates with the highest degree (20), emerging as the most significant potential host under S0 treatment. With the escalation in biogas 4.1. Changes of ARGs in soil under a low level of BS application With changes in BS application from 0 to 120 kg N ha− 1, the bacterial abundance significantly decreased, while the abundance of ARGs did not change (Fig. 3a). These results indicated that BS application with low nitrogen contents moderated the increase of ARGs, and the primary reason was that the scarcity of nitrogen 5 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 Fig. 4. Visualized networks of bacterial co-occurrence patterns (a) and network hubs (b) in different treatments. The nodes in different color represent bacterial different modularity classes in figure a. The edges in green and red represent co-presence and mutual exclusion patterns among taxa, respectively. Network hubs were visualized based on degree and closeness centrality in the network of different treatments, with the network hubs defined as degree >50 and closeness centrality >0.3. Different node colors denote different phyla in figure b. limited microbial growth, thereby highlighted the disinfection effects of biogas slurry. These findings support our hypothesis. This leads to a moderation in ARGs due to the subsequent decline in bacterial populations. Microorganisms, especially bacteria, were carriers of ARGs, and changes in bacterial numbers inevitably affected the abundance of ARGs [38–40]. Previous studies indi­ cated that native microorganisms have the potential to inhibit the invasion and colonization of exogenous antibiotic resistant bac­ teria within the BS in the soil, thus reducing the increase of ARGs [41]. Nitrogen, a vital constituent of all biological entities, is crucial for the biosynthesis of proteins and nucleic acids, which are fundamental cellular constituents [42]. The introduction of biogas slurry with low nitrogen contents markedly diminishes bacterial activity and proliferation. Consequently, at a low level of BS application, nitrogen limitation and bactericidal effects coexisted for the reduction in ARGs. 6 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 Fig. 5. Networks revealing the co-occurrence patterns among bacterial taxa (at the genus level) and ARGs and MGEs in different treatments. The nodes with different colors represent different classes of ARGs or bacterial taxa. The nodes with different sizes represent different degree. Table 1 Topological indices of co-occurrence network among bacterial taxa (at genus level) and ARGs and MGEs in different treatments. No. nodes No. edges Max. degree Avg. degree Bacterial nodes (%) Gene nodes (%) No. of co-presence (%) No. of mutual exclusion (%) Max. potential host (degree) S0 S60 S120 S180 S240 466 968 68(tetL-02) 4.155 79.61 20.39 30.68 69.32 Dethiobacter (20) 232 316 26(sul2) 2.724 72.41 27.59 29.11 70.89 Thermoclostridium (9) 321 553 37(tetL-02) 3.445 77.26 22.74 17.00 83.00 Verrucomicrobiota (14) 478 879 112(ttgA) 3.678 83.05 16.95 42.89 57.11 Elusimicrobiota (17) 454 698 128(marR-01) 3.075 78.41 21.59 33.38 66.62 Novibacillus (16) crucial mediators in acquiring and spreading ARGs through HGT [12,43]. Nutrients imported from BS promoted the expansion of microorganisms and HGT mediated by MGEs between microor­ ganisms in soil. These results were in agreement with most current studies on the effects of BS application to soil ARG profiles [23]. At a BS level of 240 kg N ha− 1, the relative abundance of ARGs in S240 was lower than that of S180 but comparable to that of S0. The results highlight the critical role of the disinfection effect of BS in the spreading of ARGs. This is because copious application of the viscous BS leads to soil saturation and anaerobic conditions, which promote the retention of ammonium nitrogen [22]. The ensuing conversion of ammonium to ammonia is toxic to microbes, as it can cross microbial cell membranes, disrupt intracellular pH, and trigger the release of small organic molecules, ultimately sup­ pressing the microbial community and curbing the increase of 4.2. Changes of ARGs in soil under a high level of BS application In the case of high level of BS application (180 kg N ha− 1) in this work, the effect of BS with abundant nutrients was the primary reason for the escalation of ARGs. At a level of 180 kg N ha− 1, bacterial abundance was higher than that in the control group, suggesting that BS application provided sufficient nutrients for the growth of bacteria, and thus enhanced the proliferation of mi­ croorganisms. Furthermore, the results suggested that the increase of network hubs, nodes, edges, maximum degree, and co-presence ratio provided a stronger HGT effect between the soil microor­ ganisms under a high nitrogen level. The HGT between the different bacterial species after nutrient-rich BS application was considered the fundamental processed for the spreading of ARGs. MGEs, including plasmids, integrons, and transposons, served as 7 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 Fig. 6. Structural equation models showing the relationships of soil properties, bacterial abundance, bacterial diversity, and MGEs on the relative abundance of ARGs in biogas slurry amended soils (a). Standardized direct and indirect effects derived from the structural equation models (b). Continuous and dashed arrows indicate positive and negative relationships, respectively. Red, blue, and green colors represent the effects path on ARGs, bacteria, and MGEs, respectively. Numbers adjacent to arrows are path coefficients. R2 values denote the proportion of variance explained. Significance levels are indicated: *0.01 < P ≤ 0.05, **0.001 < P ≤ 0.01, and ***P ≤ 0.001. The hypothetical models fit our data well, as suggested by χ2 = 4.38, P = 0.62, GFI = 0.93, AIC = 64.38, and RMSEA = 0.00. BacDiv represent bacterial alpha diversity (ACE richness); BacNum represent bacterial abundance. ARGs [44–46]. The above inference was also supported by SEMs analysis, which showed a direct negative impact of NH+ 4 -N on ARGs in this study. Throughout the past several years, only few studies have explored the reduction of ARGs by BS application through the disinfection effect, with more studies conducted on the effect of BS on soil-borne diseases. A two-year field experiment confirmed that the treatment of soil flooding with BS had a good control ef­ fect on root-knot nematodes and plant growth by altering soil nematode communities [46]. In plots treated with biogas slurry (BS), the incidence of Fusarium wilt in the soil was markedly reduced. Flooding with BS in soils infested with Fusarium oxy­ sporum, as a soil disinfestation treatment, appeared to greatly decrease the soil infestation levels of this pathogen [22]. Li et al. [45] studied the effects of BS application on tomato root-knot nematodes in pot experiments and showed that the application of BS effectively inhibited root-knot nematodes. Therefore, more profound investigation on the mechanisms governing the effect of BS application on ARGs are required in the future. 4.3. The application potential and limitations Our findings are based on a field experiment at a single soil type, which may limit the direct extrapolation of the results to other regions with different soil conditions. Furthermore, this study captured the patterns of ARGs at a single period (after har­ vest), and thus does not account for potential seasonal variations in the dynamics of the soil resistomes. Future research should therefore focus on validating these findings across a wider range of 8 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 soil types and different types of BS to establish more universal application guidelines. Moreover, employing metagenomic approach would be a powerful next step to precisely identify the bacterial hosts of the key ARGs, thereby providing a deeper un­ derstanding of the transmission risks. resistance genes in Chinese swine farms, Proceed.National Acad. Sci.USA 110 (9) (2013) 3435–3440. [5] Y.G. Zhu, M. Gillings, P. Simonet, et al., Microbial mass movements, Science 357 (6356) (2017) 1099–1100. [6] F. Wang, L. Xiang, K. Sze-Yin Leung, et al., Emerging contaminants: a one health perspective, Innovation 5 (4) (2024) 100612. [7] World Health Organization, Antimicrobial Resistance: Global Report on Surveillance, WHO Press, Geneva, 2014. [8] F. Zheng, Q.F. Bi, M. Giles, et al., Fates of antibiotic resistance genes in the gut microbiome from different soil fauna under long-term fertilization, Environ. Sci. Technol. 55 (1) (2020) 423–432. [9] W.Y. Xie, S.T. Yuan, M.G. Xu, et al., Long-term effects of manure and chemical fertilizers on soil antibiotic resistome, Soil Biol. Biochem. 122 (2018) 111–119. [10] X. Zhao, J.H. Wang, L.S. Zhu, et al., Field-based evidence for enrichment of antibiotic resistance genes and mobile genetic elements in manure-amended vegetable soils, Sci. Total Environ. 