Double-edged sword of FeS oxidation in estuarine and coastal sediments: accelerating elemental cycling yet enhancing antibiotic resistance enrichment

Wei Qiu; Xiufeng Tang; Boshuang Wu; Haoran Huang; Xiangyang Zhao; Jun Yuan; Xiaofei Li

doi:10.1128/aem.01525-26

. 2026 Sep 30;92(10):e01525-26. doi: 10.1128/aem.01525-26

Double-edged sword of FeS oxidation in estuarine and coastal sediments: accelerating elemental cycling yet enhancing antibiotic resistance enrichment

Wei Qiu ^1,^#, Xiufeng Tang ^1,^#, Boshuang Wu ², Haoran Huang ¹, Xiangyang Zhao ¹, Jun Yuan ^1,^✉, Xiaofei Li ^2,^✉

Editor: Jennifer F Biddle³

PMCID: PMC13689488 PMID: 42813796

ABSTRACT

Biogeochemical cycles exert significant influences on the fate and behavior of antibiotics in estuarine and coastal environments; however, ferrous sulfide (FeS)-mediated enrichment and dissemination of antibiotic resistance genes (ARGs) remain poorly understood. Here, we conducted microcosm experiments and field investigations to examine the relationships of FeS amendment, microbial communities, functional potentials in relation to elemental cycling, and ARG abundance. The positive effects of FeS on ARG abundance were aerobic dependent, which were accompanied by a significant enrichment of Proteobacteria, as the crucial hosts of ARGs. Proteobacteria were associated with systematic metabolic pathways for carbon fixation (Calvin-Benson-Bassham [CBB] cycle), N₂ fixation, and FeS oxidation. In addition, field samples from China estuarine and coastal wetlands further showed that CBB cycle, N₂ fixation, and sulfur oxidation pathways were significantly correlated with the relative abundance of ARGs. These findings collectively indicate that FeS oxidation facilitates enrichment and dissemination of ARGs through selective enrichment of Proteobacteria and consequent enhancement of metabolic synergies between sulfur, iron, carbon, and nitrogen cycles. However, FeS-mediated oxidation may also degrade microbial DNA and suppress ARG proliferation, indicating that the net outcome of ARG enrichment depends on oxygen conditions and microbial community filtering. The cascade processes of environmental regulation-community restructuring-metabolic synergy are of great significance to enhance ARG enrichment and dissemination. Therefore, redox fluctuations can enhance elemental cycling and further promote significant enrichment and dissemination of ARGs. These findings highlight the importance of redox dynamics in ARG monitoring and risk assessment in estuarine and coastal wetlands.

IMPORTANCE

Estuarine and coastal wetlands are important ecosystems for antibiotic resistance gene (ARG) enrichments. This study investigated FeS oxidation-mediated biogeochemical cycling and ARG dissemination. FeS oxidation increased enrichment and dissemination of the ARGs in estuarine and coastal sediments. The FeS oxidation reshaped microbial community structure and selectively enriched the critical Proteobacteria, which played the crucial hosts of the ARGs. The enrichment and dissemination of ARGs in the presence of FeS oxidation were due mainly to the enhanced metabolic coupling of sulfur oxidation, nitrogen fixation, and carbon fixation. CBB cycle, N₂ fixation, and sulfur oxidation pathways were also significantly correlated with ARG abundance along the latitudinal gradient. Therefore, enhanced elemental metabolic pathways under FeS oxidation exacerbated enrichment and dissemination of ARGs, highlighting the coupling mechanisms of biogeochemical cycle and ARGs in estuarine and coastal wetlands.

KEYWORDS: ferrous sulfide, ARGs, CBB cycle, nitrogen fixation, estuarine sediments

INTRODUCTION

Human activities have increased antibiotic loadings in the Earth environment, which are accompanied by a significant enrichment of antibiotic resistance genes (ARGs) and have attracted considerable attention in environments (1 –4). Estuarine and coastal environments are the critical ecological niches connecting terrestrial and marine ecosystems, which have greatly suffered from antibiotic pollution due to their widespread transformation and dissemination through water discharge (2). In addition, fishery activities use antibiotics to prevent diseases and further increase commercial production, making large accumulations of antibiotics and widespread dissemination in estuarine and coastal environments. Consequently, estuarine and coastal sediments can not only accumulate substantial antibiotics, and also are assumed hotspot settings for the enrichment and dissemination of ARGs due to dynamic redox conditions (5 –8). It is well acknowledged that redox fluctuations can enhance biogeochemical cycling; however, how the strengthened processes mediate the ARG enrichment and dissemination remains to be further clarified (9, 10).

Ferrous sulfide (FeS) is an iron mineral in anoxic sediments and has a profound effect on the migration and transformation of pollutants (11 –13). The tidal fluctuations can shape oxygen concentration gradient and cause reducing conditions in estuarine and coastal ecosystems, triggering abiotic and biotic FeS oxidation (14). These processes not only link the interactions between electron transfer and mineral phase transformation but also yield reactive oxygen species (ROS, such as ·OH), as well as changes in local microenvironmental pH and redox potential (Eh) (13, 15). Such changes constitute intense environmental selection pressures, directly reshaping the composition and function of microbial communities and exerting interactive effects on ARGs (16, 17). However, how the cascading effects of environmental conditions, biogeochemical processes, and microbial communities influenced by FeS oxidation mediate the enrichment and dissemination of ARGs remains poorly understood.

Microbial communities are the critical medium for ARGs, in which the composition and metabolic activity greatly determine the enrichment and dissemination of ARGs (18, 19). Previous studies have shown that specific microbial taxa, such as Proteobacteria and Bacteroidota, are the common hosts for various ARGs (20 –22). Estuarine and coastal sediments are enriched in organic matter and characterized by dynamically changing redox conditions, which are favorable to the FeS oxidation and further provide a critical energy source for microbial metabolisms (13). This energy-harnessing mechanism can directly drive metabolic processes such as carbon fixation and N₂ fixation, which are substantially responsible for biomass synthesis and microbial proliferation (23, 24). It should be noted that carbon fixation and nitrogen metabolic pathways are closely associated with ARG abundance (25), representing a potential coupling between substance metabolism and antibiotic resistance. Given that FeS oxidation is highly strengthened under tidal forces, we hypothesize that the enhanced coupling of carbon, nitrogen, and sulfur cycling processes can facilitate microbial community restructuring and metabolic reprogramming and further increase enrichment and dissemination of ARGs in estuarine and coastal environments. Previous studies on iron-sulfur mineral oxidation have mainly focused on heavy metal-associated co-selection and ROS-mediated damage to microbial cells. However, how FeS oxidation promotes ARG enrichment through the coupling of carbon, nitrogen, and sulfur metabolic pathways and the consequent enrichment of ARG-hosting taxa remain poorly understood.

In light of sparse knowledge of the enrichment and dissemination mechanisms of ARGs in estuarine and coastal sediments, we investigated the effects of FeS on ARGs under aerobic and anaerobic conditions through microcosm experiments. The main objectives of this study are (i) to clarify the enrichment of ARGs in the presence of FeS under redox fluctuations, (ii) to reveal the successful patterns of microbial communities in the presence of FeS oxidation, and (iii) to examine correlations between microbial metabolisms of carbon fixation, nitrogen fixation, sulfur, and abundance of ARGs in estuarine and coastal sediments. This study can provide insights into crucial constraints on the enrichment and dissemination of ARGs in estuarine and coastal environments.

MATERIALS AND METHODS

Study area and sample collections

Yangtze estuary is one of the largest estuaries in the world and is located on the East China coast (Fig. S1). This estuary is situated with a subtropical monsoon climate, with a mean annual temperature of 15.3°C and a mean annual precipitation of 1,100 mm. The Yangtze River has a length of 6,300 km and encompasses a watershed area of 180 × 10⁴ km². Yangtze estuary is characterized by a mixed river-tide estuary, due to the regular semi-daily fluctuations, with two high tides and two low tides every 6 h. Redox fluctuations are thus potential drivers of anaerobic and aerobic interface formations and biogeochemical cycling in this estuary. Chongming Dongtan Wetland (31°25'−31°38' N, 121°50'−122°05' E) is the largest and most well-developed tidal mudflat wetland in the Yangtze estuary. Sediments were collected in Dongtan wetland in June 2023. Surface sediment samples (0–10 cm) were collected from the low intertidal setting of Chongming Dongtan wetland (Fig. S1). Sediments were sampled in three replicates. All samples were stored at 4°C and transported to the laboratory within 2–4 h. After returning to the laboratory, sediments were kept at 4°C for conducting experimental incubation and determining physicochemical properties.

Experimental incubation

Surface sediments (0–5 cm) from the low tidal flat of Chongming Dongtan wetland were selected for the amendment incubation experiment because this layer represents the critical transition zone between anaerobic and aerobic conditions in estuarine sediments. Briefly, 300 g of fresh sediment was placed into a 500 mL brown glass jar with a septa-capped lid and mixed with overlying water to maintain a water content of 40%. Four treatments were established: (i) anaerobic + FeS, (ii) anaerobic, (iii) aerobic + FeS, and (iv) aerobic. The FeS powder (Macklin, product no. F809695) was homogeneously mixed with the sediments in an anaerobic glove box to a final concentration of 7 mg⁻¹ sediment (26). Anaerobic conditions were generated by bubbling helium gas through the headspace for 30 min, followed by immediate sealing of the jars with caps. Aerobic treatments were maintained under ambient air conditions. All incubation jars were placed in a 25°C dark incubator and incubated for 91 days. During incubation, deionized water was replenished in the aerobic treatments to compensate for evaporative loss and maintain the target moisture content. After incubation, sediments were collected for microbial analysis and physicochemical property determination (Fig. S2).

