Emerging Contaminants 12 (2026) 100594
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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
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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
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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.
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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
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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.
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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.
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