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. 2025 Oct 24;4(3):420–433. doi: 10.1021/envhealth.5c00278

Resistance Gene Dynamics, Biogeochemical Coupling, and Ecological Risks in Sediments of Anthropogenically Impacted Lake Wetlands in China

Chengzhi Mao †, Yunfeng Wang ‡, Xinghao Li §, Qiaoyi Kong †, Saleh A Al-Farraj ∥, Elvis Genbo Xu ⊥, Hans-Peter Grossart ▲,, Jie Huang †,*, Weibo Song ∇,●
PMCID: PMC13010296  PMID: 41883376

Abstract

Antibiotic resistance is a growing global threat to both public health and ecosystem stability. While the “One Health” framework emphasizes the need to monitor antibiotic resistance genes (ARGs) across diverse environments worldwide, the risks posed by ARGs in lakes affected by human activities, particularly in lake sediments that serve as natural reservoirs of ARGs, remain poorly understood. Metagenomics enables culture-independent analysis of microbial communities and resistance genes, providing essential insights into ARG dynamics. This study investigates microbial communities, ARGs, metal resistance genes (MRGs), and mobile genetic elements (MGEs) in sediments from Lake Donghu and Lake Weishan in China, two contrasting lake ecosystems subject to urbanization and agricultural activities for over four decades, using high-throughput metagenomic sequencing and assembly. ARGs and MRGs were more strongly influenced by deterministic environmental factors, particularly heavy metals (Cd, Pb, Cu), whereas microbial community structures were predominantly shaped by stochastic processes. Metagenomic binning yielded 293 metagenome-assembled genomes (MAGs), 125 of which were identified as potential ARG hosts, with Proteobacteria and Desulfobacterota being the most common. These hosts frequently cocarried MGEs, virulence factor genes (VFGs), and MRGs and exhibited metabolic pathways linked to carbon, nitrogen, and greenhouse gas (CO2 and N2O) cycling. Dissolved organic carbon (DOC) was determined as a key factor influencing microbial metabolism and promoting resistance gene dissemination. Our findings highlight a tight coupling between ARG dissemination, microbial ecological functions, and biogeochemical processes, underscoring ecosystem-level risks associated with resistance proliferation in human-impacted wetlands of China and elsewhere.

Keywords: Bacterial diversity, Sediment samples, Metagenomics, Antibiotic resistance genes, Anthropogenic impact, Environmental and health risks

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1. Introduction

Since the advent of antibiotics, their excessive and often inappropriate use in clinical, agricultural, and aquaculture environments worldwide has significantly accelerated the emergence of antibiotic-resistant pathogens (ARPs) and antibiotic resistance genes (ARGs). , Environmental pressures, such as high levels of antibiotics and heavy metals following anthropogenic pollution, contribute to the increased abundance of ARPs and resistance genes. , Heavy metals, in particular, not only enhance resistance to metal resistance genes (MRGs) but also promote the coselection and transfer of ARGs, facilitating their dissemination across microbial communities. − Research has shown that copper can accelerate the conjugative transfer of ARGs, resulting in a higher frequency of ARG exchange among microorganisms. The proliferation of ARGs and MRGs increases the likelihood of their spread to ARPs through horizontal gene transfer. Mobile genetic elements (MGEs), including integrons, plasmids, and transposons, play critical roles in disseminating ARGs and promoting antibiotic resistance. These MGEs act as vehicles for transferring resistance genes between different bacterial species, contributing to the spread of antibiotic resistance in the environment. , Horizontal gene transfer of ARGs from environmental reservoirs to human pathogens has led to their recognition as emerging environmental pollutants. Recognizing this, the United Nations Environment Programme has identified antimicrobial resistance as a primary emerging environmental issue. Metagenomics provides a culture-independent approach to directly recover genetic material from environmental samples, offering comprehensive insights into microbial diversity and resistance gene profiles that are essential for understanding the environmental dimension of antimicrobial resistance. −

Lakes are critical for drinking water supply and support essential ecosystem services such as agriculture, aquaculture, and tourism by providing clean drinking water, irrigation resources, and recreational benefits. However, rapid industrialization and urbanization, in rapidly developing countries such as China, have led to the discharge of various pollutants, including wastewater from hospitals, aquaculture farms, and sewage treatment plants into lakes. Consequently, lakes worldwide are under increasing pressure from environmental pollution, including antibiotics and metals, and thus ARGs. There is growing recognition that lakes and rivers are not only reservoirs of ARGs but also potential transmission pathways for ARPs. , Compared to rivers, the relatively slow circulation rate of pollutants in lakes enhances the likelihood of spreading antibiotic resistance to humans. Studies have shown that some microorganisms can acquire ARGs to tolerate environmental stress, and once transferred to humans, these microorganisms can significantly impact healthcare.