Determination of sediment properties

Water content was determined after drying at 105°C. Sediment pH was measured using a Mettler-Toledo pH meter with a soil-to-water ratio of 1:2.5 (weight/volume). Sediment was extracted with 2 mol L^–1 KCl solution and shaken at 150 rpm for 30 min and then was centrifuged at 3000 rpm for 5 min. The concentrations of NO₂⁻, NO₃⁻, and NH₄⁺ were determined using a continuous flow analyzer (SAN plus, Skalar Analytical B.V., Breda, Netherlands). Sediments were dried and ground and then passed through a 200-mesh nylon sieve. The screened sediments were acidified with 1 mol L⁻¹ HCl to measure total organic carbon content (TOC). Dissolved organic carbon (DOC) was extracted by deionized water at a ratio of 1:5 (weight/volume). The DOC and TOC were determined using a total organic carbon analyzer (SSM-5000A, Shimadzu, Japan). Easily oxidized organic carbon (EOC) was measured by potassium dichromate oxidation method (27). Microbial biomass carbon (MBC) and nitrogen (MBN) were determined by chloroform fumigation method (28, 29). Sulfate in sediments was extracted with deionized water and determined by ion chromatography (Dionex ICS-2000, America). Sulfide was measured by methylene blue spectrophotometry method (30). Fe²⁺ and Fe³⁺ concentrations in sediments were measured using a 0.1% o-phenanthroline colorimetric method (31). Sediment particle size was measured using a laser particle size analyzer (2000, Mastersizer, UK).

Gene catalog construction and ARG/MGE functional annotation

In this study, we used CD-HIT to de-replicate the gene set for identifying and quantitatively analyzing antibiotic resistance genes and further constructed a non-redundant gene catalog to ensure the accuracy of subsequent analyses. We used Diamond to align non-redundant gene sequences with the Comprehensive Antibiotic Resistance Database (CARD, v2.0.1) and the Mobile Element Database (mobileOG) (32), making the similarity threshold of 60% and the significance threshold of e-value <1 × 10⁻¹⁰ to strictly ensure the reliability of annotation results. Non-redundant gene sequences were aligned with the CARD using Diamond. After identifying the ARGs and mobile genetic elements (MGEs) based on the alignment results, we mapped the raw sequencing reads to the non-redundant gene set using the Salmon. The abundance of each gene was normalized by transcripts per million (TPM) to eliminate the effects of sequencing depth and gene length, thereby obtaining reliable gene abundance data (33). Finally, the relative abundance of various categories of ARGs and MGEs was calculated based on TPM values. Additionally, all gene sequences were aligned with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to explore their biological functional context and to obtain pathway annotation information.

DNA extraction, metagenomic sequencing, assembly, and genome binning

Total DNA in sediments was extracted using the DNeasy Power Soil Kit. DNA quality and concentration were detected using the NanoDrop-2000 spectrophotometer. Subsequently, DNA was subjected to paired-end sequencing on the Illumina NovaSeq platform using NovaSeq Reagent Kits. Data quality control was performed with Trimmomatic, metagenomic sequences were assembled with MEGAHIT, open reading frames were predicted with MetaGene, and a non-redundant gene catalog was constructed with CD-HIT-EST. Representative sequences were selected by comparing with the NCBI NR database.

We processed the assembly results using an integrated binning strategy (34). Specifically, binning was first performed separately using MetaBAT2 (35), MaxBin2 (36), and SemiBin2. Subsequently, their results were integrated using DAS Tool (37) to obtain a non-redundant set of optimal genomes. Dereplication clustering of these genomes was carried out with dRep to remove highly similar strains. Open reading frames were predicted using Prokka, and functional annotation was performed on the dereplicated genomes. The completeness and contamination of all genomes were evaluated using CheckM2 (38). We further performed species taxonomic annotation of the quality-qualified genomes using GTDB-Tk based on the latest version of the genome classification database (39). Finally, quality-filtered sequencing reads were mapped back to the metagenome-assembled genomes (MAGs) with CoverM to calculate their relative abundance in each sample. In addition, this study reanalyzed a publicly available metagenomic data set (SRP341954/PRJNA771937) comprising 16 sampling sites in estuarine and coastal wetlands. This data set was originally reported by Zheng et al. (40). We used it for environmental validation in the field, focusing on ARG abundance, Proteobacteria abundance, and their associations with key metabolic pathways.

Statistical analysis

Statistical analyses were performed using SPSS v23.1 and R v4.2.3. One-way analysis of variance (ANOVA) was used to test differences among treatments in sediment physicochemical properties, antibiotic abundance, relative abundances of ARGs and MGEs, ARG-MGE co-located gene clusters (AMCCs), and Proteobacteria abundance, followed by Tukey’s honestly significant difference (HSD) post hoc test for pairwise comparisons. To control for multiple testing in correlation analyses, P values were adjusted using the Benjamini-Hochberg false discovery rate method. For the SparCC-based co-occurrence network, only significant correlations with |r| > 0.6 and P < 0.05 were retained for network construction. Microbial community analyses were conducted in R v4.2.3. Alpha diversity indices were calculated using the vegan package, and beta diversity was assessed based on Bray-Curtis dissimilarity. Simple linear regression was used to examine the associations between key metabolic pathways and ARGs (P < 0.05). Sankey diagrams were generated to visualize links between ARGs and their potential microbial hosts. Mantel tests were performed to evaluate correlations between environmental factors and ARGs or MAGs. MAGs were retained only when they met the quality criteria of completeness >50% and contamination <10%, as estimated by CheckM2. Structural equation modeling was further used to assess the direct and indirect effects of environmental factors on ARGs and key ARG-carrying MAGs, and model fit was evaluated using standard indices, including χ²/df < 5, comparative fit index (CFI) ≥ 0.90, Tucker-Lewis index (TLI) ≥ 0.90, root-mean-square error of approximation (RMSEA) ≤ 0.08, and standardized root-mean-square residual (SRMR) ≤ 0.08.

RESULTS

Changes of ARGs and microbial communities

FeS addition significantly increased ARG abundance, as well as sulfonamide and multidrug resistance genes under aerobic conditions (Fig. 1a and b). FeS did not significantly affect the abundance of MGEs under anaerobic nor aerobic conditions (Fig. 1c). The gene clusters formed by ARGs and MGEs (AMCCs) showed that FeS significantly increased AMCCs abundance under aerobic conditions (Fig. 1d). Although the total abundance of MGEs did not change significantly under the Aerobic-FeS treatment, the abundance of AMCCs increased markedly. This indicates that FeS oxidation did not increase the overall MGEs pool, but instead strengthened the physical linkage between ARGs and MGEs at the genomic level, thereby potentially enhancing horizontal gene transfer. Significant separation in microbial structures was observed between the groups, and FeS addition reduced the microbial variation (Fig. S3a). FeS addition under aerobic conditions significantly reduced ACE, Chao, and Shannon indices (Fig. S3b). Proteobacteria, Chloroflexi, Acidobacteria, Actinobacteria, and Bacteroidetes were the dominant phyla in these groups (Fig. 2a). Relative abundance of Proteobacteria was significantly increased in the presence of FeS under aerobic conditions (Fig. 2b). The detected ARGs (22/28) were primarily derived from Proteobacteria (Fig. S3c). Despite the overall reduced complexity in the FeS + Aerobic-FeS group, this group showed the highest proportion of Proteobacteria (29/41) and the highest positive correlations among communities (61%; Fig. 2c through g). The reduced network complexity suggests strong environmental filtering under the Aerobic-FeS treatment. Meanwhile, the increased proportion of positive correlations may reflect enhanced metabolic complementarity and greater niche overlap among community members.

Composite four‑panel figure showing gene abundance under different incubation treatments. Shown are a bar plot for antibiotic‑resistance genes, a polar plot for ARG subtype distribution, and bar charts for normalized MGE and AMCC abundance. — Effects of FeS on antibiotic resistance genes (ARGs). (a) Effects of FeS addition on the total abundance of ARGs under aerobic and anaerobic conditions. Different letters above the bars indicate significant differences between treatments (Tukey’s HSD test, P < 0.05). (b) Effects of FeS addition on the abundance of different classes of ARGs under aerobic and anaerobic conditions. Different letters indicate significant differences between treatments based on the Tukey–Kramer HSD test following ANOVA (P < 0.05). (c) Effects of FeS addition on the abundance of mobile genetic elements (MGEs) under aerobic and anaerobic conditions. Different letters above the bars indicate significant differences between treatments (Tukey’s HSD test, P < 0.05). (d) Effects of FeS addition on the abundance of ARG–MGE co-occurrence clusters (AMCCs) formed by ARGs and MGEs under aerobic and anaerobic conditions. Different letters above the bars indicate significant differences between treatments (Tukey’s HSD test, P < 0.05). Each treatment had three biological replicates (n = 3).

Multi‑panel figure including bar graphs for phylum relative abundance and six microbial association network diagrams showing MAG correlation patterns across four experimental groups.. — Bacterial community changes under FeS addition. (a) Relative abundance (%) of the major bacterial phyla in different treatment groups. (b) Effects of FeS addition on the relative abundance of Proteobacteria under aerobic and anaerobic conditions. Different letters above the bars indicate significant differences between treatments (Tukey’s HSD test, P < 0.05). Each treatment had three biological replicates (n = 3). (**c–g**) MAGs-based species co-occurrence networks. Nodes represent individual MAGs, and edges indicate SparCC correlations (|r| > 0.6). Node size is proportional to the number of connections (degree), and node colors reflect taxonomic affiliation. The letter P stands for Proteobacteria.

MAGs and elemental metabolic potential

MAGs were affiliated with multiple bacterial phyla, including Chloroflexota (6), Gemmatimonadota (3), Myxococcota (1), Acidobacteriota (1), Bacteroidota (1), and Proteobacteria (24) (Fig. 3a). Notably, several MAGs (mag32, mag35, mag22, mag27, mag34, and mag37) affiliated with Proteobacteria were highly enriched in the presence of FeS under aerobic conditions (Fig. 3a). The functional annotation analysis showed that metabolic pathways were associated with carbon fixation, nitrogen metabolism, and FeS oxidation carried out by these MAGs. Except for mag14 and mag18, all other Proteobacteria-affiliated MAGs possessed the complete metabolic potential for carbon fixation, nitrogen metabolism, and FeS oxidation (Fig. 3b). Additionally, mag7 and mag42 affiliated with Gemmatimonadota, along with mag13, mag20, and mag30 affiliated with Chloroflexota, exhibited complete metabolic capabilities for carbon fixation, nitrogen cycling, and FeS oxidation.