654 (2019) 906–913. [11] Y. He, Q. Yuan, J. Mathieu, et al., Antibiotic resistance genes from livestock waste: occurrence, dissemination, and treatment, npj Clean Water 3 (2020) 4. [12] M. Mu, F. Yang, B. Han, et al., Manure application: a trigger for vertical accumulation of antibiotic resistance genes in cropland soils, Ecotoxicol. Environ. Saf. 237 (2022) 113555. [13] F. Wang, R. Sun, H. Hu, et al., The overlap of soil and vegetable microbes drives the transfer of antibiotic resistance genes from manure-amended soil to vegetables, Sci. Total Environ. 828 (2022) 154463. [14] Z. Xiao, R. Han, J. Su, et al., Application of earthworm and silicon can alleviate antibiotic resistance in soil-Chinese cabbage system with ARGs contamina­ tion, Environ. Pollut. 319 (2023) 120900. [15] C.J. Pu, H. Liu, G.C. Ding, et al., Impact of direct application of biogas slurry and residue in fields: in situ analysis of antibiotic resistance genes from pig manure to fields, J. Hazard Mater. 344 (2018) 441–449. [16] Y. Guo, T. Qiu, M. Gao, et al., Diversity and abundance of antibiotic resistance genes in rhizosphere soil and endophytes of leafy vegetables: focusing on the effect of the vegetable species, J. Hazard Mater. 415 (2021) 125595. [17] X. Zhao, J.P. Shen, C.L. Shu, et al., Attenuation of antibiotic resistance genes in livestock manure through vermicomposting via Protaetia brevitarsis and its fate in a soil-vegetable system, Sci. Total Environ. 807 (2022) 150781. [18] Y.J. Zhang, H.W. Hu, Q.L. Chen, et al., Transfer of antibiotic resistance from manure-amended soils to vegetable microbiomes, Environ. Int. 130 (2019) 104912. [19] Z. Mei, L. Xiang, F. Wang, et al., Bioaccumulation of Manure-borne antibiotic resistance genes in carrot and its exposure assessment, Environ. Int. 157 (2021) 106830. [20] Y. Wang, J. Cai, X. Chen, et al., The connection between the antibiotic resis­ tome and nitrogen-cycling microorganisms in paddy soil is enhanced by application of chemical and plant-derived organic fertilizers, Environ. Res. 243 (2024) 117880. [21] C. Liu, Y. Chen, X. Li, et al., Temporal effects of repeated application of biogas slurry on soil antibiotic resistance genes and their potential bacterial hosts, Environ. Pollut. 258 (2020) 113652. [22] Y. Cao, J.D. Wang, H.S. Wu, et al., Soil chemical and microbial responses to biogas slurry amendment and its effect on Fusarium wilt suppression, Appl. Soil Ecol. 107 (2016) 116–123. [23] Y. Lu, J.M. Li, J. Meng, et al., Long-term biogas slurry application increased antibiotics accumulation and antibiotic resistance genes (ARGs) spread in agricultural soils with different properties, Sci. Total Environ. 759 (2021) 143473. [24] N. Li, R. Chang, S. Chen, et al., The role of the biogas slurry microbial com­ munities in suppressing fusarium wilt of cucumber, Waste Manag. 151 (2022) 142–153. [25] Y. He, D. Zhu, D. Wang, Diversity of antibiotic resistance genes in paddy soils in Sichuan Province,China, J. Agro-Environ.Sci. 39 (6) (2020) 1249–1258. [26] Z. Wang, N. Zhang, C. Li, et al., Diversity of antibiotic resistance genes in soils with four different fertilization treatments, Front. Microbiol. 14 (2023) 1291599. [27] Q. Sui, J. Zhang, M. Chen, et al., Fate of microbial pollutants and evolution of antibiotic resistance in three types of soil amended with swine slurry, En­ viron. Pollut. 245 (2019) 353–362. [28] M. Baker, A.D. Williams, S.P.T. Hooton, et al., Antimicrobial resistance in dairy slurry tanks: a critical point for measurement and control, Environ. Int. 169 (2022) 107516. [29] J. Huygens, G. Rasschaert, M. Heyndrickx, et al., Impact of fertilization with pig or calf slurry on antibiotic residues and resistance genes in the soil, Sci. Total Environ. 822 (2022) 153518. [30] X. Zhao, J.P. Shen, L.M. Zhang, et al., Arsenic and cadmium as predominant factors shaping the distribution patterns of antibiotic resistance genes in polluted paddy soils, J. Hazard Mater. 389 (2020) 121838. [31] F. Wang, M. Xu, R.D. Stedtfeld, et al., Long-term effect of different fertilization and cropping systems on the soil antibiotic resistome, Environ. Sci. Technol. 52 (22) (2018) 13037–13046. [32] M.T. Suzuki, L.T. Taylor, E.F. DeLong, Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5'-nuclease assays, Appl. Environ. Microbiol. 