Recently, increasing numbers of researchers are focusing on the occurrence and distribution of ARGs in global lakes, using methods such as amplicon and metagenomic shotgun sequencing. , While most studies have concentrated on the water column, sediments, which harbor large microbial populations and act as their reservoir, are critical for understanding ARG dynamics. Lately, several studies have highlighted that resistance risk in sediments is significantly higher compared to the overlying water column. This heightened risk is attributed to the dense microbial communities and the accumulation of contaminants in sediments, which create a conducive environment for ARG proliferation. Notably, such comparisons are typically based on absolute ARG abundances, without normalization to bacterial cell numbers, and may partly reflect the much higher microbial biomass commonly found in sediments. Furthermore, ARGs exhibit the highest incidence of mobility and health risks in surface sediments. Changes in ARGs carried by microbial communities in sediments can indicate anthropogenic impacts such as heavy metal pollution. Typical ARGs detected in sediments include those conferring resistance to multidrug, sulfonamide, macrolide-lincosamide-streptogramin (MLS), tetracycline, and β-lactam. Importantly, many of these ARGs have been shown to be coselected by heavy metals like copper, zinc, and mercury, underscoring the role of metal contamination in shaping sediment resistomes. Despite these advances, there remains a scarcity of studies focusing on host organisms that could enhance the transmission risk of ARGs. Understanding these hosts and their interactions with ARGs is crucial for developing comprehensive and urgent mitigation strategies. Although the metagenomic approach involves analyzing the collective genome of microbial communities, it also provides comprehensive insights into diversity and functions of microorganisms, including those carrying ARGs. However, most studies on ARG hosts including ARPs are based on predictions from reads or assembled contigs. Few studies focus on the isolation and identification of complete individual strains or the reconstruction of complete genomes. This method is crucial for preventing and controlling ARG contamination, as it offers detailed information by sequencing complete genomes, enabling more accurate identification of pathogenic microorganisms.

In this study, we selected two representative lakes, Lake Donghu and Lake Weishan, located in China’s subtropical and temperate regions, respectively. These lakes are representative of freshwater systems under long-term anthropogenic stress, including urbanization and agricultural activities. Both lakes have been exposed to continuous urban and rural pollution for over four decades, driven by rapid city expansion (Lake Donghu) and intensive aquaculture and agriculture (Lake Weishan). Previous studies have highlighted that industrial wastewater, domestic sewage, and agricultural runoff have contributed to severe anthropogenic pollution in these lakes, significantly affecting aquatic microbial communities and resistance genes. However, the response of microbial communities in sediments to environmental stressors, as well as the relationships among ARGs, MRGs, microbial communities, and MGEs, remain understudied, particularly considering the significant role of heavy metals in the dissemination of resistance genes. Although metagenomic sequencing and the construction of metagenome-assembled genomes (MAGs) provide valuable insights into microbial community structure and resistance gene content, this approach is still developing, and detailed characterization and analysis of MAGs remain limited and require further investigation. Yet, a deeper understanding of the interlinkages between microbial dynamics and ARG profiles is important for more accurately assessing the potential impacts of urbanization and industrialization on the distribution of resistance genes in human-impacted lake ecosystems. To address these knowledge gaps, we hypothesized that anthropogenic stressors in anthropogenically impacted lakes, e.g. eutrophication and heavy metal contamination, shape microbial community assembly processes. Specifically, these stressors may promote the persistence, enrichment, and dissemination of ARGs. To test this hypothesis, we performed shotgun metagenomic sequencing to explore the dynamics in diversity and distribution of ARGs and MRGs in lake sediments across different seasons in China. The specific objectives of this study are to (1) investigate the microbial community assembly processes in response to environmental stressors; (2) examine the spatiotemporal distribution patterns of resistance genes (ARGs and MRGs) under varying conditions such as elevated nutrient and heavy metal concentrations; (3) identify potential microbial hosts involved in ARG dissemination and assess the associated resistome risk to human health; and (4) analyze the biogeochemical implications of microbial functions for ecosystem services.

2. Materials and Methods

2.1. Overview of the Study Area, Sampling, and Environmental Analysis

Lake Donghu and Lake Weishan represent two ecologically and socioeconomically important lakes in China, both heavily influenced by human activities and pollution. Lake Donghu, the second largest urban lake in China, is situated in the largest city in central China, and is surrounded by hospitals, residential areas, and scenic spots. Numerous studies show that Lake Donghu is under significant environmental pressures from pollutants such as heavy metals and antibiotics due to urbanization. , Lake Weishan, the largest freshwater lake in northern China, is located along a key section of the Beijing-Hangzhou Grand Canal. In addition to its importance for transportation, the lake serves as a vital reservoir that regulates regional water resources and supports agriculture, fisheries, and other ecosystem functions. However, since the late 20th century, intensive industrial and agricultural development in the catchment area has resulted in continuous inflows of wastewater, leading to eutrophication and heavy metal contamination.

To capture seasonal and spatial heterogeneity, surface sediments (top 10 cm) were collected from 14 stations in both lakes during January and July 2021 (Table S1). Station selection followed established criteria. , Samples were transported on dry ice to the laboratory, homogenized, and divided into aliquots for DNA extraction and physicochemical analyses. A detailed description of the sampling strategy is provided in Supporting Information (Text S1).

Environmental parameters, including temperature, dissolved oxygen (DO), electrical conductivity (EC), pH, dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), and concentrations of Cd, Cr, Cu, Pb, and Zn were measured. These metals were chosen as they are the most representative and ecologically relevant contaminants in freshwater sediments of China. For detailed detection methods, please refer to Text S2. In this study, environmental stress was characterized as a composite of the above environmental parameters, and was incorporated into multivariate analyses to assess its associations with the distribution of resistance genes and microbial community structures.