Multi‑omics composite panel. Shown are a phylogenetic tree of MAGs coupled with bar plots counting MGE and ARG numbers per genome and a heatmap displaying functional pathway profiles for carbon, nitrogen and FeS‑oxidation metabolism across MAGs. — MAGs enrichment and their metabolic potentials for carbon, nitrogen, iron, and sulfur (a) Phylogenetic analysis of MAGs carrying both ARGs and MGEs. MAGs were color-coded at the phylum level. The outer ring (pie chart) displays the relative abundance distribution of these MAGs across different treatment groups. The bar graph indicates the numbers of MGEs (red bars) and ARGs (blue bars) contained within these MAGs. (b) MAGs with concurrent metabolic potential for carbon fixation, nitrogen metabolism, and iron-sulfur oxidation were identified. Metabolic functions are color-coded in blue (carbon fixation), pink (nitrogen metabolism), and yellow (iron-sulfur oxidation), with solid squares indicating that the corresponding pathway was annotated in the MAGs.

Metabolic pathways of ARGs enrichment

CBB cycle exhibited significant positive correlations with ARGs (Fig. 4a and b). Specifically, key genes of the CBB cycle, including the aldolase gene aldo, the fructose-1,6-bisphosphatase gene fbp, and the glyceraldehyde-3-phosphate dehydrogenase gene glpX, showed significantly positive correlations with a subset of ARGs (Fig. 4c). Nitrogen metabolism showed a significant positive correlation with ARGs (Fig. 4d). Anammox, DNRA, ANR, and N₂ fixation were significantly associated with ARGs, with N₂ fixation making the highest contribution (Fig. 4e). Nitrogen fixation genes nifK and nifH were significantly associated with antibiotic resistance genes opmH and qacEdelta1, respectively (Fig. 4f). Although FeS oxidation showed no significant correlation with ARGs, thiosulfate oxidation (S4I) pathway, the SOX system, and the sulfur oxidation metabolic pathway exhibited significant positive correlations with ARGs (Fig. 4g and h). The fccB for sulfur oxidation was significantly associated with multiple ARGs (pupM, rphA, efpA, and qepA4; Fig. 4i). To examine how the key metabolic pathways mediated ARGs, we further analyzed the relationships between key functional genes and ARGs (Fig. 5; Fig. S4). For the CBB cycle, the key enzyme genes rbcL and rbcS for the carboxylation process, as well as the key genes prkB and fbaA for the reduction phase, exhibited significant positive correlations with ARGs (Fig. 5a through d; Fig. S4a and b). Within the nitrogen fixation pathway, nifK, nifH, and nifD showed significant correlations with ARGs (Fig. 5e through g). In the sulfur oxidation pathway, the flavocytochrome c sulfide dehydrogenase genes fccA and fccB showed significant positive correlations with ARGs (Fig. 5h and i).

Three-column multi-panel figure. Scatter plots show ARG abundance-functional gene copy correlations; bar plots show pathway contribution proportions; networks show significant gene‑ARG associations for C, N, and FeS oxidation pathways. — Associations between carbon fixation, nitrogen metabolism, FeS oxidation, and ARGs. (**a and b**) The association between the total abundance of ARGs and carbon fixation genes, as well as the contribution of different carbon fixation pathways to the variation in ARG abundance. The r and P values above the bars indicate correlation strength and statistical significance. (c) Correlation analysis between CBB cycle-related genes and ARGs. The red lines indicate significant correlations between them (r > 0.7, P < 0.05). (**d and e**) The association between the total abundance of ARGs and nitrogen metabolism genes, as well as the contribution of different nitrogen metabolism pathways to the variation in ARG abundance. The r and P values above the bars indicate correlation strength and statistical significance. (f) Correlation analysis between nitrogen fixation-related genes and ARGs. The red lines indicate significant correlations between them (r > 0.7, P < 0.05). (**g and h**) The association between the total abundance of ARGs and FeS oxidation genes, as well as the contribution of different FeS oxidation pathways to the variation in ARG abundance. The r and P values above the bars indicate correlation strength and statistical significance. (i) Correlation analysis between sulfur oxidation-related genes and ARGs. The red lines indicate significant correlations between them (r > 0.7, P < 0.05).

Nine‑panel scatter‑regression plots illustrating significant positive correlations between ARG relative abundance and gene copy numbers of key carbon‑cycling, nitrogen‑cycling and sulfur‑cycling functional markers. — Correlation analysis of key genes in CBB cycle, nitrogen fixation, and sulfur oxidation processes with ARGs. (**a–d**) Linear regression relationship between key CBB cycle genes (*rbcS*, *rbcL*, *prkB*, and *fbaA*) and ARG abundance. (**e–g**) Linear regression relationship between key nitrogen fixation genes (*nifD*, *nifK*, and *nifH*) and ARG abundance. (**h and i**) Linear regression relationship between key sulfur oxidation genes (*fccA* and *fccB*) and ARG abundance.

Sediment factors mediate ARGs

FeS addition significantly increased concentrations of TOC, MBN, SO₄^2–, and Fe²⁺, but decreased EOC concentration under aerobic conditions (Fig. S5). Specific associations were observed between sediment factors and antibiotic ARGs. MBN showed significant positive correlations with multidrug and sulfonamide-resistance genes, while SO₄^2– was significantly correlated with multidrug-resistance genes. Fe³⁺ was significantly associated with β-lactam resistance genes, and Fe²⁺ was significantly linked to sulfonamide resistance genes. TOC exhibited a significant correlation with peptide resistance genes. pH showed no significant correlation with ARGs, but it was associated with AMCCs (Fig. 6a and b). TOC, DOC, and EOC were significantly correlated with ARGs and AMCCs. MBN was significantly correlated with ARGs and AMCCs. SO₄^2–, S^2–, Fe²⁺, and Fe³⁺ also showed positive and negative correlations with ARGs and AMCCs (Fig. 6a and b). S^2– not only had a significant direct positive effect on ARGs and AMCCs but can also indirectly affect the abundance of ARGs and AMCCs by regulating TOC and MBN (Fig. 6c and d).

Composite figure with two correlation heatmaps of MAG‑environment associations and two piecewise structural equation models quantifying direct and indirect effects of geochemical variables on AMCC and ARG abundance. — The impacts of sediment factors on ARGs. (a) Heatmap showing the correlation between environmental factors and metagenome-assembled genomes (MAGs) that simultaneously carry ARGs and mobile genetic elements (MGEs; *P < 0.05, **P < 0.01, and ***P < 0.001). The bar chart shows the number of MAGs significantly correlated with environmental factors, where red represents positive correlation, and blue represents negative correlation. (b) Heatmap showing the correlation between environmental factors and ARGs (*P < 0.05, **P < 0.01, and ***P < 0.001). The bar chart shows the number of ARGs significantly correlated with environmental factors, where red represents positive correlation, and blue represents negative correlation. (c) Analyze the direct and indirect relationships between S²⁻, Fe²⁺, TOC, MBN, and the abundance of MAGs carrying ARGs and MGEs based on structural equation modeling. The numerical values next to the arrows are standardized path coefficients, and R² represents the variance explained rate of the variables. Significance levels of regression weights were calculated using two-sided t-tests (*P < 0.05, **P < 0.01, and ***P < 0.001). Red represents a positive correlation, and blue represents a negative correlation. (d) Analyze the direct and indirect relationships between S²⁻, Fe²⁺, TOC, MBN, and the abundance of ARGs. The numerical values next to the arrows are standardized path coefficients, and R² represents the variance explained rate of the variables. Significance levels of regression weights were calculated using two-sided t-tests (*P < 0.05, **P < 0.01, and ***P < 0.001). Red represents a positive correlation, and blue represents a negative correlation.

DISCUSSION

The “One Health” concept emphasizes that antibiotics have pressured challenge to global ecosystems and human health (41 –43). With this in mind, ARGs, as emerging environmental pollutants (1, 44), have potential health risks but should not be ignored mainly due to their widespread transmission and dissemination in natural environments (2, 45). Estuarine and coastal wetlands are critical linkages between terrestrial and marine environments, which have suffered from substantial land-based pollutants and have posed regional ecological security and public health (7, 46). Sulfate respiration can yield FeS, which is generally formed in flooding and strongly reducing environments (11, 46). More importantly, redox dynamics in estuarine and coastal environments are highly coupled with sulfur and iron cycling, thereby playing a critical role in the migration and transformation of heavy metals and antibiotics (26, 46).

FeS oxidation facilitates ARGs enrichment

The positive effects of FeS on ARGs were oxygen-dependent because FeS oxidation occurs strongly under oxygen-enriched conditions. In this study, we found that FeS significantly increased the abundance of ARGs and AMCCs under aerobic conditions. However, anaerobic conditions did not increase the abundances of ARGs and AMCCs in the presence of FeS (P > 0.05), which is mainly due to the limited FeS oxidation. These results indicated that FeS oxidation is significant in triggering ARG enrichment. Previous studies also reported that redox fluctuations are critical factors affecting the spatial distribution of ARGs in estuarine and coastal sediments (47). It is well acknowledged that FeS can generate ROS under aerobic conditions (13). ROS can induce oxidative stress responses in microorganisms, further increasing the recombination frequency of MGEs and promoting the horizontal transfer of ARGs (13, 48). In contrast, FeS is relatively stable and cannot trigger the aforementioned chain reaction under anaerobic conditions, which could be due to limited biogeochemical cycling of iron and sulfide. These results confirm that significantly enhanced ARG abundance was observed in oxic sediments, further emphasizing that redox fluctuation is a critical factor affecting ARGs enrichment and distribution in estuarine and coastal environments.