66 (11) (2000) 4605–4614. [33] J. Zhou, L. Wu, Y. Deng, et al., Reproducibility and quantitation of amplicon 5. Conclusion In summary, this work provided comprehensive insights into nitrogen contents within BS on the patterns of ARGs in soils. The concentration of NH+ 4 -N in soil under BS application was the direct driving factor influencing ARG characterizations. In detail, BS application moderated the increase of ARGs at low nitrogen levels as a result of the restricted nitrogen supply and disinfection effects in BS. High nitrogen contents in BS application could directly in­ crease the relative abundance of ARGs in soil as the enhancement from nutrients exceeded the function of disinfection. The anaer­ obic environment greatly abated the ARGs abundance when the soil under saturated conditions with BS application. This study provides valuable insights into the effects and potential mecha­ nisms of targeted BS application on soil ARGs, while also high­ lighting the high utilization potential of BS as a soil amendment. Future research should further explore how different BS applica­ tion strategies affect ARG abundance across various soil types, in order to improve the generalizability and practical relevance of these findings. CRediT authorship contribution statement Xiang Zhao: Writing – original draft, Methodology, Funding acquisition, Data curation, Conceptualization. Jian Wang: Writing – original draft, Methodology, Data curation. Yufei Li: Software, Investigation. Qianqian Lang: Software, Investigation. Jijin Li: Validation, Resources. Bensheng Liu: Investigation. Guoyuan Zou: Resources, Conceptualization. Junxiang Xu: Software, Investiga­ tion. Qinping Sun: Writing – review & editing, Resources, Funding acquisition, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was supported by, the National Natural Science Foundation of China (grant number 42207034), the Scientific and technological innovation capacity building project of BAAFS (KJCX20251007), and the Youth Fund Project of Beijing Academy of Agriculture and Forestry Sciences (grant number QNJJ202215). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.emcon.2025.100594. References [1] A. Pruden, R. Pei, H. Storteboom, et al., Antibiotic resistance genes as emerging contaminants: studies in northern Colorado, Environ. Sci. Technol. 40 (23) (2006) 7445–7450. [2] UNEP, Frontiers 2017 Emerging Issues of Environmental Concern, 2017. [3] H.K. Allen, J. Donato, H.H. Wang, et al., Call of the wild: antibiotic resistance genes in natural environments, Nat. Rev. Microbiol. 8 (4) (2010) 251–259. [4] Y.G. Zhu, T.A. Johnson, J.Q. Su, et al., Diverse and abundant antibiotic 9 X. Zhao, J. Wang, Y. Li et al. Emerging Contaminants 12 (2026) 100594 sequencing-based detection, J.ISME 5 (8) (2011) 1303–1313. [34] R.C. Edgar, Search and clustering orders of magnitude faster than BLAST, Bioinformatics 26 (19) (2010) 2460–2461. [35] R.C. Edgar, UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing, bioRxiv (2016) 081257. [36] J.G. Caporaso, J. Kuczynski, J. Stombaugh, et al., QIIME allows analysis of highthroughput community sequencing data, Nat. Methods 7 (5) (2010) 335–336. [37] T. Chen, Y.X. Liu, L.Q. Huang, ImageGP: an easy-to-use data visualization web server for scientific researchers, iMeta 1 (1) (2022) e5. [38] Q.L. Chen, X.L. An, H. Li, et al., Long-term field application of sewage sludge increases the abundance of antibiotic resistance genes in soil, Environ. Int. 92–93 (2016) 1–10. [39] F. Zheng, D. Zhu, M. Giles, et al., Mineral and organic fertilization alters the microbiome of a soil nematode Dorylaimus stagnalis and its resistome, Sci. Total Environ. 680 (2019) 70–78. [40] Q. Xiang, D. Zhu, M. Giles, et al., Agricultural activities affect the pattern of the resistome within the phyllosphere microbiome in peri-urban environments, J. Hazard Mater. 382 (2020) 121068. [41] Q.L. Chen, X.L. An, H. Li, et al., Do manure-borne or indigenous soil micro­ organisms influence the spread of antibiotic resistance genes in manured soil? Soil Biol. Biochem. 114 (2017) 229–237. [42] W.D. Vries, X.J. Liu, L.X. Yuan, Progress on nitrogen research from soil to plant and to the environment, Front. Agricult. Sci. Eng. 9 (3) (2022) 313–315. [43] G.S. Bbosa, N. Mwebaza, J. Odda, et al., Antibiotics/antibacterial drug use, their marketing and promotion during the post-antibiotic golden age and their role in emergence of bacterial resistance, Health 6 (5) (2014) 410–425. [44] K.S. Warren, Ammonia toxicity and pH, Nature 195 (4836) (1962) 47–49. [45] Y. Li, B. Liu, J. Xu, et al., Effects of soil flooding of biogas slurry on root-knot nematode (Meloidogyne spp.) and soil nematode community, Chin. J. EcoAgric. 28 (8) (2020) 1249–1257. [46] Y. Li, B. Liu, J. Li, et al., Flooding soil with biogas slurry suppresses root-knot nematodes and alters soil nematode communities, Heliyon 10 (9) (2024) e30226. 10
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