2.2. DNA Extraction and Metagenomic Analysis

Total DNA was extracted from sediment samples using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The quality and concentration of the extracted DNA were assessed using 1% agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). Metagenomic sequencing was performed at Biomarker Technologies Co., Ltd. (Beijing, China). Paired-end sequencing reads (PE150) were generated on the Illumina HiSeq platform.

For ARG annotation, we employed DeepARG, a deep learning-based tool capable of modeling both short reads and full-length genes. By integrating reference sequences from CARD, ARDB, and UNIPROT, DeepARG provides robust and comprehensive resistome profiling, including the detection of novel ARGs often missed by conventional alignment-based approaches. Detailed data processing steps and parameters are provided in the Supporting Information (Text S3).

2.3. Assessment of the Resistome Risk

The resistome risk was evaluated using the MetaCompare pipeline, which estimates the co-occurrence of ARGs, MGEs, and human pathogens. Briefly, for a given total number of nonredundant contigs (N contigs), we estimated the relative quantities of contigs containing ARG-like sequences(a), contigs containing both ARGs and MGE-like sequences(b), and contigs annotated with pathogen sequences (PATH) along with ARGs and MGEs(c). The resistome risk scores were computed as detailed in Text S4.

2.4. Statistical Analyses

We assessed and quantified the differences, associations, and driving mechanisms between environmental stress and functional genes using the linear discriminant analysis (LDA), principal coordinate analysis (PCoA), Duncan’s test, the neutral community model (NCM), the modified stochasticity ratio (MST), Procrustes analysis, and Spearman’s correlation analysis. Detailed methods are provided in Text S5.

3. Results

3.1. Sediment Physicochemical Parameters

Significant spatiotemporal variations were observed in sediment quality parameters (Figures , S1, Tables S2, S3). Generally, the levels of total phosphorus (TP: 1.19 mg/g vs. 0.63 mg/g) and six heavy metals were significantly higher in Lake Donghu than in Lake Weishan (Figure S1, Table S1, S2), while electrical conductivity (EC: 254.45 μS/cm vs. 443.92 μS/cm) and dissolved organic carbon (DOC: 148.61 mg/kg vs. 252.72 mg/kg) showed the opposite trend (Figures , S1, Table S2). Temporal variations were also observed, with some physicochemical factors showing significant seasonal differences. For example, dissolved oxygen (DO) levels in January were significantly higher than in July, whereas EC, pH, and temperature exhibited the opposite pattern (Figures , S1, Table S3). Overall, Lake Donghu experiences more severe pollution than Lake Weishan, particularly with heavy metals.

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Geographical locations of sampling sites (A) and seasonal variations in environmental factors in Lake Weishan (B) and Lake Donghu (C).

3.2. Distribution Characteristics and Diversity of Microbial Communities

Variations in their community structure and diversity are essential for understanding ARG dynamics. Through taxonomic annotation, we identified 6,215 bacterial, 4,193 viral, 312 archaeal, and 98 eukaryotic taxa across 14 sediment samples collected from the two anthropogenically impacted lakes during different seasons (Figures A, S2). A significant proportion of taxa (71.4%) were shared across lakes and seasons, with the number of unique species in each lake and season ranging from 332 to 516 (Figure A). Proteobacteria were the dominant phylum in Lake Donghu and Lake Weishan, with average relative abundances of 60.2% and 66.6%, respectively. Additionally, a high proportion of Actinobacteria (21.0% and 17.6%), Planctomycetes (3.2% and 2.7%), Firmicutes (3.0% and 2.5%), and Bacteroidetes (2.7% and 2.5%) were observed in Lake Donghu and Lake Weishan, respectively (Figure B). LEfSe analysis revealed 15 significantly enriched phyla in Lake Donghu, compared to only one in Lake Weishan (LDA score >2). In addition, Verrucomicrobia was identified as a biomarker differentiating seasonal variations, with higher enrichment observed in July (Figure S3).

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Microbial community structures and microbial diversity differences in the sediment of Lakes Donghu (DS) and Weishan (WS). (A) Venn diagram showing the number of shared and unique microorganisms among different lakes and seasons. (B) Bar chart representing the relative abundance of the top 15 phyla in the sediment microbial community. (C) Boxplot illustrating the alpha diversity indices of sediment microbial communities between lakes and seasons, with different letters indicating significant differences (Duncan test, p < 0.05). (D–F) Principal Coordinates Analysis (PCoA) plot based on Bray–Curtis distances, depicting microbial community patterns and differences between lakes and seasons.

Overall, differences in sediment microbial community structures and diversity between the two lakes were more distinct than the seasonal variations observed within each lake. As measured by the Shannon index, alpha diversity exhibited significant variation between the lakes (Figure C). Beta diversity, analyzed using PCoA based on Bray–Curtis distances, showed a clear separation between groups, particularly between the lakes (Figure D-F, p < 0.001).