However, some studies have reported that chemical oxidation processes can oxidatively degrade DNA of microbial communities and further inhibit ARGs spread in aquatic environments (49). This suggests that the oxidation process of FeS under aerobic conditions may cause a tradeoff effect between enrichment and degradation of ARGs. FeS oxidation under aerobic conditions can stimulate recombination of MGEs and biogeochemical cycling, further facilitating enrichment and dissemination of ARGs (13, 16). In contrast, the oxic FeS oxidation process can degrade extracellular or intracellular DNA via Fenton-like reactions, which strongly reduces ARGs enrichments (50, 51). The contradictory results underline an environmentally dependent dynamic tradeoff mechanism of ARGs enrichments, whereby the redox dynamics of FeS, along with the composition and activity of microbial community, collectively determine whether the net effect of ARGs mediated by FeS oxidation tends to “promote dissemination” or “drive degradation.”

Microbial community restructure favored ARGs hosts

Microorganisms are the key hosts of ARGs and directly regulate the enrichment and dissemination potential of ARGs through changes in their community composition and abundance (17, 52). In FeS-mediated sediments, the presence of FeS under aerobic conditions significantly reduced the α-diversity of microbial community but specifically increased the relative abundance of Proteobacteria. Therefore, the decreased microbial diversity would cause the enrichment of dominant taxa, which could be due mainly to the convergent community succession under environmental pressures (53). It has been well acknowledged that Proteobacteria are widely considered the main hosts and transmission vectors of ARGs in the bacterial kingdom (48). The enrichment of Proteobacteria in the presence of FeS oxidation may benefit from their strong antioxidant capacity, metal tolerance, as well as diverse energy metabolism pathways (54). In this study, host-tracking analysis strongly confirmed that the detected ARGs were mainly hosted by Proteobacteria, indicating that this phylum plays a critical role as reservoirs and transmission vectors of ARGs in FeS-enriched and aerobic environments.

Co-occurrence network analysis showed a shift in microbial interaction patterns that may affect enrichment and dissemination of ARGs. Although the complexity of the network in the presence of FeS under aerobic conditions decreased, Proteobacteria were mostly responsible for nodes (26/55) and exhibited the highest proportion of positive correlations (61%). This “simplified but more synergistic” network structure may facilitate efficient resource allocation and functional coordination, further enhancing the competitive advantage of Proteobacteria under extraneous pressures. In addition, it has been demonstrated that increasing the proportion of positive correlations between microbial networks can suggest niche overlap and functional complementarity (55), which are accompanied by metabolic coupling within Proteobacteria and between this phylum and other taxa in carbon, nitrogen, sulfur, and iron metabolisms.

Element metabolism-driven ARGs enrichment

Biogeochemical cycles of carbon, nitrogen, sulfur, and iron through element-cycling metabolic genes are expected to have potential effects on ARGs enrichment (56, 57). The enhanced metabolic processes not only provide energy and material foundations for microbial growth but also maintain the elemental balance of ecosystems (58, 59). It is worth noting that perturbations in the element metabolism processes are accompanied by changes in the abundance and composition of ARGs (10, 60). Sulfur-mediated biogeochemical processes can significantly couple with nitrogen reduction, Fe(II) oxidation, as well as carbon fixation (61). In addition, Fe(II) can enhance carbon fixation by chemoautotrophic processes (26) and also stimulate nitrogen reduction through electron transfer (24), playing a mediating role in the coupling of carbon and nitrogen cycles. Therefore, metabolic processes of carbon, nitrogen, iron, and sulfur can substantially stimulate the host pathways of ARGs in estuarine and coastal environments, representing mechanistic linkages between elemental metabolic activities and dissemination of antibiotic resistance.

However, the influences of elemental cycles on ARGs are not independent from each other; they are coupled through multi-element collaborative mechanisms (54, 62). In this study, Proteobacteria-affiliated MAGs were enriched under aerobic conditions, in which FeS had enhanced the complete metabolic pathways for carbon and nitrogen fixation and FeS oxidation. The abundance of key genes in these three metabolic pathways was positively correlated with ARG abundance and was more significant after FeS addition. N₂ fixation genes (nifK, nifH, and nifD) showed the greatest upregulation (up to 606%), and sulfur oxidation genes (fccA and fccB) and CBB cycle genes (rbcL, rbcS, fbaA, and prkB) were also significantly increased. From the perspective of energy metabolism, sulfur oxidation, as an energy-yielding process, can provide ATP and reducing power for the energy-consuming CBB cycle and N₂ fixation (13), further creating metabolic coupling within Proteobacteria. These metabolic couplings not only enhance the adaptive advantage of Proteobacteria in FeS oxidation environments but also indirectly promote co-selection and enrichment of ARGs by increasing cellular metabolic activity and biomass turnover (60). In addition, S^2– not only directly and positively regulated ARGs and AMCCs but also indirectly influenced them by affecting TOC and MBN for microbial activity. These results suggest that the sulfur cycle plays a vital role in FeS-driven ARGs enrichment, creating a multi-element synergistic driving network by coupling carbon and nitrogen metabolisms.

Environmental implications: from laboratory to field scales

In this study, we found that FeS oxidation altered microbial structure and elemental metabolic pathways, further leading to enrichment and dissemination of the ARGs. FeS addition under aerobic conditions was observed to favor the proliferation of Proteobacteria (with a 12% increase in relative abundance; Fig. S6). The positive impacts of Proteobacteria on ARGs were mainly related to the changes in three types of metabolic functions, namely sulfur oxidation, nitrogen fixation, and the CBB cycle. The abundance of key genes in the sulfur oxidation, nitrogen fixation, and CBB cycle pathways increased by 67%–606%, collectively creating the key molecular basis for regulating the enrichment and transfer of ARGs. In addition, sulfur oxidation can provide ATP and reducing power for the CBB cycle and biological nitrogen fixation, highlighting metabolic coupling within Proteobacteria. The results revealed that the total abundances of ARGs and AMCCs increased significantly after the addition of 7 mg/g FeS, whereas no obvious variation was observed in the total abundance of MGEs. This indicates that FeS enhances the horizontal transfer risk of ARGs not by increasing the abundance of MGEs but by strengthening the linkage between ARGs and MGEs on the genome.

To verify the environmental generalizability of our findings, we further analyzed data collected from 16 field sites in Chinese estuarine and coastal wetlands during the cold season (March–April) and warm season (August–September) of 2019 (Fig. 7a and b). The study area was divided into southern and northern regions by the Yangtze River (CJ). The abundances of ARGs and nitrogen fixation genes were significantly higher in the southern region, while no significant differences in the abundances of carbon fixation and sulfur oxidation genes were observed between regions or seasons (Fig. S7). Results of Mantel analysis and random forest analysis showed that sediment physicochemical variables collectively explained 37.49% of the variation in ARGs, and nitrogen-related variables, including NH₄⁺, NO₃⁻, and NO₂⁻, were the most important predictors for ARG variation (Fig. S8). In addition, ARG abundance was observed to significantly correlate with the abundance of Proteobacteria (Fig. 7c), suggesting the exact mechanistic linkages of FeS mediation, Proteobacteria enrichment, metabolic coupling, and ARGs increase in natural environments. It is worth noting that not all sites exhibited a synchronous increase in both variables, which may be attributed to other factors such as local sediment FeS, organic matter, and microbial structure, suggesting that this mechanism was co-regulated by multiple factors in estuarine and coastal environments. Most importantly, the CBB cycle, nitrogen fixation, and sulfur oxidation pathways were significantly correlated with the abundance of ARGs (Fig. 7d through f), further highlighting that Proteobacteria can drive the enrichment and dissemination of ARGs through multi-element metabolic synergies. Therefore, the coupling metabolic pathways have broader implications for ARGs dissemination in global estuarine and coastal environments.

Multi‑panel figure containing geographic distribution bar maps for Proteobacteria and ARGs across Chinese river sites, plus scatter‑regression plots showing correlations between ARG abundance and functional trait abundance in field samples. — Comprehensive mechanistic elucidation of ARGs enrichment driven by FeS. (a) Map showing the distribution of 16 sampling sites in the estuarine-coastal zone. Bar charts reflect the relative abundance of Proteobacteria at each site in the warm and cold seasons. (b) Map showing the distribution of 16 sampling sites in the estuarine-coastal zone. Bar charts reflect the relative abundance of ARGs at each site in the warm and cold seasons. (**c–f**) Dot-line plots showing the linear regression between the relative abundance of ARGs and that of Proteobacteria, CBB cycle, N₂ fixation, and sulfur oxidation, respectively.

Conclusion

In this study, FeS oxidation was observed to increase enrichment and dissemination of the ARGs in estuarine and coastal sediments. The FeS oxidation reshaped microbial community structure and selectively enriched the critical Proteobacteria, which played the crucial hosts of ARGs. The enrichment and dissemination of ARGs in the presence of FeS oxidation were due to the enhanced metabolic coupling of sulfur oxidation, nitrogen fixation, and carbon fixation. CBB cycle, N₂ fixation, and sulfur oxidation pathways were also significantly correlated with ARG abundance along the latitudinal gradient. These results highlight that FeS oxidation facilitated biogeochemical cycling and further exacerbated the enrichment and dissemination of ARGs. In addition, enrichments and disseminations of ARGs will be enhanced in the contexts of redox fluctuations, which potentially increase antibiotics risk in estuarine and coastal wetlands. The enrichment and dissemination of ARGs enhanced by biogeochemical cycling have great implications for antibiotic management in other relevant and global ecosystems. The findings and results of this study remain uncertain and limited. The microcosm incubation experiments are required to examine the effects of FeS concentration gradients, temperature conditions, and tidal cycle. In addition, the field-scale observations provide correlative evidence only, and in situ experiments are required to further validate the underlying mechanisms.

ACKNOWLEDGMENTS

This study was financially supported by the Natural Science Foundation of China (42422604, 42507400, and 42271101), the China Postdoctoral Science Foundation (grants GZB20250575 and 2025M782491), and the Jiangsu Provincial Postdoctoral Research Excellence Program (2025ZB164).