3.3. Assembly Process of Microbial Communities in Sediment

To elucidate the mechanisms driving community diversity, we employed the neutral community model (NCM) and the modified stochasticity ratio (MST). First, the NCM was used to assess the relationship between taxa occurrence frequency and variations in relative abundance (Figure A-B). This model explained 92.1% and 89.7% of the community variance in Lakes Donghu and Weishan, respectively. The high explanatory power (R 2) and Nm values suggest that stochastic processes are the primary drivers of microbial community assembly in sediments. We further quantified the relative contributions of deterministic and stochastic processes by calculating the MST. Stochastic processes accounted for an average of 63.7% and 50.9% of microbial community assembly in Lakes Donghu and Weishan, respectively (Figure C). These results underscore the significant role of stochastic processes in shaping microbial communities in sediments. In particular, the higher microbial diversity observed in Lake Donghu may reflect its greater environmental heterogeneity and stronger stochastic assembly processes, as indicated by the NCM and MST results (Figures C, ).

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Assembly patterns of surface sediment communities. Fit of the Neutral Community Model (NCM) for community assembly in Lakes Donghu (A) and Weishan (B). The solid blue lines represent the best fit to the NCM, and the dashed blue lines indicate the 95% confidence intervals for the NCM. R 2 indicates the degree of fit to this model; Nm, the product of community size (N) and migration rate (m), represents the metacommunity size times immigration. (C) Modified stochasticity ratio (MST) of sediment community assembly.

3.4. High-Throughput Profiling of ARGs, MRGs, and MGEs

Across all samples, 52,817 ARG-like ORFs were identified and categorized into 355 ARG subtypes and 28 ARG types (Figure A). The three most prevalent ARG types were multidrug, macrolide-lincosamide-streptogramin (MLS), and tetracycline, collectively accounting for 72.7% of all detected ARGs, with multidrug resistance genes particularly dominant at around 50% (Figure B). Resistance genes for antibacterial free fatty acids (AFFA), beta-lactam, tetracycline, sulfonamide, and triclosan showed significant differences in abundance between the lakes (Figure C, Table S4). Triclosan resistance genes exhibited substantial seasonal variation in Lake Weishan (Figure C; Table S4). The ten most abundant ARGs were macB, multidrug_ABC_transporter, rpoB2, mexF, bcrA, acrB, ompR, bacA, tetA, and mupA (Figure S4). Interestingly, the differential ARG distribution patterns correlate with key environmental pressures such as heavy metal and nutrient concentrations. For instance, Lake Donghu with the highest levels of heavy metals and microbial diversity exhibited significantly higher levels of ARGs compared to Lake Weishan (Figures C, C, Table S2).

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Distribution characteristics and differences of ARG types, MGEs, and MRGs between lakes and seasons. (A) Number of shared and unique ARGs between lakes and seasons. (B) The distribution of the top 10 abundant ARG types. (C) Significant differences in ARG types (AFFA, antibacterial_free_fatty_acids) between lakes and seasons, with different letters indicating significant differences (Duncan test, p < 0.05). (D–F) Principal coordinates analysis (PCoA) of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and metal resistance genes (MRGs) based on Bray–Curtis distances. Significance of the differential groups was statistically tested using Adonis.

Altogether, 167 MRGs were detected in the sediments of both lakes, including 87 heavy metal resistance genes (HMGs), which contributed 75.7% of the total MRG abundance. Copper, arsenic, and zinc resistance genes were the most abundant, contributing 16.2%, 11.4%, and 8.5% of the total number of HMGs, respectively (Figure S5A). The Duncan test revealed that 26 HMGs exhibited differential abundances between both lakes, while only 6 HMGs showed significant seasonal differences (Figure S5B, Table S5). Seven heavy metals were detected in the sediment of both lakes, with concentrations ranging from 0.5 to 31,763.2 μg/L in each sample (Table S1).

A total of 1,158 MGE genes were identified and classified into five functional groups. The most abundant MGEs (354) were associated with phage-related biological processes (Table S6). Genes involved in replication, recombination, or repair were the most abundant, representing 43.5% of the relative abundance, followed by integration/excision-related genes (21.0%) and phages (16.9%). The five most abundant MGE genes included tnpA, uvrA, oppF, pilR, and dnaE (Table S6). Furthermore, class 1 integron (intl1), which is crucial for the horizontal gene transfer of ARGs, was detected in all analyzed samples, with abundances ranging from 15.4 to 29.1. The gene-level profiles of ARGs, MGEs, and MRGs were separated along PCoA axes 1 and/or 2, as confirmed by Permutational Multivariate Analysis of Variance (PERMANOVA, Adonis test) (Figure D-F). Differences in the distribution of ARG, MGE, and MRG profiles were more pronounced between lakes than between seasons (Figure S6).

3.5. Factors Driving Distribution and Proliferation of Resistance Genes

We employed the neutral community model (NCM) to assess the role of neutral processes in the distribution of ARG and MRG profiles. Neither ARG (R 2 = −2.7, Nm = 3,183) nor MRG (R 2 = −15.0, Nm = 13,677) profiles fit the NCM, as indicated by the negative R 2 values. This finding suggests that neutral processes played an insignificant role in shaping the spatial distribution of these profiles (Figure A and B). Procrustes analysis further revealed a strong correlation between environmental factors and ARG (M 2 = 0.67, p < 0.01) and MRG profiles (M 2 = 0.59, p < 0.01) (Figure C and D). Additionally, horizontal gene transfer via intl1 has been recognized as a key mechanism in the spread of resistance genes. Procrustes analysis revealed significant correlations between intl1 and ARG (M 2 = 0.08, p < 0.01) and MRG profiles (M 2 = 0.21, p < 0.01) (Figure E and F). A strong correlation was also observed between environmental factors and intl1 abundance (M 2 = 0.43, p < 0.01; Figure S7). Spearman’s correlation analysis confirmed that environmental factors significantly affected the abundance of 20 ARG types. Among these, seven ARG types exhibited significant positive correlations with heavy metals (e.g., Cd, Cr, Cu, Fe, and Pb). Furthermore, intl1 abundance showed significant positive correlations with resistance genes for aminoglycosides, beta-lactams, bicyclomycins, and triclosan (Figure S8).

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Distribution and dissemination mechanisms of resistance genes and resistome risk score. The fit of the neutral community model for (A) ARG and (B) MRG profiles. The solid blue lines represent the best fit to the NCM, and the dashed blue lines indicate the 95% confidence intervals for the NCM. R 2 indicates the degree of fit to this model. Nm, the product of community size (N) and migration rate (m), indicates metacommunity size times immigration. Procrustes analysis shows the correlation between environmental factors and (C) ARGs and (D) MRGs profiles, and the correlation between (E) MGEs and ARGs and (F) MRGs profiles (Env, environmental factors). (G) Spatial distribution of the resistome risk score. (H) Highly significant correlation between the resistome risk score and ARG abundance.

3.6. Resistome Risk in Lake Sediments

Based on the conceptual framework proposed by Martínez et al. (2015), we analyzed the co-occurrence of ARGs, MGEs, and human bacterial pathogens (HBPs) to evaluate the potential ARG dissemination risk across different seasons in the sediments of Lakes Donghu and Weishan. The average resistance risk score in the sediments of both lakes was 22.6, with no significant differences observed between lakes or seasons (Figure G, Wilcoxon test, p > 0.05). Spearman’s correlation analysis showed a significant positive relationship between resistome risk scores and ARG abundance (Figure H). As expected, environmental stress, primarily driven by heavy metal contamination, promoted the spread of ARGs (Figure C–F), thereby further elevating resistance risks in lake sediments.

3.7. Potential High-Risk ARG Hosts in Lake Sediments

De novo assembly and binning of metagenomic data from surface sediments of both lakes led to reconstruct 6 archaeal and 149 bacterial MAGs with high completeness (>90%) and low contamination (<5%). Among them, 79 MAGs were obtained from Lake Donghu and 76 from Lake Weishan. Each MAG carried 1–16 ARGs, 2–57 MRGs, and 11–121 VFGs (Figure , Table S7). Among all 155 MAGs, 125 were found to cocarry ARGs, VFGs, and MGEs, with 37 containing intl1, thereby identifying them as high-risk ARG hosts. These included 3 archaeal and 122 bacterial MAGs, belonging to 34 classes and 17 phyla (Figure , Table S7). Collectively, these MAGs harbored 72 types of ARGs, categorized into 22 ARG subtypes. Multidrug resistance genes were the most prevalent, identified in 32 and 20 hosts from Lakes Donghu and Weishan, respectively. Proteobacteria and Desulfobacterota were identified as the dominant high-risk ARG hosts, together accounting for 64.8% of all MAGs and carrying, on average, 72.1% of the total ARGs. Notably, these two phyla also constituted the major carriers of MRGs, with 70.7% of their MAGs containing MRGs (Figure , Table S7).

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Evolutionary relationship diagram of all potential hosts carrying antibiotic resistance genes (ARGs). The figure illustrates the evolutionary relationships among bacteria and archaea that carry ARGs, metal resistance genes (MRGs), virulence factor genes (VFGs), and mobile genetic elements (MGEs). The evolutionary branches are color-coded to represent different phyla, while the height of the bars indicates the abundance of functional genes or elements present. The outermost circle signifies the presence or absence of the class 1 integron (intl1) gene.

3.8. Metabolic Characteristics of Potential High-Risk ARG Hosts

To further explore the metabolic characteristics of potential hosts involved in ARG dissemination in the environment, we applied the METABOLIC pipeline. A total of 124 MAGs were engaged in 11 environmentally relevant metabolic processes (Figure ). Among these, MAGs associated with iron redox cycling were the most prevalent, with an average of 6.81 gene copies per MAG across both lakes, likely reflecting the elevated iron concentrations in the sediments. In addition, 31 and 25 MAGs from Lakes Donghu and Weishan, respectively, were involved in arsenic oxidation and reduction. Similarly, manganese oxidation was detected in 6 and 11 MAGs from Lakes Donghu and Weishan, respectively (Figure ).