Contributor Information

Jun Yuan, Email: junyuan@njau.edu.cn.

Xiaofei Li, Email: xfli@sklec.ecnu.edu.cn.

Jennifer F. Biddle, University of Delaware, Lewes, Delaware, USA

DATA AVAILABILITY

The metagenomic sequencing reads generated in this study were deposited in the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1198723 and SRP341954.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/aem.01525-26.

Supplemental material. aem.01525-26-s0001.docx.

Table S1; Fig. S1 to S8.

aem.01525-26-s0001.docx^{(13.5MB, docx)}

DOI: 10.1128/aem.01525-26.SuF1

ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse.

REFERENCES

1. Pruden A, Pei R, Storteboom H, Carlson KH. 2006. Antibiotic resistance genes as emerging contaminants: studies in northern Colorado. Environ Sci Technol 40:7445–7450. doi: 10.1021/es060413l [DOI] [PubMed] [Google Scholar]
2. Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, Yu S, Chen YS, Zhang T, Gillings MR, Su JQ. 2017. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol 2:16270. doi: 10.1038/nmicrobiol.2016.270 [DOI] [PubMed] [Google Scholar]
3. Niu GQ, Zhu SY, Yuan XH, Yang WY, Ding ZX, Xie PH, Wen T, Shen QR, Yuan J. 2025. Stimulating the non-ARG harbored microbes with a specific carbon source can suppress the rebound of soil ARB after calcium peroxide application. Fundam Res 02:008. doi: 10.1016/j.fmre.2025.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Ying H, Zhang Y, Hu W, Wu W, Mao G. 2025. Glaciers as reservoirs of antibiotic resistance genes: hidden risks to human and ecosystem health in a warming world. Biocontaminant 1:e021. doi: 10.48130/biocontam-0025-0022 [DOI] [Google Scholar]
5. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, et al. 2020. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 48:D517–D525. doi: 10.1093/nar/gkz935 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Chen B, Liang X, Nie X, Huang X, Zou S, Li X. 2015. The role of class I integrons in the dissemination of sulfonamide resistance genes in the Pearl River and Pearl River Estuary, South China. J Hazard Mater 282:61–67. doi: 10.1016/j.jhazmat.2014.06.010 [DOI] [PubMed] [Google Scholar]
7. Zhao Z, Zhang Y, Liu R, Wang L, Xu H, Meng Q, Gu X, Tang L. 2023. Antibiotic resistance genes in constructed wetlands: driving indicators and risk assessment. J Hazard Mater 459:132314. doi: 10.1016/j.jhazmat.2023.132314 [DOI] [PubMed] [Google Scholar]
8. Liu H, Chen Y, Shi H, Huang X, Kim Y, Hou W, Yu J. 2025. Source apportionment and ecological risk of heavy metals in sediments from estuaries and bays in China: insights into cross-regional transport. Mar Pollut Bull 220:118420. doi: 10.1016/j.marpolbul.2025.118420 [DOI] [PubMed] [Google Scholar]
9. Chu Y, Dong X, Fang S, Gan L, Lee X, Zhou L. 2025. Viruses in human-impacted estuarine ecotones: distribution, metabolic potential, and environmental risks. Water Res 282:123750. doi: 10.1016/j.watres.2025.123750 [DOI] [PubMed] [Google Scholar]
10. Wang F, Xiong J, Lin L, Xu W, Liu L, Yang S, Cao W. 2025. Antibiotic resistance genes link to nitrogen removal potential via co-hosting preference for denitrification genes in a subtropical estuary. J Hazard Mater 498:139801. doi: 10.1016/j.jhazmat.2025.139801 [DOI] [PubMed] [Google Scholar]
11. Rickard D, Luther GWI. 2007. Chemistry of iron sulfides. Chem Rev 107:514–562. doi: 10.1021/cr0503658 [DOI] [PubMed] [Google Scholar]
12. Borch T, Kretzschmar R, Kappler A, Cappellen PV, Ginder-Vogel M, Voegelin A, Campbell K. 2010. Biogeochemical redox processes and their impact on contaminant dynamics. Environ Sci Technol 44:15–23. doi: 10.1021/es9026248 [DOI] [PubMed] [Google Scholar]
13. Mu C, Zhu C, Wang D, Huang D, Zhou D. 2025. Role of activated carbon in hydroxyl radical production and secondary mineral formation during iron sulfide (FeS) oxidation. Environ Sci Technol 59:16452–16462. doi: 10.1021/acs.est.5c06235 [DOI] [PubMed] [Google Scholar]
14. Li M, Wang Z, Feng Z, Lu J, Chen D, Chen C, He H, Zhang Q, Chen X, Morel JL, Baker AJM, Chao Y, Tang Y, Jiang F, Qiu R, Wang S. 2025. New insights into efficient iron sulfide oxidation for arsenic immobilization by microaerophilic and acidophilic Fe(II)-oxidizing bacteria under micro-oxygen and acidic conditions. J Hazard Mater 489:137695. doi: 10.1016/j.jhazmat.2025.137695 [DOI] [PubMed] [Google Scholar]
15. Chen N, Geng M, Huang D, Tan M, Li Z, Liu G, Zhu C, Fang G, Zhou D. 2022. Hydroxyl radical formation during oxygen-mediated oxidation of ferrous iron on mineral surface: dependence on mineral identity. J Hazard Mater 434:128861. doi: 10.1016/j.jhazmat.2022.128861 [DOI] [PubMed] [Google Scholar]
16. Zhang Y, Bian Z, Wang F, Peng Y, Xiao W, Zhang Q. 2025. In-situ synthesis of FeS nanoparticles enhances sulfamethoxazole degradation via accelerated electron transfer in anaerobic bacterial communities. Water Res 273:123025. doi: 10.1016/j.watres.2024.123025 [DOI] [PubMed] [Google Scholar]
17. Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MOA, Dantas G. 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337:1107–1111. doi: 10.1126/science.1220761 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, Dantas G. 2014. Bacterial phylogeny structures soil resistomes across habitats. Nature 509:612–616. doi: 10.1038/nature13377 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Liu K, Wang X, Li F, Tao J, Weng C-H, Hu Q, Kang D, Luo Z, Tang Z, Liu N, Qiu J. 2025. Metagenomic characterization of antibiotic resistance genes and human bacterial pathogens in groundwater from agricultural, landfill, and hazardous waste disposal sites (HWDS) sources: drivers, risks, and source tracking. J Hazard Mater 498:139803. doi: 10.1016/j.jhazmat.2025.139803 [DOI] [PubMed] [Google Scholar]
20. Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM, Zhang T. 2015. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J 9:2490–2502. doi: 10.1038/ismej.2015.59 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Liao X, Hou L, Zhang L, Grossart HP, Liu K, Liu J, Chen Y, Liu Y, Hu A. 2024. Distinct influences of altitude on microbiome and antibiotic resistome assembly in a glacial river ecosystem of Mount Everest. J Hazard Mater 479:135675. doi: 10.1016/j.jhazmat.2024.135675 [DOI] [PubMed] [Google Scholar]
22. Lund D, Parras-Moltó M, Inda-Díaz JS, Ebmeyer S, Larsson DGJ, Johnning A, Kristiansson E. 2025. Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes. Nat Commun 16:2595. doi: 10.1038/s41467-025-57825-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Couture RM, Gobeil C, Tessier A. 2010. Arsenic, iron and sulfur co-diagenesis in lake sediments. Geochim Cosmochim Acta 74:1238–1255. doi: 10.1016/j.gca.2009.11.028 [DOI] [Google Scholar]
24. Liu X, Li P, Bao K, Wang Y, Wang H, Wang Y, Jiang Z, Yang Y, Yuan S, Kappler A, Wang Y. 2025. Synergistic interaction between microbial nitrogen fixation and iron reduction in the environment. ISME J 19:wraf212. doi: 10.1093/ismejo/wraf212 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Zhu YG, Zhao Y, Zhu D, Gillings M, Penuelas J, Ok YS, Capon A, Banwart S. 2019. Soil biota, antimicrobial resistance and planetary health. Environ Int 131:105059. doi: 10.1016/j.envint.2019.105059 [DOI] [PubMed] [Google Scholar]
26. Wu B, Tang X, Song Z, Qi M, Liu M, Li X, Hou L. 2025. Chemoautotrophic carbon fixation favors iron-bound organic carbon formation in estuarine and coastal sediments. Environ Sci Technol 59:12642–12655. doi: 10.1021/acs.est.5c00324 [DOI] [PubMed] [Google Scholar]
27. Li XF, Hou LJ, Liu M, Lin XB, Li Y, Li SW. 2015. Primary effects of extracellular enzyme activity and microbial community on carbon and nitrogen mineralization in estuarine and tidal wetlands. Appl Microbiol Biotechnol 99:2895–2909. doi: 10.1007/s00253-014-6187-4 [DOI] [PubMed] [Google Scholar]
28. Vance ED, Brookes PC, Jenkinson DS. 1987. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem 19:703–707. doi: 10.1016/0038-0717(87)90052-6 [DOI] [Google Scholar]
29. Brookes PC, Landman A, Pruden G, Jenkinson DS. 1985. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem 17:837–842. doi: 10.1016/0038-0717(85)90144-0 [DOI] [Google Scholar]
30. Deng FY, Hou LJ, Liu M, Zheng YL, Yin GY, Li XF, Lin XB, Chen F, Gao J, Jiang XF. 2015. Dissimilatory nitrate reduction processes and associated contribution to nitrogen removal in sediments of the Yangtze Estuary. J Geophys Res 120:1521–1531. doi: 10.1002/2015JG003007 [DOI] [Google Scholar]