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Metabolic analysis results of MAGs with potential for resistance gene dissemination. (A) Potential pathways and (B) number of MAGs with ARG dissemination potential, greenhouse gas emissions and participation in metal element cycling in Lakes Donghu (DH) and Weishan (WS). (C) Copy numbers of functional genes related to ARG dissemination potential, greenhouse gas emissions, and participation in metal element cycling in DH and WS. (D) Taxonomic annotation results and counts of MAGs with ARG dissemination potential, greenhouse gas emissions, and participation in metal element cycling. The metabolic profiling of MAGs with dissemination potential in DS and WS environments encompasses formate oxidation (1, 2), aerobic carbon monoxide oxidation (3), urease (4), arsenate reduction (5), arsenite oxidation (6) and methylation (7), iron oxidation (8) and reduction (9), manganese oxidation (10), and nitric oxide reduction (11), along with the associated microorganisms (phylum).

More importantly, 71 and 68 MAGs in Lakes Donghu and Weishan, respectively, participated in CO2-releasing processes, while respectively 14 and 11 MAGs in Lake Donghu and Weishan contributed to the production of the highly potent greenhouse gas N2O. Interestingly, gene copy numbers related to all 11 environmental metabolic pathways were generally higher in Lake Weishan than in Lake Donghu. In particular, the copy number of genes associated with CO2 emissions was 2.38 per MAG in Lake Weishan, ca. 1.7 times higher than that in Lake Donghu (1.38 per MAG) (Figure ), consistent with the observed DOC concentrations in the two lakes (Table S1). Correlation analysis further revealed a significant positive relationship between DOC concentrations and the gene copy numbers involved in greenhouse gas emission processes, especially those related to formate oxidation, a key pathway for microbial CO2 production (Figure S9). Taxonomic analysis showed that Desulfobacterota and Proteobacteria, particularly members of Gammaproteobacteria, were the main contributors to greenhouse gas release. Desulfobacterota facilitated the release of CO2 and N2O through multiple metabolic pathways such as formate oxidation, aerobic CO oxidation, and nitric oxide reduction. Additionally, Proteobacteria contributed to CO2 release via urease-mediated metabolism (Figure , Table S8).

4. Discussion

4.1. Stochastic Processes Dominate Sediment Microbial Community Assembly

Rapid urbanization is widely recognized as a source of environmental stress on aquatic ecosystems, a pattern also reflected in the lake sediments examined in this study. Significant variations in heavy metal concentrations were observed, particularly in Lake Donghu, where zinc exhibited the highest average level among all analyzed metals (Table S1). These findings are consistent with previous studies identifying Pb, Cd, Cu, and Zn as major anthropogenically derived pollutants. Liu et al. (2014) further reported considerable ecological risks associated with heavy metals in surface sediments of Lake Donghu. In addition to metal contamination, other environmental parameters, including DO, EC, pH, and temperature, exhibited notable seasonal fluctuations, similar to the patterns previously documented in river sediments.

Proteobacteria were the dominant microbial taxa in the sediments of both lakes, a pattern commonly observed in diverse freshwater ecosystems. In this study, microbial diversity differed more significantly between the two anthropogenically impacted lakes than within each lake across the two sampled seasons. While this suggests a spatially structured pattern, we caution against overgeneralization, as our analysis is based on only two seasonal time points. Higher temporal resolution sampling may reveal stronger seasonal dynamics, as observed in Lake Taihu by Zhu et al. (2019). Indeed, similar temporal variability may also exist in our lakes but remain undetected due to limited sampling frequency. Nevertheless, spatial variation has also been widely reported in other aquatic environments, including rivers and mangrove swamps.

Despite the high levels of heavy metals detected in both lakes, our analysis using the neutral community model and the modified stochasticity ratio revealed that stochastic processes, rather than deterministic environmental factors, predominantly governed microbial community assembly (Figure ). This pattern may be attributed to the relatively stable environmental, e.g., physicochemical conditions in lake sediments, where microbial communities may have developed resilience and adaptive capacities that buffer them against environmental fluctuations. Numerous studies have corroborated the role of stochastic processes in microbial community assembly. Similarly, Hou et al. (2021) highlighted the key role of stochasticity in driving microbial community dynamics across both sedimentary and soil ecosystems, underscoring the broad relevance of this mechanism.

4.2. Environmental Stress as the Primary Factor of Resistome Dissemination

The spread of ARGs poses a critical threat to global public health and modern medicine. We identified 28 distinct ARG types across both lakes, with macB, multidrug_ABC_transporter, and rpoB2 being the most prevalent. These ARGs exhibited significant spatiotemporal variation, which is consistent with patterns reported in other aquatic systems such as deep subtropical lakes, urban river sediments, and aquaculture environments. , ARG abundance was significantly higher in Lake Donghu, correlating with elevated heavy metal concentrations and greater microbial diversity compared to Lake Weishan (Figures C, B–C; Table S2). These findings support our hypothesis that anthropogenic activities, particularly those impacting Lake Donghu, pose increased environmental stress and lead to elevated ARG levels.

Heavy metal pollution imposes long-term selective pressures on microbial communities, promoting the evolution of resistance mechanisms that drive both selection and coselection of MRGs and ARGs. ,, Consequently, this mechanism likely contributes to the observed increase in ARG diversity and abundance, thereby elevating the risk of resistance dissemination. Our results, supported by the neutral model, Procrustes analysis, and correlation analysis, highlight the critical role of heavy metals in ARG dissemination (Figure ; Figure S8). This may reflect coregulation of genetic pathways conferring resistance to both heavy metals and antibiotics. Heavy metal exposure may also alter bacterial uptake and efflux mechanisms, further promoting antibiotic resistance. In addition, heavy metals may interact with dissolved organic matter, facilitating horizontal gene transfer and accelerating ARG transmission. In addition to heavy metals, antibiotic residues are recognized as potential drivers of ARG proliferation. However, microcosm experiments conducted in Lake Donghu indicated no statistically significant correlation (p > 0.05) between ambient antibiotic concentrations and ARG profiles, suggesting that antibiotics exert a relatively limited influence compared with heavy metal stress in this system.

Moreover, recent evidence highlights the broader role of urbanization-induced pollution in reshaping microbial ecology, particularly through impacts on plasmid diversity and function. A study by Barrantes-Jiménez et al. (2025) demonstrated that pollution gradients associated with urban development can increase plasmid diversity and ARG carriage in aquatic systems, independent of chromosomal patterns. This suggests that, beyond heavy metal stress, urbanization may promote resistome proliferation by enhancing the abundance and mobility of plasmid-borne resistance elements. Given the urbanized setting of Lake Donghu, these findings may partially explain the elevated ARG diversity and abundance observed in this system.

Seasonal factors may also contribute to the spatiotemporal dynamics observed in our study. Temperature differences between winter and summer can alter microbial growth, enzymatic activity, and horizontal gene transfer frequency, thereby influencing ARG diversity. Nutrient availability, particularly nitrogen and phosphorus, also fluctuates seasonally, reshaping community composition and functional traits. These natural drivers likely act in concert with anthropogenic stressors, jointly regulating microbial diversity and resistome dissemination in lake sediments.

4.3. Ecological Risk Assessment

4.3.1. Risk Assessment of ARGs

The pervasive occurrence of ARGs in natural environments is well-documented, however, their ecological and public health risks remain unevenly distributed. It is important to note that not all ARGs pose equal threats to human health, as their potential to transfer horizontally from environmental reservoirs to human pathogens varies considerably. In this study, resistance risk in the lake sediments remained relatively stable across both lakes and seasons. In sediments, this stability may be due to the persistence of resistance genes and the enduring presence of MGEs, which facilitate horizontal gene transfer and maintain a consistently high-risk profile across space and time. Moreover, complex interactions between ARGs, MGEs, and gene transfer mechanisms play a critical role in shaping the resistome risk dynamics.

Spearman correlation analysis revealed a significant positive association between ARG abundance and resistance risk, indicating that higher ARG loads are closely linked to elevated health and ecological risks. Among the 28 detected ARG subtypes, only three displayed a significant spatiotemporal variation, potentially explaining the overall consistency in risk levels (Figure G, H). This finding suggests that, while ARG composition may fluctuate, the underlying mechanisms driving resistance risk are largely steady. Notably, resistance risk in these sediments was higher than that previously observed in wastewater treatment plants , and eutrophic lake waters. This is consistent with previous findings that sediments often act as long-term reservoirs of ARGs due to their higher microbial density, pollutant accumulation, and enhanced potential for horizontal gene transfer. ,

To gain deeper insights into the dissemination potential and pathogenicity of ARGs, we applied metagenomic binning and MAG-based functional annotation. A substantial number of medium- to high-quality MAGs from both lakes were found to harbor ARGs, often in conjunction with MGEs and VFGs, suggesting strong potential for resistance dissemination among environmental bacteria. The detection of mobile ARGs highlights the substantial risk of gene exchange between environmental microbes and clinical pathogens. Particularly, intl1 was detected in numerous MAGs, reinforcing its known role in promoting metal-induced antibiotic resistance. The frequent and consistent co-occurrence of metal resistance genes in ARG-carrying MAGs further supports the hypothesis that metal pollution facilitates ARG persistence and horizontal dissemination. These findings align with previous observations in poultry farm environments, drinking water biofilms, and activated sludge systems. Furthermore, Proteobacteria and Desulfobacterota were identified as the dominant ARG hosts in both lakes, consistent with earlier reports from marine and mariculture environments.