31. Lovley DR, Phillips EJ. 1987. Rapid assay for microbially reducible ferric iron in aquatic sediments. Appl Environ Microbiol 53:1536–1540. doi: 10.1128/aem.53.7.1536-1540.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Brown CL, Mullet J, Hindi F, Stoll JE, Gupta S, Choi M, Keenum I, Vikesland P, Pruden A, Zhang L. 2022. mobileOG-db: a manually curated database of protein families mediating the life cycle of bacterial mobile genetic elements. Appl Environ Microbiol 88:e0099122. doi: 10.1128/aem.00991-22 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Wagner GP, Kin K, Lynch VJ. 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:281–285. doi: 10.1007/s12064-012-0162-3 [DOI] [PubMed] [Google Scholar]
34. Tully BJ, Graham ED, Heidelberg JF. 2018. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci Data 5:170203. doi: 10.1038/sdata.2017.203 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359. doi: 10.7717/peerj.7359 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–607. doi: 10.1093/bioinformatics/btv638 [DOI] [PubMed] [Google Scholar]
37. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3:836–843. doi: 10.1038/s41564-018-0171-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Chklovski A, Parks DH, Woodcroft BJ, Tyson GW. 2023. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20:1203–1212. doi: 10.1038/s41592-023-01940-w [DOI] [PubMed] [Google Scholar]
39. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2019. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36:1925–1927. doi: 10.1093/bioinformatics/btz848 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Zheng D, Yin G, Liu M, Hou L, Yang Y, Liu X, Jiang Y, Chen C, Wu H. 2022. Metagenomics highlights the impact of climate and human activities on antibiotic resistance genes in China’s estuaries. Environ Pollut 301:119015. doi: 10.1016/j.envpol.2022.119015 [DOI] [PubMed] [Google Scholar]
41. Zhang H, Quan H, Song S, Sun L, Lu H. 2023. Comprehensive assessment of toxicity and environmental risk associated with sulfamethoxazole biodegradation in sulfur-mediated biological wastewater treatment. Water Res 246:120753. doi: 10.1016/j.watres.2023.120753 [DOI] [PubMed] [Google Scholar]
42. Li Y, Zhang K, Zhang H, Li M, Ma L, Liu Y, Feng F, Jiang W, Ge J, Chen J, Yu X. 2025. Organic pollutant-induced long-distance ROS signaling drives plant systemic acquired acclimation via rhizomicrobiota. Nat Commun 16:9077. doi: 10.1038/s41467-025-64138-y [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Wu H, Shen J, Zhang H, Fang Q, Zhu T, Yuan J, Shen Q, Xue C. 2026. Fusarium oxysporum f. sp. niveum invasion promotes Pseudomonas-driven antibiotic resistance gene enrichment. J Hazard Mater 503:141084. doi: 10.1016/j.jhazmat.2026.141084 [DOI] [PubMed] [Google Scholar]
44. Li X-Y, Zhu C-W, Chen S, Xiang Q, Lu C, Lin X-Y, Chen Q-L. 2024. Elevated CO₂ increased antibiotic resistomes in seed endophytes: evidence from a free-air CO₂ enrichment (FACE) experiment. Environ Sci Technol 58:23190–23200. doi: 10.1021/acs.est.4c09625 [DOI] [PubMed] [Google Scholar]
45. Ke Y, Sun W, Xue Y, Zhu Y, Yan S, Xie S. 2024. Effects of treatments and distribution on microbiome and antibiotic resistome from source to tap water in three Chinese geographical regions based on metagenome assembly. Water Res 249:120894. doi: 10.1016/j.watres.2023.120894 [DOI] [PubMed] [Google Scholar]
46. Wang X, Kang L, Li T, Wang S, Ma H, Tian Y, Sun J, Li J. 2025. Pyrite-mediated typical antibiotics degradation in estuarine sediments: the role of sulfur species under aerobic and anaerobic conditions. Environ Sci Technol 59:15931–15943. doi: 10.1021/acs.est.5c01739 [DOI] [PubMed] [Google Scholar]
47. Zhao Y, Cocerva T, Cox S, Tardif S, Su JQ, Zhu YG, Brandt KK. 2019. Evidence for co-selection of antibiotic resistance genes and mobile genetic elements in metal polluted urban soils. Sci Total Environ 656:512–520. doi: 10.1016/j.scitotenv.2018.11.372 [DOI] [PubMed] [Google Scholar]
48. Zhang Y, Dong K, Liu T, Shi L, Ren M, Li C. 2025. Abundance changes and transfer mechanism of antibiotic resistance genes in water under sub-lethal disinfection. Water-Energy Nexus 8:18–30. doi: 10.1016/j.wen.2025.02.001 [DOI] [Google Scholar]
49. Ren Z, Yang B, Petropoulos E, Liu H, Hou P, He S, Ma X, Zhang J, Xue L, Yang L. 2024. Under what circumstances can chemical oxidation processes effectively degrade antibiotics resistance genes? Evidence from meta-analysis. J Environ Chem Eng 12:113385. doi: 10.1016/j.jece.2024.113385 [DOI] [Google Scholar]
50. Jiang Y, Ran J, Mao K, Yang X, Zhong L, Yang C, Feng X, Zhang H. 2022. Recent progress in Fenton/Fenton-like reactions for the removal of antibiotics in aqueous environments. Ecotoxicol Environ Saf 236:113464. doi: 10.1016/j.ecoenv.2022.113464 [DOI] [PubMed] [Google Scholar]
51. Pan Z, Jiang X, Feng X, Liu Y, Dong W, Chen Y, Li C, Yang B, Hou J, Zhang J, Zhu L, Lin D, Xu J. 2025. Controllable supply-demand effect during superior Fe single-atom catalyst synthesis for targeted guanine oxidation of antibiotic resistance genes. Environ Sci Technol 59:5382–5393. doi: 10.1021/acs.est.4c13667 [DOI] [PubMed] [Google Scholar]
52. Zhai K, Yin K, Lin Y, Chen S, Bi Y, Xing R, Ren C, Chen Z, Yu Z, Chen Z, Zhou S. 2025. Free radicals on aging microplastics regulated the prevalence of antibiotic resistance genes in the aquatic environment: new insight into the effect of microplastics on the spreading of biofilm resistomes. Environ Sci Technol 59:11735–11744. doi: 10.1021/acs.est.4c12699 [DOI] [PubMed] [Google Scholar]
53. Pester M, Knorr K-H, Friedrich MW, Wagner M, Loy A. 2012. Sulfate-reducing microorganisms in wetlands - fameless actors in carbon cycling and climate change. Front Microbiol 3:72. doi: 10.3389/fmicb.2012.00072 [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Gao FZ, Hu LX, Liu YS, Yang HY, He LY, Bai H, Liu F, Jin XW, Ying GG. 2025. Unveiling the prevalence of metal resistance genes and their associations with antibiotic resistance genes in heavy metal-contaminated rivers. Water Res 281:123699. doi: 10.1016/j.watres.2025.123699 [DOI] [PubMed] [Google Scholar]
55. Oña L, Shreekar SK, Kost C. 2025. Disentangling microbial interaction networks. Trends Microbiol 33:619–634. doi: 10.1016/j.tim.2025.01.013 [DOI] [PubMed] [Google Scholar]
56. Siles JA, Starke R, Martinovic T, Parente Fernandes ML, Orgiazzi A, Bastida F. 2022. Distribution of phosphorus cycling genes across land uses and microbial taxonomic groups based on metagenome and genome mining. Soil Biol Biochem 174:108826. doi: 10.1016/j.soilbio.2022.108826 [DOI] [Google Scholar]
57. Li Y, Gao Y, Chen W, Zhang W, Lu X. 2024. Shifts in bacterial diversity, interactions and microbial elemental cycling genes under cadmium contamination in paddy soil: Implications for altered ecological function. J Hazard Mater 461:132544. doi: 10.1016/j.jhazmat.2023.132544 [DOI] [PubMed] [Google Scholar]
58. Falkowski PG, Fenchel T, Delong EF. 2008. The microbial engines that drive Earth’s biogeochemical cycles. Science 320:1034–1039. doi: 10.1126/science.1153213 [DOI] [PubMed] [Google Scholar]
59. Kuypers MMM, Marchant HK, Kartal B. 2018. The microbial nitrogen-cycling network. Nat Rev Microbiol 16:263–276. doi: 10.1038/nrmicro.2018.9 [DOI] [PubMed] [Google Scholar]
60. Zheng C, Song J, Shan M, Qiu M, Cui M, Huang C, Chen W, Wang J, Zhang L, Yu Y, Fang H. 2026. Nutrition cycling microbiomes drive the succession of antibiotic resistome in long-term manured soils. J Adv Res 84:29–41. doi: 10.1016/j.jare.2025.09.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Zhang Q, Xu N, Lei C, Chen B, Wang T, Ma Y, Lu T, Penuelas J, Gillings M, Zhu Y, Fu Z, Qian H. 2023. Metagenomic insight into the global dissemination of the antibiotic resistome. Adv Sci 10:e2303925. doi: 10.1002/advs.202303925 [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Zou Y, Chen W, Wang L, Li Y, Wang Z, Tao R. 2025. Microbial metabolic functions, rather than taxonomic composition, predominantly shape the distribution of antibiotic resistance genes in an effluent-impacted hyporheic zone. Water Res 287:124455. doi: 10.1016/j.watres.2025.124455 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental material. aem.01525-26-s0001.docx.

Table S1; Fig. S1 to S8.

aem.01525-26-s0001.docx^{(13.5MB, docx)}

DOI: 10.1128/aem.01525-26.SuF1

Data Availability Statement

The metagenomic sequencing reads generated in this study were deposited in the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1198723 and SRP341954.

[B1] 1. Pruden A, Pei R, Storteboom H, Carlson KH. 2006. Antibiotic resistance genes as emerging contaminants: studies in northern Colorado. Environ Sci Technol 40:7445–7450. doi: 10.1021/es060413l [DOI] [PubMed] [Google Scholar]

[B2] 2. Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, Yu S, Chen YS, Zhang T, Gillings MR, Su JQ. 2017. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol 2:16270. doi: 10.1038/nmicrobiol.2016.270 [DOI] [PubMed] [Google Scholar]

[B3] 3. Niu GQ, Zhu SY, Yuan XH, Yang WY, Ding ZX, Xie PH, Wen T, Shen QR, Yuan J. 2025. Stimulating the non-ARG harbored microbes with a specific carbon source can suppress the rebound of soil ARB after calcium peroxide application. Fundam Res 02:008. doi: 10.1016/j.fmre.2025.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B4] 4. Ying H, Zhang Y, Hu W, Wu W, Mao G. 2025. Glaciers as reservoirs of antibiotic resistance genes: hidden risks to human and ecosystem health in a warming world. Biocontaminant 1:e021. doi: 10.48130/biocontam-0025-0022 [DOI] [Google Scholar]

[B5] 5. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen A-LV, Cheng AA, Liu S, et al. 2020. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 48:D517–D525. doi: 10.1093/nar/gkz935 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B6] 6. Chen B, Liang X, Nie X, Huang X, Zou S, Li X. 2015. The role of class I integrons in the dissemination of sulfonamide resistance genes in the Pearl River and Pearl River Estuary, South China. J Hazard Mater 282:61–67. doi: 10.1016/j.jhazmat.2014.06.010 [DOI] [PubMed] [Google Scholar]

[B7] 7. Zhao Z, Zhang Y, Liu R, Wang L, Xu H, Meng Q, Gu X, Tang L. 2023. Antibiotic resistance genes in constructed wetlands: driving indicators and risk assessment. J Hazard Mater 459:132314. doi: 10.1016/j.jhazmat.2023.132314 [DOI] [PubMed] [Google Scholar]

[B8] 8. Liu H, Chen Y, Shi H, Huang X, Kim Y, Hou W, Yu J. 2025. Source apportionment and ecological risk of heavy metals in sediments from estuaries and bays in China: insights into cross-regional transport. Mar Pollut Bull 220:118420. doi: 10.1016/j.marpolbul.2025.118420 [DOI] [PubMed] [Google Scholar]

[B9] 9. Chu Y, Dong X, Fang S, Gan L, Lee X, Zhou L. 2025. Viruses in human-impacted estuarine ecotones: distribution, metabolic potential, and environmental risks. Water Res 282:123750. doi: 10.1016/j.watres.2025.123750 [DOI] [PubMed] [Google Scholar]

[B10] 10. Wang F, Xiong J, Lin L, Xu W, Liu L, Yang S, Cao W. 2025. Antibiotic resistance genes link to nitrogen removal potential via co-hosting preference for denitrification genes in a subtropical estuary. J Hazard Mater 498:139801. doi: 10.1016/j.jhazmat.2025.139801 [DOI] [PubMed] [Google Scholar]

[B11] 11. Rickard D, Luther GWI. 2007. Chemistry of iron sulfides. Chem Rev 107:514–562. doi: 10.1021/cr0503658 [DOI] [PubMed] [Google Scholar]

[B12] 12. Borch T, Kretzschmar R, Kappler A, Cappellen PV, Ginder-Vogel M, Voegelin A, Campbell K. 2010. Biogeochemical redox processes and their impact on contaminant dynamics. Environ Sci Technol 44:15–23. doi: 10.1021/es9026248 [DOI] [PubMed] [Google Scholar]

[B13] 13. Mu C, Zhu C, Wang D, Huang D, Zhou D. 2025. Role of activated carbon in hydroxyl radical production and secondary mineral formation during iron sulfide (FeS) oxidation. Environ Sci Technol 59:16452–16462. doi: 10.1021/acs.est.5c06235 [DOI] [PubMed] [Google Scholar]

[B14] 14. Li M, Wang Z, Feng Z, Lu J, Chen D, Chen C, He H, Zhang Q, Chen X, Morel JL, Baker AJM, Chao Y, Tang Y, Jiang F, Qiu R, Wang S. 2025. New insights into efficient iron sulfide oxidation for arsenic immobilization by microaerophilic and acidophilic Fe(II)-oxidizing bacteria under micro-oxygen and acidic conditions. J Hazard Mater 489:137695. doi: 10.1016/j.jhazmat.2025.137695 [DOI] [PubMed] [Google Scholar]

[B15] 15. Chen N, Geng M, Huang D, Tan M, Li Z, Liu G, Zhu C, Fang G, Zhou D. 2022. Hydroxyl radical formation during oxygen-mediated oxidation of ferrous iron on mineral surface: dependence on mineral identity. J Hazard Mater 434:128861. doi: 10.1016/j.jhazmat.2022.128861 [DOI] [PubMed] [Google Scholar]

[B16] 16. Zhang Y, Bian Z, Wang F, Peng Y, Xiao W, Zhang Q. 2025. In-situ synthesis of FeS nanoparticles enhances sulfamethoxazole degradation via accelerated electron transfer in anaerobic bacterial communities. Water Res 273:123025. doi: 10.1016/j.watres.2024.123025 [DOI] [PubMed] [Google Scholar]

[B17] 17. Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MOA, Dantas G. 2012. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337:1107–1111. doi: 10.1126/science.1220761 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B18] 18. Forsberg KJ, Patel S, Gibson MK, Lauber CL, Knight R, Fierer N, Dantas G. 2014. Bacterial phylogeny structures soil resistomes across habitats. Nature 509:612–616. doi: 10.1038/nature13377 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B19] 19. Liu K, Wang X, Li F, Tao J, Weng C-H, Hu Q, Kang D, Luo Z, Tang Z, Liu N, Qiu J. 2025. Metagenomic characterization of antibiotic resistance genes and human bacterial pathogens in groundwater from agricultural, landfill, and hazardous waste disposal sites (HWDS) sources: drivers, risks, and source tracking. J Hazard Mater 498:139803. doi: 10.1016/j.jhazmat.2025.139803 [DOI] [PubMed] [Google Scholar]

[B20] 20. Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM, Zhang T. 2015. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J 9:2490–2502. doi: 10.1038/ismej.2015.59 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B21] 21. Liao X, Hou L, Zhang L, Grossart HP, Liu K, Liu J, Chen Y, Liu Y, Hu A. 2024. Distinct influences of altitude on microbiome and antibiotic resistome assembly in a glacial river ecosystem of Mount Everest. J Hazard Mater 479:135675. doi: 10.1016/j.jhazmat.2024.135675 [DOI] [PubMed] [Google Scholar]

[B22] 22. Lund D, Parras-Moltó M, Inda-Díaz JS, Ebmeyer S, Larsson DGJ, Johnning A, Kristiansson E. 2025. Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes. Nat Commun 16:2595. doi: 10.1038/s41467-025-57825-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B23] 23. Couture RM, Gobeil C, Tessier A. 2010. Arsenic, iron and sulfur co-diagenesis in lake sediments. Geochim Cosmochim Acta 74:1238–1255. doi: 10.1016/j.gca.2009.11.028 [DOI] [Google Scholar]

[B24] 24. Liu X, Li P, Bao K, Wang Y, Wang H, Wang Y, Jiang Z, Yang Y, Yuan S, Kappler A, Wang Y. 2025. Synergistic interaction between microbial nitrogen fixation and iron reduction in the environment. ISME J 19:wraf212. doi: 10.1093/ismejo/wraf212 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B25] 25. Zhu YG, Zhao Y, Zhu D, Gillings M, Penuelas J, Ok YS, Capon A, Banwart S. 2019. Soil biota, antimicrobial resistance and planetary health. Environ Int 131:105059. doi: 10.1016/j.envint.2019.105059 [DOI] [PubMed] [Google Scholar]

[B26] 26. Wu B, Tang X, Song Z, Qi M, Liu M, Li X, Hou L. 2025. Chemoautotrophic carbon fixation favors iron-bound organic carbon formation in estuarine and coastal sediments. Environ Sci Technol 59:12642–12655. doi: 10.1021/acs.est.5c00324 [DOI] [PubMed] [Google Scholar]

[B27] 27. Li XF, Hou LJ, Liu M, Lin XB, Li Y, Li SW. 2015. Primary effects of extracellular enzyme activity and microbial community on carbon and nitrogen mineralization in estuarine and tidal wetlands. Appl Microbiol Biotechnol 99:2895–2909. doi: 10.1007/s00253-014-6187-4 [DOI] [PubMed] [Google Scholar]

[B28] 28. Vance ED, Brookes PC, Jenkinson DS. 1987. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem 19:703–707. doi: 10.1016/0038-0717(87)90052-6 [DOI] [Google Scholar]

[B29] 29. Brookes PC, Landman A, Pruden G, Jenkinson DS. 1985. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem 17:837–842. doi: 10.1016/0038-0717(85)90144-0 [DOI] [Google Scholar]

[B30] 30. Deng FY, Hou LJ, Liu M, Zheng YL, Yin GY, Li XF, Lin XB, Chen F, Gao J, Jiang XF. 2015. Dissimilatory nitrate reduction processes and associated contribution to nitrogen removal in sediments of the Yangtze Estuary. J Geophys Res 120:1521–1531. doi: 10.1002/2015JG003007 [DOI] [Google Scholar]

[B31] 31. Lovley DR, Phillips EJ. 1987. Rapid assay for microbially reducible ferric iron in aquatic sediments. Appl Environ Microbiol 53:1536–1540. doi: 10.1128/aem.53.7.1536-1540.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B32] 32. Brown CL, Mullet J, Hindi F, Stoll JE, Gupta S, Choi M, Keenum I, Vikesland P, Pruden A, Zhang L. 2022. mobileOG-db: a manually curated database of protein families mediating the life cycle of bacterial mobile genetic elements. Appl Environ Microbiol 88:e0099122. doi: 10.1128/aem.00991-22 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B33] 33. Wagner GP, Kin K, Lynch VJ. 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:281–285. doi: 10.1007/s12064-012-0162-3 [DOI] [PubMed] [Google Scholar]

[B34] 34. Tully BJ, Graham ED, Heidelberg JF. 2018. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci Data 5:170203. doi: 10.1038/sdata.2017.203 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B35] 35. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359. doi: 10.7717/peerj.7359 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] 36. Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–607. doi: 10.1093/bioinformatics/btv638 [DOI] [PubMed] [Google Scholar]