4.3.2. Role of ARG Dissemination Hosts in Metal Cycling and the Greenhouse Effect

Recent studies have increasingly emphasized the importance of microbial cohosts that simultaneously harbor ARGs and elemental cycling genes, particularly in the context of environmental change and ecological risk. However, environmental and biochemical functions of these cohosts and their relationship to ARG acquisition within lake ecosystems remain insufficiently understood, partly due to the complexity of microbial processes and the limited resolution of traditional detection methods. Our metagenomic results indicate that ARG dissemination hosts significantly contribute to the biogeochemical cycling of metal elements (e.g., Fe, As, Mn) and the emission of greenhouse gases such as CO2 and N2O. In particular, Lake Weishan, an integral component of the eastern route of the South-to-North Water Diversion Project, has undergone extensive construction and operation, requiring substantial energy and material inputs. These anthropogenic modifications have been associated with elevated greenhouse gas emissions, potentially due to the higher greenhouse gas emission potential observed in ARG-carrying microbial communities in Lake Weishan. In addition to engineering-related disturbances, Lake Weishan is characterized by more extensive agricultural activity in its watershed. However, this is not directly reflected in sediment heavy metal concentrations, as total phosphorus (TP) and six key heavy metals were all significantly higher in Lake Donghu than in Lake Weishan (Figure S1, Table S1–S2). These elevated levels likely stem from historical urbanization and fish farming practices in Lake Donghu. In contrast, Lake Weishan exhibited higher levels of dissolved organic carbon (DOC) and electrical conductivity (EC), suggesting stronger inputs from agricultural runoff and greater accumulation of organic matter. Given that agricultural activities are closely linked to greenhouse gas emissions throughout the entire life cycle of agricultural products. In Lake Weishan, we observed an enrichment of genes related to carbon cycling and greenhouse gas production specifically in ARG-carrying microbial communities. This suggests that agricultural inputs not only influence resistance gene dynamics but also promote microbial functions associated with greenhouse gas emissions. Moreover, sedimentary DOC, a major component of dissolved organic matter (DOM), plays a critical role in regulating CO2 emissions. Our correlation analysis (Figure S9) further supports this mechanism, showing that DOC variation is linked to both microbial carbon cycling activity and greenhouse gas fluxes, providing a deeper understanding of how agricultural activities can influence both resistance and environmental gas exchange through microbial processes.

By reconstructing MAGs, we obtained high-resolution, genome-level insights into the composition, resistance potential, and metabolic profiles of microbial communities. This enhanced resolution allowed for a more accurate characterization of the environmental functions and ecological risks posed by potential ARG hosts in lake sediments. Compared to conventional community-level analyses, genome-level functional annotation offers a clearer and improved understanding of microbial contributions to both resistance dissemination and environmental impacts. , Notably, the co-occurrence of MGEs, VFGs, and greenhouse gas-related functional genes within the identified ARG hosts highlights the presence of a complex and interconnected gene transfer network. This network facilitates the spread of antibiotic resistance and potentially enhances microbial contributions to greenhouse gas emissions and metal cycling. In summary, our findings reveal multifaceted ecological roles of ARG dissemination hosts, particularly due to linking microbial resistance with biogeochemical processes and climate-related impacts. By integrating functional annotation at the genome level, we provide a foundation for developing targeted, data-driven strategies to curb resistance dissemination and provide the basis for mitigating its broader environmental consequences.

Despite these novel insights, this study has several limitations. Sampling was limited to two lakes and two seasons, which may constrain the generalizability of the findings. Although heavy metals and DOC were identified as key correlates of resistance dissemination, causal links remain unresolved. Future research with wider spatiotemporal coverage and controlled microcosm experiments is needed to clarify mechanisms and strengthen risk assessment in human-impacted aquatic ecosystems.

5. Conclusions

This study reveals distinct biogeographical patterns and ecological risks of microbial communities, ARGs, MRGs, and MGEs in lake sediments of two anthropogenically impacted lakes under varying anthropogenic influences. Microbial communities were predominantly shaped by stochastic processes, whereas ARGs and MRGs were strongly influenced by deterministic environmental factors, particularly heavy metal concentrations, highlighting their role in promoting resistance dissemination. Metagenomic analyses identified high-risk ARG hosts, mainly Proteobacteria and Desulfobacterota, that coharbored VFGs, MGEs, and MRGs, which were also involved in elemental cycling and greenhouse gas emissions. DOC was identified as a key regulatory factor of microbial metabolic profiles and antibiotic resistance potential. The observed coupling between microbial antibiotics resistance, elemental cycling, and greenhouse gas flux underscores the potentially broader ecological risks associated with ARG proliferation. Overall, these findings provide new insights into the environmental functions and risks of ARG hosts and support the need for integrated strategies, such as pollution control and ecological restoration, to mitigate antibiotic resistance spread and resulting environmental implications.

Supplementary Material

eh5c00278_si_001.pdf (1.2MB, pdf)
eh5c00278_si_002.xlsx (41.6KB, xlsx)

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Nos. 32030015 and 32470545) and the Ongoing Research Funding program (ORF-2026-7), King Saud University, Riyadh, Saudi Arabia. E.G.X. acknowledges the support of the Department of Biology, University of Southern Denmark, and Danmarks Frie Forskningsfond (No. 0165-00056B).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00278.

  • Experimental details and methods and annotation results of species and functional genes (ARGs, MRGs, MGEs) across spatiotemporal contexts (figures) (PDF)

  • Differential analyses and correlations with environmental factors (tables) (XLSX)

Chengzhi Mao: Methodology; data curation; validation; writingoriginal draft; visualization. Yunfeng Wang: Investigation; visualization; data curation; writingreview and editing. Xinghao Li: Writingreview and editing. Qiaoyi Kong: Writingreview and editing. Saleh A. Al-Farraj: Writingreview and editing. Elvis Genbo Xu: Writingreview and editing. Hans-Peter Grossart: Writingreview and editing. Jie Huang: Conceptualization; project administration; supervision; writingreview and editing. Weibo Song: Writingreview and editing; visualization; funding acquisition; supervision.

#.

These authors contributed equally to this work.

The authors declare no competing financial interest.

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eh5c00278_si_001.pdf (1.2MB, pdf)
eh5c00278_si_002.xlsx (41.6KB, xlsx)

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