[B37] 37. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, Banfield JF. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3:836–843. doi: 10.1038/s41564-018-0171-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] 38. Chklovski A, Parks DH, Woodcroft BJ, Tyson GW. 2023. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20:1203–1212. doi: 10.1038/s41592-023-01940-w [DOI] [PubMed] [Google Scholar]

[B39] 39. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2019. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36:1925–1927. doi: 10.1093/bioinformatics/btz848 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B40] 40. Zheng D, Yin G, Liu M, Hou L, Yang Y, Liu X, Jiang Y, Chen C, Wu H. 2022. Metagenomics highlights the impact of climate and human activities on antibiotic resistance genes in China’s estuaries. Environ Pollut 301:119015. doi: 10.1016/j.envpol.2022.119015 [DOI] [PubMed] [Google Scholar]

[B41] 41. Zhang H, Quan H, Song S, Sun L, Lu H. 2023. Comprehensive assessment of toxicity and environmental risk associated with sulfamethoxazole biodegradation in sulfur-mediated biological wastewater treatment. Water Res 246:120753. doi: 10.1016/j.watres.2023.120753 [DOI] [PubMed] [Google Scholar]

[B42] 42. Li Y, Zhang K, Zhang H, Li M, Ma L, Liu Y, Feng F, Jiang W, Ge J, Chen J, Yu X. 2025. Organic pollutant-induced long-distance ROS signaling drives plant systemic acquired acclimation via rhizomicrobiota. Nat Commun 16:9077. doi: 10.1038/s41467-025-64138-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[B43] 43. Wu H, Shen J, Zhang H, Fang Q, Zhu T, Yuan J, Shen Q, Xue C. 2026. Fusarium oxysporum f. sp. niveum invasion promotes Pseudomonas-driven antibiotic resistance gene enrichment. J Hazard Mater 503:141084. doi: 10.1016/j.jhazmat.2026.141084 [DOI] [PubMed] [Google Scholar]

[B44] 44. Li X-Y, Zhu C-W, Chen S, Xiang Q, Lu C, Lin X-Y, Chen Q-L. 2024. Elevated CO₂ increased antibiotic resistomes in seed endophytes: evidence from a free-air CO₂ enrichment (FACE) experiment. Environ Sci Technol 58:23190–23200. doi: 10.1021/acs.est.4c09625 [DOI] [PubMed] [Google Scholar]

[B45] 45. Ke Y, Sun W, Xue Y, Zhu Y, Yan S, Xie S. 2024. Effects of treatments and distribution on microbiome and antibiotic resistome from source to tap water in three Chinese geographical regions based on metagenome assembly. Water Res 249:120894. doi: 10.1016/j.watres.2023.120894 [DOI] [PubMed] [Google Scholar]

[B46] 46. Wang X, Kang L, Li T, Wang S, Ma H, Tian Y, Sun J, Li J. 2025. Pyrite-mediated typical antibiotics degradation in estuarine sediments: the role of sulfur species under aerobic and anaerobic conditions. Environ Sci Technol 59:15931–15943. doi: 10.1021/acs.est.5c01739 [DOI] [PubMed] [Google Scholar]

[B47] 47. Zhao Y, Cocerva T, Cox S, Tardif S, Su JQ, Zhu YG, Brandt KK. 2019. Evidence for co-selection of antibiotic resistance genes and mobile genetic elements in metal polluted urban soils. Sci Total Environ 656:512–520. doi: 10.1016/j.scitotenv.2018.11.372 [DOI] [PubMed] [Google Scholar]

[B48] 48. Zhang Y, Dong K, Liu T, Shi L, Ren M, Li C. 2025. Abundance changes and transfer mechanism of antibiotic resistance genes in water under sub-lethal disinfection. Water-Energy Nexus 8:18–30. doi: 10.1016/j.wen.2025.02.001 [DOI] [Google Scholar]

[B49] 49. Ren Z, Yang B, Petropoulos E, Liu H, Hou P, He S, Ma X, Zhang J, Xue L, Yang L. 2024. Under what circumstances can chemical oxidation processes effectively degrade antibiotics resistance genes? Evidence from meta-analysis. J Environ Chem Eng 12:113385. doi: 10.1016/j.jece.2024.113385 [DOI] [Google Scholar]

[B50] 50. Jiang Y, Ran J, Mao K, Yang X, Zhong L, Yang C, Feng X, Zhang H. 2022. Recent progress in Fenton/Fenton-like reactions for the removal of antibiotics in aqueous environments. Ecotoxicol Environ Saf 236:113464. doi: 10.1016/j.ecoenv.2022.113464 [DOI] [PubMed] [Google Scholar]

[B51] 51. Pan Z, Jiang X, Feng X, Liu Y, Dong W, Chen Y, Li C, Yang B, Hou J, Zhang J, Zhu L, Lin D, Xu J. 2025. Controllable supply-demand effect during superior Fe single-atom catalyst synthesis for targeted guanine oxidation of antibiotic resistance genes. Environ Sci Technol 59:5382–5393. doi: 10.1021/acs.est.4c13667 [DOI] [PubMed] [Google Scholar]

[B52] 52. Zhai K, Yin K, Lin Y, Chen S, Bi Y, Xing R, Ren C, Chen Z, Yu Z, Chen Z, Zhou S. 2025. Free radicals on aging microplastics regulated the prevalence of antibiotic resistance genes in the aquatic environment: new insight into the effect of microplastics on the spreading of biofilm resistomes. Environ Sci Technol 59:11735–11744. doi: 10.1021/acs.est.4c12699 [DOI] [PubMed] [Google Scholar]

[B53] 53. Pester M, Knorr K-H, Friedrich MW, Wagner M, Loy A. 2012. Sulfate-reducing microorganisms in wetlands - fameless actors in carbon cycling and climate change. Front Microbiol 3:72. doi: 10.3389/fmicb.2012.00072 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B54] 54. Gao FZ, Hu LX, Liu YS, Yang HY, He LY, Bai H, Liu F, Jin XW, Ying GG. 2025. Unveiling the prevalence of metal resistance genes and their associations with antibiotic resistance genes in heavy metal-contaminated rivers. Water Res 281:123699. doi: 10.1016/j.watres.2025.123699 [DOI] [PubMed] [Google Scholar]

[B55] 55. Oña L, Shreekar SK, Kost C. 2025. Disentangling microbial interaction networks. Trends Microbiol 33:619–634. doi: 10.1016/j.tim.2025.01.013 [DOI] [PubMed] [Google Scholar]

[B56] 56. Siles JA, Starke R, Martinovic T, Parente Fernandes ML, Orgiazzi A, Bastida F. 2022. Distribution of phosphorus cycling genes across land uses and microbial taxonomic groups based on metagenome and genome mining. Soil Biol Biochem 174:108826. doi: 10.1016/j.soilbio.2022.108826 [DOI] [Google Scholar]

[B57] 57. Li Y, Gao Y, Chen W, Zhang W, Lu X. 2024. Shifts in bacterial diversity, interactions and microbial elemental cycling genes under cadmium contamination in paddy soil: Implications for altered ecological function. J Hazard Mater 461:132544. doi: 10.1016/j.jhazmat.2023.132544 [DOI] [PubMed] [Google Scholar]

[B58] 58. Falkowski PG, Fenchel T, Delong EF. 2008. The microbial engines that drive Earth’s biogeochemical cycles. Science 320:1034–1039. doi: 10.1126/science.1153213 [DOI] [PubMed] [Google Scholar]

[B59] 59. Kuypers MMM, Marchant HK, Kartal B. 2018. The microbial nitrogen-cycling network. Nat Rev Microbiol 16:263–276. doi: 10.1038/nrmicro.2018.9 [DOI] [PubMed] [Google Scholar]

[B60] 60. Zheng C, Song J, Shan M, Qiu M, Cui M, Huang C, Chen W, Wang J, Zhang L, Yu Y, Fang H. 2026. Nutrition cycling microbiomes drive the succession of antibiotic resistome in long-term manured soils. J Adv Res 84:29–41. doi: 10.1016/j.jare.2025.09.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B61] 61. Zhang Q, Xu N, Lei C, Chen B, Wang T, Ma Y, Lu T, Penuelas J, Gillings M, Zhu Y, Fu Z, Qian H. 2023. Metagenomic insight into the global dissemination of the antibiotic resistome. Adv Sci 10:e2303925. doi: 10.1002/advs.202303925 [DOI] [PMC free article] [PubMed] [Google Scholar]

[B62] 62. Zou Y, Chen W, Wang L, Li Y, Wang Z, Tao R. 2025. Microbial metabolic functions, rather than taxonomic composition, predominantly shape the distribution of antibiotic resistance genes in an effluent-impacted hyporheic zone. Water Res 287:124455. doi: 10.1016/j.watres.2025.124455 [DOI] [PubMed] [Google Scholar]

PERMALINK

Double-edged sword of FeS oxidation in estuarine and coastal sediments: accelerating elemental cycling yet enhancing antibiotic resistance enrichment

Wei Qiu

Xiufeng Tang

Boshuang Wu

Haoran Huang

Xiangyang Zhao

Jun Yuan

Xiaofei Li

Roles

ABSTRACT

IMPORTANCE

INTRODUCTION

MATERIALS AND METHODS

Study area and sample collections

Experimental incubation

Determination of sediment properties

Gene catalog construction and ARG/MGE functional annotation

DNA extraction, metagenomic sequencing, assembly, and genome binning

Statistical analysis

RESULTS

Changes of ARGs and microbial communities

Fig 1.

Fig 2.

MAGs and elemental metabolic potential

Fig 3.

Metabolic pathways of ARGs enrichment

Fig 4.

Fig 5.

Sediment factors mediate ARGs

Fig 6.

DISCUSSION

FeS oxidation facilitates ARGs enrichment

Microbial community restructure favored ARGs hosts

Element metabolism-driven ARGs enrichment

Environmental implications: from laboratory to field scales

Fig 7.

Conclusion

ACKNOWLEDGMENTS

Contributor Information

DATA AVAILABILITY

SUPPLEMENTAL MATERIAL

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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